Grid computing enlarge with computing platform which is collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organization to form a distributed high performance computing infrastructure. Grid computing solves the complex computing
problems amongst multiple machines. Grid computing solves the large scale computational demands in a high performance computing environment. The main emphasis in the grid computing is given to the resource management and the job scheduler .The goal of the job scheduler is to maximize the resource utilization and minimize the processing time of the jobs. Existing approaches of Grid scheduling doesn’t give much emphasis on the performance of a Grid scheduler in processing time parameter. Schedulers allocate resources to the jobs to be executed using the First come First serve algorithm. In this paper, we have provided an optimize algorithm to queue of the scheduler using various scheduling methods like Shortest Job First, First in First out, Round robin. The job scheduling system is responsible to select best suitable machines in a grid for user jobs. The management and scheduling system generates job schedules for each machine in the grid by taking static restrictions and dynamic parameters of jobs and machines
into consideration. The main purpose of this paper is to develop an efficient job scheduling algorithm to maximize the resource utilization and minimize processing time of the jobs. Queues can be optimized by using various scheduling algorithms depending upon the performance criteria to be improved e.g. response
time, throughput. The work has been done in MATLAB using the parallel computing toolbox.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
This document describes an enhanced adaptive scoring job scheduling algorithm with replication strategy for grid environments. The algorithm aims to improve upon an existing adaptive scoring job scheduling algorithm by identifying whether jobs are data-intensive or computation-intensive. It then divides large jobs into subtasks, replicates the subtasks, and allocates the replicas to clusters based on a computed cluster score in order to improve resource utilization and job completion times. The algorithm is evaluated through simulation using the GridSim toolkit.
Service Request Scheduling in Cloud Computing using Meta-Heuristic Technique:...IRJET Journal
This document discusses using the Teaching Learning Based Optimization (TLBO) meta-heuristic technique for service request scheduling between users and cloud service providers. TLBO is a nature-inspired algorithm that mimics the teacher-student learning process. It is compared to other meta-heuristic algorithms like Genetic Algorithm. The key steps of TLBO involve initializing a population, evaluating fitness, selecting the best solution as teacher, and updating the population through teacher and learner phases until termination criteria is met. The document proposes using number of users and virtual machines as parameters for TLBO scheduling in cloud computing. MATLAB simulation results show the initial and final iterations converging to an optimal scheduling solution.
A novel scheduling algorithm for cloud computing environmentSouvik Pal
The document describes a proposed genetic algorithm-based scheduling approach for cloud computing environments. It aims to minimize waiting time and queue length. The algorithm first permutes task burst times and finds minimum waiting times using FCFS and genetic algorithms. It then applies a queuing model to the sequences with minimum waiting time from each approach. Experimental results on 4 sample tasks show the genetic algorithm reduces waiting time compared to FCFS. The genetic operators of selection, crossover and mutation are applied to evolve optimal task scheduling sequences.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Cost-Efficient Task Scheduling with Ant Colony Algorithm for Executing Large ...Editor IJCATR
This document summarizes a research paper that proposes an optimized ant colony optimization (ACO) algorithm for task scheduling in cloud computing. The goal is to minimize makespan and cost while improving fairness and load balancing. The ACO algorithm is adapted to prioritize and fairly allocate tasks to machines based on their performance. Simulations show the proposed ACO algorithm reduces makespan by 80% compared to Berger and greedy algorithms. It also increases processor utilization and balances loads across machines better than the other algorithms. The researchers conclude the optimized ACO approach improves resource usage and user satisfaction for task scheduling in cloud computing.
Job Resource Ratio Based Priority Driven Scheduling in Cloud Computingijsrd.com
Cloud Computing is an emerging technology in the area of parallel and distributed computing. Clouds consist of a collection of virtualized resources, which include both computational and storage facilities that can be provisioned on demand, depending on the users' needs. Job scheduling is one of the major activities performed in all the computing environments. Cloud computing is one the upcoming latest technology which is developing drastically. To efficiently increase the working of cloud computing environments, job scheduling is one the tasks performed in order to gain maximum profit. In this paper we proposed a new scheduling algorithm based on priority and that priority is based on ratio of job and resource. To calculate priority of job we use analytical hierarchy process. In this paper we also compare result with other algorithm like First come first serve and round robin algorithms.
This document presents a scheduling strategy that performs dynamic job grouping at runtime to optimize the execution of applications with many fine-grained tasks on global grids. The strategy groups individual jobs into larger "job groups" based on the processing requirements of each job, the capabilities of available grid resources, and a defined granularity size. It aims to minimize overall job execution time and cost while maximizing resource utilization. The strategy is evaluated through simulations using the GridSim toolkit, which models grid resources and application scheduling.
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...IRJET Journal
This document proposes a scheduling algorithm for allocating resources in cloud computing based on the Project Evaluation and Review Technique (PERT). It aims to address issues like starvation of lower priority tasks. The algorithm models task allocation as a directed acyclic graph and uses PERT to schedule critical and non-critical tasks, prioritizing higher priority tasks. The algorithm is evaluated against other scheduling methods and shows improvements in reducing completion time and optimizing resource allocation for all tasks.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
This document describes an enhanced adaptive scoring job scheduling algorithm with replication strategy for grid environments. The algorithm aims to improve upon an existing adaptive scoring job scheduling algorithm by identifying whether jobs are data-intensive or computation-intensive. It then divides large jobs into subtasks, replicates the subtasks, and allocates the replicas to clusters based on a computed cluster score in order to improve resource utilization and job completion times. The algorithm is evaluated through simulation using the GridSim toolkit.
Service Request Scheduling in Cloud Computing using Meta-Heuristic Technique:...IRJET Journal
This document discusses using the Teaching Learning Based Optimization (TLBO) meta-heuristic technique for service request scheduling between users and cloud service providers. TLBO is a nature-inspired algorithm that mimics the teacher-student learning process. It is compared to other meta-heuristic algorithms like Genetic Algorithm. The key steps of TLBO involve initializing a population, evaluating fitness, selecting the best solution as teacher, and updating the population through teacher and learner phases until termination criteria is met. The document proposes using number of users and virtual machines as parameters for TLBO scheduling in cloud computing. MATLAB simulation results show the initial and final iterations converging to an optimal scheduling solution.
A novel scheduling algorithm for cloud computing environmentSouvik Pal
The document describes a proposed genetic algorithm-based scheduling approach for cloud computing environments. It aims to minimize waiting time and queue length. The algorithm first permutes task burst times and finds minimum waiting times using FCFS and genetic algorithms. It then applies a queuing model to the sequences with minimum waiting time from each approach. Experimental results on 4 sample tasks show the genetic algorithm reduces waiting time compared to FCFS. The genetic operators of selection, crossover and mutation are applied to evolve optimal task scheduling sequences.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Cost-Efficient Task Scheduling with Ant Colony Algorithm for Executing Large ...Editor IJCATR
This document summarizes a research paper that proposes an optimized ant colony optimization (ACO) algorithm for task scheduling in cloud computing. The goal is to minimize makespan and cost while improving fairness and load balancing. The ACO algorithm is adapted to prioritize and fairly allocate tasks to machines based on their performance. Simulations show the proposed ACO algorithm reduces makespan by 80% compared to Berger and greedy algorithms. It also increases processor utilization and balances loads across machines better than the other algorithms. The researchers conclude the optimized ACO approach improves resource usage and user satisfaction for task scheduling in cloud computing.
Job Resource Ratio Based Priority Driven Scheduling in Cloud Computingijsrd.com
Cloud Computing is an emerging technology in the area of parallel and distributed computing. Clouds consist of a collection of virtualized resources, which include both computational and storage facilities that can be provisioned on demand, depending on the users' needs. Job scheduling is one of the major activities performed in all the computing environments. Cloud computing is one the upcoming latest technology which is developing drastically. To efficiently increase the working of cloud computing environments, job scheduling is one the tasks performed in order to gain maximum profit. In this paper we proposed a new scheduling algorithm based on priority and that priority is based on ratio of job and resource. To calculate priority of job we use analytical hierarchy process. In this paper we also compare result with other algorithm like First come first serve and round robin algorithms.
This document presents a scheduling strategy that performs dynamic job grouping at runtime to optimize the execution of applications with many fine-grained tasks on global grids. The strategy groups individual jobs into larger "job groups" based on the processing requirements of each job, the capabilities of available grid resources, and a defined granularity size. It aims to minimize overall job execution time and cost while maximizing resource utilization. The strategy is evaluated through simulations using the GridSim toolkit, which models grid resources and application scheduling.
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...IRJET Journal
This document proposes a scheduling algorithm for allocating resources in cloud computing based on the Project Evaluation and Review Technique (PERT). It aims to address issues like starvation of lower priority tasks. The algorithm models task allocation as a directed acyclic graph and uses PERT to schedule critical and non-critical tasks, prioritizing higher priority tasks. The algorithm is evaluated against other scheduling methods and shows improvements in reducing completion time and optimizing resource allocation for all tasks.
This document proposes a genetic algorithm called Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA) to optimize the cost of executing workflows in the public cloud. It discusses how genetic algorithms can be applied to the workflow scheduling problem to generate optimal schedules. The WSGA represents potential scheduling solutions as chromosomes, uses a fitness function to evaluate scheduling costs, and applies genetic operators like selection, crossover and mutation to evolve new schedules over multiple iterations. The goal is to minimize total execution cost while meeting workflow dependencies and deadline constraints. An experimental setup is described and the WSGA approach is claimed to reduce costs more than other heuristic scheduling algorithms for communication-intensive workflows.
Effective and Efficient Job Scheduling in Grid ComputingAditya Kokadwar
The integration of remote and diverse resources and the increasing computational needs of Grand Challenges problems combined with the faster growth of the internet and communication technologies leads to the development of global computational grids. Grid computing is a prevailing technology, which unites underutilized resources in order to support sharing of resources and services distributed across numerous administrative region. An efficient and effective scheduling system is essentially required in order to achieve the promising capacity of grids. The main goal of scheduling is to maximize the resource utilization and minimize processing time and cost of the jobs. In this research, the objective is to prioritize the jobs based on execution cost and then allocate the resources with minimum cost by merging it with conventional job grouping strategy to provide the solution for better and more efficient job scheduling which is beneficial to both user and resource broker. The proposed scheduling approach in grid computing employs a dynamic cost-based job scheduling algorithm for making an efficient mapping of a job to available resources in the grid. It also improves communication to computation ratio (CCR) and utilization of available resources by grouping the user jobs before resource allocation.
OPTIMIZED RESOURCE PROVISIONING METHOD FOR COMPUTATIONAL GRID ijgca
Grid computing is an accumulation of heterogeneous, dynamic resources from multiple administrative areas which are geographically distributed that can be utilized to reach a mutual end. Development of resource provisioning-based scheduling in large-scale distributed environments like grid computing brings in new requirement challenges that are not being believed in traditional distributed computing environments. Computational grid is applying the resources of many systems in a network to a single problem at the same time. Grid scheduling is the method by which work specified by some means is assigned to the resources that complete the work in the environment which cannot fulfill the user requirements considerably. The satisfaction of users while providing the resources might increase the beneficiary level of resource suppliers. Resource scheduling has to satisfy the multiple constraints specified by the user. The option of resource with the satisfaction of multiple constraints is the most tedious process. This trouble is solved by bringing out the particle swarm optimization based heuristic scheduling algorithm which attempts to select the most suitable resource from the set of available resources. The primary parameters that are taken in this work for selecting the most suitable resource are the makespan and cost. The experimental result shows that the proposed method yields optimal scheduling with the atonement of all user requirements.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
This document summarizes a research paper that proposes a new algorithm called KD-Tree approach for efficient virtual machine (VM) allocation in cloud computing environments. The algorithm aims to minimize the response time for allocating VMs to user requests. It does this by adopting a KD-Tree data structure to index physical host machines, allowing the scheduler to quickly find the host that can accommodate a new VM request with the minimum latency in O(Log n) time. The proposed approach is evaluated through simulations using the CloudSim toolkit and is shown to outperform an existing linear scheduling strategy (LSTR) algorithm in terms of reducing VM allocation times.
The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
Sharing of cluster resources among multiple Workflow Applicationsijcsit
Many computational solutions can be expressed as workflows. A Cluster of processors is a shared
resource among several users and hence the need for a scheduler which deals with multi-user jobs
presented as workflows. The scheduler must find the number of processors to be allotted for each workflow
and schedule tasks on allotted processors. In this work, a new method to find optimal and maximum
number of processors that can be allotted for a workflow is proposed. Regression analysis is used to find
the best possible way to share available processors, among suitable number of submitted workflows. An
instance of a scheduler is created for each workflow, which schedules tasks on the allotted processors.
Towards this end, a new framework to receive online submission of workflows, to allot processors to each
workflow and schedule tasks, is proposed and experimented using a discrete-event based simulator. This
space-sharing of processors among multiple workflows shows better performance than the other methods
found in literature. Because of space-sharing, an instance of a scheduler must be used for each workflow
within the allotted processors. Since the number of processors for each workflow is known only during
runtime, a static schedule can not be used. Hence a hybrid scheduler which tries to combine the advantages
of static and dynamic scheduler is proposed. Thus the proposed framework is a promising solution to
multiple workflows scheduling on cluster.
This document proposes a new task scheduling algorithm called Dynamic Heterogeneous Shortest Job First (DHSJF) for heterogeneous cloud computing systems. DHSJF aims to improve performance metrics like reduced makespan and low energy consumption by considering the heterogeneity of resources and workloads. It discusses existing scheduling algorithms like Round Robin, First Come First Serve and their limitations. The proposed DHSJF algorithm prioritizes tasks with the shortest estimated completion time to optimize resource utilization and improve overall performance of the cloud computing system. Simulation results show that DHSJF provides better results for metrics like average waiting time and turnaround time as compared to Round Robin and First Come First Serve scheduling algorithms.
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneou...rahulmonikasharma
This document discusses the challenges of dynamic resource allocation and task scheduling in heterogeneous cloud environments. It outlines that resource allocation involves deciding how to allocate resources to tasks to maximize utilization, while task scheduling assigns tasks to processors to minimize execution time. The major challenges are optimizing allocated resources to minimize costs while meeting customer demands and application requirements. Allocating resources dynamically in heterogeneous cloud environments is difficult due to issues like resource contention, scarcity, and fragmentation. The document also discusses approaches to resource modeling, allocation, offering, discovery and monitoring that algorithms must address to effectively allocate resources on demand.
The document discusses using a genetic algorithm to schedule tasks in a cloud computing environment. It aims to minimize task execution time and reduce computational costs compared to the traditional Round Robin scheduling algorithm. The proposed genetic algorithm mimics natural selection and genetics to evolve optimal task schedules. It was tested using the CloudSim simulation toolkit and results showed the genetic algorithm provided better performance than Round Robin scheduling.
A survey of various scheduling algorithm in cloud computing environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The document discusses optimization of resource allocation in cloud environments using a modified particle swarm optimization (PSO) approach. It proposes a Modified Resource Allocation Mutation PSO (MRAMPSO) strategy that uses an Extended Multi Queue Scheduling algorithm to schedule tasks based on resource availability and reschedules failed tasks. The MRAMPSO strategy is compared to standard PSO and other algorithms to show it can reduce execution time, makespan, transmission cost, and round trip time.
A Survey of Job Scheduling Algorithms Whit Hierarchical Structure to Load Ba...Editor IJCATR
Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever
before. To solve these complicated problems, grid computing becomes a popular tool. a grid environment collects, integrates, and uses
heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling problems are at the heart of
any Grid-like computational system. a good scheduling algorithm can assign jobs to resources efficiently and can balance the system
load. in this paper we survey three algorithms for grid scheduling and compare benefit and disadvantages of their based on makespan.
Differentiating Algorithms of Cloud Task Scheduling Based on various Parametersiosrjce
Cloud computing is a new design structure for large, distributed data centers. Cloud computing
system promises to offer end user “pay as go” model. To meet the expected quality requirements of users, cloud
computing need to offer differentiated services to users. QoS differentiation is very important to satisfy
different users with different QoS requirements. In this paper, various QoS based scheduling algorithms,
scheduling parameters and the future scope of discussed algorithms have been studied. This paper summarizes
various cloud scheduling algorithms, findings of algorithms, scheduling factors, type of scheduling and
parameters considered
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
Optimization of energy consumption in cloud computing datacenters IJECEIAES
Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
1. The document proposes a new framework for scheduling multiple DAG applications on a cluster of processors. It involves finding the optimal and maximum number of processors that can be allotted to each DAG.
2. Regression analysis is used to model the reduction in makespan for each additional processor allotted to a DAG. This information helps determine the best way to share available processors among submitted DAGs.
3. The framework receives DAG submissions, allocates processors to each DAG, and schedules tasks on the allotted processors. The goal is to maximize resource utilization and minimize overall completion time. Experiments show this approach performs better than other methods in literature.
DGBSA : A BATCH JOB SCHEDULINGALGORITHM WITH GA WITH REGARD TO THE THRESHOLD ...IJCSEA Journal
In this paper , we will provide a scheduler on batch jobs with GA regard to the threshold detector. In The algorithm proposed in this paper, we will provide the batch independent jobs with a new technique ,so we can optimize the schedule them. To do this, we use a threshold detector then among the selected jobs, processing resources can process batch jobs with priority. Also hierarchy of tasks in each batch, will be determined with using DGBSA algorithm. Now , with the regard to the works done by previous ,we can provide an algorithm that by adding specific parameters to fitness function of the previous algorithms ,develop a optimum fitness function that in the proposed algorithm has been used. According to assessment done on DGBSA algorithm, in compare with the similar algorithms, it has more performance. The effective parameters that used in the proposed algorithm can reduce the total wasting time in compare with previous algorithms. Also this algorithm can improve the previous problems in batch processing with a new technique.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
Dynamic Task Scheduling based on Burst Time Requirement for Cloud EnvironmentIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
This document describes an improved Max-Min scheduling algorithm that considers additional constraints beyond just completion time. The improved algorithm calculates a proportional fairness score for each job/task based on its size, completion time, payload storage rate, and RAM requirements. It then sorts the jobs based on these scores to prioritize jobs with the highest scores, addressing limitations of the traditional Max-Min algorithm that only considers completion time. The algorithm is evaluated using a simulator with scientific workflows and workloads. Results show the improved algorithm efficiently schedules jobs while accounting for multiple constraints.
Grid computing can involve lot of computational tasks which requires trustworthy computational nodes. Load balancing in grid computing is a technique which overall optimizes the whole process of assigning computational tasks to processing nodes. Grid computing is a form of distributed computing but different from conventional distributed computing in a manner that it tends to be heterogeneous, more loosely coupled and dispersed geographically. Optimization of this process must contains the overall maximization of resources utilization with balance load on each processing unit and also by decreasing the overall time or output. Evolutionary algorithms like genetic algorithms have studied so far for the implementation of load balancing across the grid networks. But problem with these genetic algorithm is that they are quite slow in cases where large number of tasks needs to be processed. In this paper we give a novel approach of parallel genetic algorithms for enhancing the overall performance and optimization of managing the whole process of load balancing across the grid nodes.
A survey of various scheduling algorithm in cloud computing environmenteSAT Journals
Abstract Cloud computing is known as a provider of dynamic services using very large scalable and virtualized resources over the Internet. Due to novelty of cloud computing field, there is no many standard task scheduling algorithm used in cloud environment. Especially that in cloud, there is a high communication cost that prevents well known task schedulers to be applied in large scale distributed environment. Today, researchers attempt to build job scheduling algorithms that are compatible and applicable in Cloud Computing environment Job scheduling is most important task in cloud computing environment because user have to pay for resources used based upon time. Hence efficient utilization of resources must be important and for that scheduling plays a vital role to get maximum benefit from the resources. In this paper we are studying various scheduling algorithm and issues related to them in cloud computing. Index Terms: cloud computing, scheduling, algorithm
This document proposes a genetic algorithm called Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA) to optimize the cost of executing workflows in the public cloud. It discusses how genetic algorithms can be applied to the workflow scheduling problem to generate optimal schedules. The WSGA represents potential scheduling solutions as chromosomes, uses a fitness function to evaluate scheduling costs, and applies genetic operators like selection, crossover and mutation to evolve new schedules over multiple iterations. The goal is to minimize total execution cost while meeting workflow dependencies and deadline constraints. An experimental setup is described and the WSGA approach is claimed to reduce costs more than other heuristic scheduling algorithms for communication-intensive workflows.
Effective and Efficient Job Scheduling in Grid ComputingAditya Kokadwar
The integration of remote and diverse resources and the increasing computational needs of Grand Challenges problems combined with the faster growth of the internet and communication technologies leads to the development of global computational grids. Grid computing is a prevailing technology, which unites underutilized resources in order to support sharing of resources and services distributed across numerous administrative region. An efficient and effective scheduling system is essentially required in order to achieve the promising capacity of grids. The main goal of scheduling is to maximize the resource utilization and minimize processing time and cost of the jobs. In this research, the objective is to prioritize the jobs based on execution cost and then allocate the resources with minimum cost by merging it with conventional job grouping strategy to provide the solution for better and more efficient job scheduling which is beneficial to both user and resource broker. The proposed scheduling approach in grid computing employs a dynamic cost-based job scheduling algorithm for making an efficient mapping of a job to available resources in the grid. It also improves communication to computation ratio (CCR) and utilization of available resources by grouping the user jobs before resource allocation.
OPTIMIZED RESOURCE PROVISIONING METHOD FOR COMPUTATIONAL GRID ijgca
Grid computing is an accumulation of heterogeneous, dynamic resources from multiple administrative areas which are geographically distributed that can be utilized to reach a mutual end. Development of resource provisioning-based scheduling in large-scale distributed environments like grid computing brings in new requirement challenges that are not being believed in traditional distributed computing environments. Computational grid is applying the resources of many systems in a network to a single problem at the same time. Grid scheduling is the method by which work specified by some means is assigned to the resources that complete the work in the environment which cannot fulfill the user requirements considerably. The satisfaction of users while providing the resources might increase the beneficiary level of resource suppliers. Resource scheduling has to satisfy the multiple constraints specified by the user. The option of resource with the satisfaction of multiple constraints is the most tedious process. This trouble is solved by bringing out the particle swarm optimization based heuristic scheduling algorithm which attempts to select the most suitable resource from the set of available resources. The primary parameters that are taken in this work for selecting the most suitable resource are the makespan and cost. The experimental result shows that the proposed method yields optimal scheduling with the atonement of all user requirements.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
This document summarizes a research paper that proposes a new algorithm called KD-Tree approach for efficient virtual machine (VM) allocation in cloud computing environments. The algorithm aims to minimize the response time for allocating VMs to user requests. It does this by adopting a KD-Tree data structure to index physical host machines, allowing the scheduler to quickly find the host that can accommodate a new VM request with the minimum latency in O(Log n) time. The proposed approach is evaluated through simulations using the CloudSim toolkit and is shown to outperform an existing linear scheduling strategy (LSTR) algorithm in terms of reducing VM allocation times.
The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
Sharing of cluster resources among multiple Workflow Applicationsijcsit
Many computational solutions can be expressed as workflows. A Cluster of processors is a shared
resource among several users and hence the need for a scheduler which deals with multi-user jobs
presented as workflows. The scheduler must find the number of processors to be allotted for each workflow
and schedule tasks on allotted processors. In this work, a new method to find optimal and maximum
number of processors that can be allotted for a workflow is proposed. Regression analysis is used to find
the best possible way to share available processors, among suitable number of submitted workflows. An
instance of a scheduler is created for each workflow, which schedules tasks on the allotted processors.
Towards this end, a new framework to receive online submission of workflows, to allot processors to each
workflow and schedule tasks, is proposed and experimented using a discrete-event based simulator. This
space-sharing of processors among multiple workflows shows better performance than the other methods
found in literature. Because of space-sharing, an instance of a scheduler must be used for each workflow
within the allotted processors. Since the number of processors for each workflow is known only during
runtime, a static schedule can not be used. Hence a hybrid scheduler which tries to combine the advantages
of static and dynamic scheduler is proposed. Thus the proposed framework is a promising solution to
multiple workflows scheduling on cluster.
This document proposes a new task scheduling algorithm called Dynamic Heterogeneous Shortest Job First (DHSJF) for heterogeneous cloud computing systems. DHSJF aims to improve performance metrics like reduced makespan and low energy consumption by considering the heterogeneity of resources and workloads. It discusses existing scheduling algorithms like Round Robin, First Come First Serve and their limitations. The proposed DHSJF algorithm prioritizes tasks with the shortest estimated completion time to optimize resource utilization and improve overall performance of the cloud computing system. Simulation results show that DHSJF provides better results for metrics like average waiting time and turnaround time as compared to Round Robin and First Come First Serve scheduling algorithms.
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneou...rahulmonikasharma
This document discusses the challenges of dynamic resource allocation and task scheduling in heterogeneous cloud environments. It outlines that resource allocation involves deciding how to allocate resources to tasks to maximize utilization, while task scheduling assigns tasks to processors to minimize execution time. The major challenges are optimizing allocated resources to minimize costs while meeting customer demands and application requirements. Allocating resources dynamically in heterogeneous cloud environments is difficult due to issues like resource contention, scarcity, and fragmentation. The document also discusses approaches to resource modeling, allocation, offering, discovery and monitoring that algorithms must address to effectively allocate resources on demand.
The document discusses using a genetic algorithm to schedule tasks in a cloud computing environment. It aims to minimize task execution time and reduce computational costs compared to the traditional Round Robin scheduling algorithm. The proposed genetic algorithm mimics natural selection and genetics to evolve optimal task schedules. It was tested using the CloudSim simulation toolkit and results showed the genetic algorithm provided better performance than Round Robin scheduling.
A survey of various scheduling algorithm in cloud computing environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The document discusses optimization of resource allocation in cloud environments using a modified particle swarm optimization (PSO) approach. It proposes a Modified Resource Allocation Mutation PSO (MRAMPSO) strategy that uses an Extended Multi Queue Scheduling algorithm to schedule tasks based on resource availability and reschedules failed tasks. The MRAMPSO strategy is compared to standard PSO and other algorithms to show it can reduce execution time, makespan, transmission cost, and round trip time.
A Survey of Job Scheduling Algorithms Whit Hierarchical Structure to Load Ba...Editor IJCATR
Due to the advances in human civilization, problems in science and engineering are becoming more complicated than ever
before. To solve these complicated problems, grid computing becomes a popular tool. a grid environment collects, integrates, and uses
heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling problems are at the heart of
any Grid-like computational system. a good scheduling algorithm can assign jobs to resources efficiently and can balance the system
load. in this paper we survey three algorithms for grid scheduling and compare benefit and disadvantages of their based on makespan.
Differentiating Algorithms of Cloud Task Scheduling Based on various Parametersiosrjce
Cloud computing is a new design structure for large, distributed data centers. Cloud computing
system promises to offer end user “pay as go” model. To meet the expected quality requirements of users, cloud
computing need to offer differentiated services to users. QoS differentiation is very important to satisfy
different users with different QoS requirements. In this paper, various QoS based scheduling algorithms,
scheduling parameters and the future scope of discussed algorithms have been studied. This paper summarizes
various cloud scheduling algorithms, findings of algorithms, scheduling factors, type of scheduling and
parameters considered
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
Optimization of energy consumption in cloud computing datacenters IJECEIAES
Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.
1. The document proposes a new framework for scheduling multiple DAG applications on a cluster of processors. It involves finding the optimal and maximum number of processors that can be allotted to each DAG.
2. Regression analysis is used to model the reduction in makespan for each additional processor allotted to a DAG. This information helps determine the best way to share available processors among submitted DAGs.
3. The framework receives DAG submissions, allocates processors to each DAG, and schedules tasks on the allotted processors. The goal is to maximize resource utilization and minimize overall completion time. Experiments show this approach performs better than other methods in literature.
DGBSA : A BATCH JOB SCHEDULINGALGORITHM WITH GA WITH REGARD TO THE THRESHOLD ...IJCSEA Journal
In this paper , we will provide a scheduler on batch jobs with GA regard to the threshold detector. In The algorithm proposed in this paper, we will provide the batch independent jobs with a new technique ,so we can optimize the schedule them. To do this, we use a threshold detector then among the selected jobs, processing resources can process batch jobs with priority. Also hierarchy of tasks in each batch, will be determined with using DGBSA algorithm. Now , with the regard to the works done by previous ,we can provide an algorithm that by adding specific parameters to fitness function of the previous algorithms ,develop a optimum fitness function that in the proposed algorithm has been used. According to assessment done on DGBSA algorithm, in compare with the similar algorithms, it has more performance. The effective parameters that used in the proposed algorithm can reduce the total wasting time in compare with previous algorithms. Also this algorithm can improve the previous problems in batch processing with a new technique.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
Dynamic Task Scheduling based on Burst Time Requirement for Cloud EnvironmentIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
This document describes an improved Max-Min scheduling algorithm that considers additional constraints beyond just completion time. The improved algorithm calculates a proportional fairness score for each job/task based on its size, completion time, payload storage rate, and RAM requirements. It then sorts the jobs based on these scores to prioritize jobs with the highest scores, addressing limitations of the traditional Max-Min algorithm that only considers completion time. The algorithm is evaluated using a simulator with scientific workflows and workloads. Results show the improved algorithm efficiently schedules jobs while accounting for multiple constraints.
Grid computing can involve lot of computational tasks which requires trustworthy computational nodes. Load balancing in grid computing is a technique which overall optimizes the whole process of assigning computational tasks to processing nodes. Grid computing is a form of distributed computing but different from conventional distributed computing in a manner that it tends to be heterogeneous, more loosely coupled and dispersed geographically. Optimization of this process must contains the overall maximization of resources utilization with balance load on each processing unit and also by decreasing the overall time or output. Evolutionary algorithms like genetic algorithms have studied so far for the implementation of load balancing across the grid networks. But problem with these genetic algorithm is that they are quite slow in cases where large number of tasks needs to be processed. In this paper we give a novel approach of parallel genetic algorithms for enhancing the overall performance and optimization of managing the whole process of load balancing across the grid nodes.
A survey of various scheduling algorithm in cloud computing environmenteSAT Journals
Abstract Cloud computing is known as a provider of dynamic services using very large scalable and virtualized resources over the Internet. Due to novelty of cloud computing field, there is no many standard task scheduling algorithm used in cloud environment. Especially that in cloud, there is a high communication cost that prevents well known task schedulers to be applied in large scale distributed environment. Today, researchers attempt to build job scheduling algorithms that are compatible and applicable in Cloud Computing environment Job scheduling is most important task in cloud computing environment because user have to pay for resources used based upon time. Hence efficient utilization of resources must be important and for that scheduling plays a vital role to get maximum benefit from the resources. In this paper we are studying various scheduling algorithm and issues related to them in cloud computing. Index Terms: cloud computing, scheduling, algorithm
Optimized Resource Provisioning Method for Computational Gridijgca
Grid computing is an accumulation of heterogeneous, dynamic resources from multiple administrative areas which are geographically distributed that can be utilized to reach a mutual end. Development of resource provisioning-based scheduling in large-scale distributed environments like grid computing brings in new requirement challenges that are not being believed in traditional distributed computing environments. Computational grid is applying the resources of many systems in a network to a single problem at the same time. Grid scheduling is the method by which work specified by some means is assigned to the resources that complete the work in the environment which cannot fulfill the user requirements considerably. The satisfaction of users while providing the resources might increase the beneficiary level of resource suppliers. Resource scheduling has to satisfy the multiple constraints specified by the user. The option of resource with the satisfaction of multiple constraints is the most tedious process. This trouble is solved by bringing out the particle swarm optimization based heuristic scheduling algorithm which attempts to select the most suitable resource from the set of available resources. The primary parameters that are taken in this work for selecting the most suitable resource are the makespan and cost. The experimental result shows that the proposed method yields optimal scheduling with the atonement of all user requirements
In the era of big data, even though we have large infrastructure, storage data varies in size,
formats, variety, volume and several platforms such as hadoop, cloud since we have problem associated
with an application how to process the data which is varying in size and format. Data varying in
application and resources available during run time is called dynamic workflow. Using large
infrastructure and huge amount of resources for the analysis of data is time consuming and waste of
resources, it’s better to use scheduling algorithm to analyse the given data set, for efficient execution of
data set without time consuming and evaluate which scheduling algorithm is best and suitable for the
given data set. We evaluate with different data set understand which is the most suitable algorithm for
analysis of data being efficient execution of data set and store the data after analysis
Propose a Method to Improve Performance in Grid Environment, Using Multi-Crit...Editor IJCATR
The most important purpose of grid networks is resource subscription in a dynamic and heterogeneous environment.
They are accessible through using various methods. Subscription has mainly computational, scientific and other implications. In
order to reach grid purposes and to use available resources in grid environment, subtasks are distributed among resources and are
scheduled by considering the quality of service. It has been tried to distribute subtasks between resources in a way that maximum
QOS can be obtained. In this study, a method has been presented. In this method, three parameters; namely, sent and transferred
time between RMS and resource, process time of subtask by the resource, and the load of available tasks in resources row, have
been taken into account. In this way, multi-criteria decision is made by using TOPSIS method and this priority of the resources
are determined to assign them to subtasks. Finally, time response, as an efficient parameter, has been improved and optimized by
optimal assignment of the resources to subtasks.
Optimization of resource allocation in computational gridsijgca
The resource allocation in Grid computing system needs to be scalable, reliable and smart. It should also be adaptable to change its allocation mechanism depending upon the environment and user’s requirements. Therefore, a scalable and optimized approach for resource allocation where the system can adapt itself to the changing environment and the fluctuating resources is essentially needed. In this paper, a Teaching Learning based optimization approach for resource allocation in Computational Grids is proposed. The proposed algorithm is found to outperform the existing ones in terms of execution time and cost. The algorithm is simulated using GRIDSIM and the simulation results are presented.
The document discusses optimization of resource allocation in computational grids. It proposes using a Teaching-Learning Based Optimization (TLBO) approach for resource allocation. The TLBO algorithm is found to outperform existing algorithms like Ant Colony Optimization, Genetic Algorithm, and Particle Swarm Optimization in terms of execution time and cost. The algorithm is simulated using GRIDSIM and results are presented. Existing resource allocation strategies in computational grids are also reviewed, including static and dynamic approaches as well as auction/market-based models.
This document discusses scheduling in cloud computing. It proposes a priority-based scheduling protocol to improve resource utilization, server performance, and minimize makespan. The protocol assigns priorities to jobs, allocates jobs to processors based on completion time, and processes jobs in parallel queues to efficiently schedule jobs in cloud computing. Future work includes analyzing time complexity and completion times through simulation to validate the protocol's efficiency.
Efficient Resource Management Mechanism with Fault Tolerant Model for Computa...Editor IJCATR
Grid computing provides a framework and deployment environment that enables resource
sharing, accessing, aggregation and management. It allows resource and coordinated use of various
resources in dynamic, distributed virtual organization. The grid scheduling is responsible for resource
discovery, resource selection and job assignment over a decentralized heterogeneous system. In the
existing system, primary-backup approach is used for fault tolerance in a single environment. In this
approach, each task has a primary copy and backup copy on two different processors. For dependent
tasks, precedence constraint among tasks must be considered when scheduling backup copies and
overloading backups. Then, two algorithms have been developed to schedule backups of dependent and
independent tasks. The proposed work is to manage the resource failures in grid job scheduling. In this
method, data source and resource are integrated from different geographical environment. Faulttolerant
scheduling with primary backup approach is used to handle job failures in grid environment.
Impact of communication protocols is considered. Communication protocols such as Transmission
Control Protocol (TCP), User Datagram Protocol (UDP) which are used to distribute the message of
each task to grid resources.
Bragged Regression Tree Algorithm for Dynamic Distribution and Scheduling of ...Editor IJCATR
In the past few years, Grid computing came up as next generation computing platform which is a combination of
heterogeneous computing resources combined by a network across dynamic and geographically separated organizations. So, it
provides the perfect computing environment to solve large-scale computational demands. As the Grid computing demands are still
increasing from day to day due to rise in large number of complex jobs worldwide. So, the jobs may take much longer time to
complete due to poor distribution of batches or groups of jobs to inappropriate CPU’s. Therefore there is need to develop an efficient
dynamic job scheduling algorithm that would assign jobs to appropriate CPU’s dynamically. The main problem which dealt in the
paper is, how to distribute the jobs when the payload, importance, urgency, flow time etc. dynamically keeps on changing as the grid
expands or is flooded with number of job requests from different machines within the grid.
In this paper, we present a scheduling strategy which takes the advantage of decision tree algorithm to take dynamic decision
based on the current scenarios and which automatically incorporates factor analysis for considering the distribution of jobs.
This document provides a comparative analysis of various grid-based scheduling algorithms. It discusses six different algorithms: Min-Min, Sufferage, Heterogeneous Earliest Finish Time (HEFT), Critical Path-On-a-Processor (CPOP), Reliability Aware Scheduling Algorithm with Duplication of HDC System (RASD), and Hierarchical Job Scheduling for Clusters of Workstations (HJS). It compares the algorithms based on parameters like response time, resource utilization, load balancing, and considers factors like architecture, environment, and dynamicity. The document concludes that grid scheduling is important for optimizing resource allocation in distributed, heterogeneous environments.
Comparative Analysis of Various Grid Based Scheduling Algorithmsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Distributed Feature Selection for Efficient Economic Big Data AnalysisIRJET Journal
The document proposes a new framework for efficiently analyzing large and high-dimensional economic big data. The framework combines methods for economic feature selection and econometric model construction to identify patterns in economic development from vast amounts of economic indicator data. It relies on three key aspects: 1) novel data pre-processing techniques to prepare high-quality economic data, 2) an innovative distributed feature identification solution to locate important economic indicators from multidimensional datasets, and 3) new econometric models to capture patterns of economic development. The framework is demonstrated on economic data collected over 30 years from over 300 towns in Dalian, China.
This document provides an overview of scheduling mechanisms in cloud computing. It discusses task scheduling, gang scheduling based on performance and cost evaluation, and resource scheduling. For task scheduling, it describes classifying tasks based on quality of service parameters and MapReduce level scheduling. It then explains two gang scheduling algorithms - Adaptive First Come First Serve (AFCFS) and Largest Job First Serve (LJFS) - and how they are used to evaluate performance and cost. Finally, it briefly discusses resource scheduling and factors that affect scheduling mechanisms in cloud computing like efficiency, fairness, costs, and communication patterns.
An adaptive algorithm for task scheduling for computational grideSAT Journals
Abstract
Grid Computing is a collection of computing and storage resources that are collected from multiple administrative domains. Grid resources can be applied to reach a common goal. Since computational grids enable the sharing and aggregation of a wide variety of geographically distributed computational resources, an effective task scheduling is vital for managing the tasks. Efficient scheduling algorithms are the need of the hour to achieve efficient utilization of the unused CPU cycles distributed geographically in various locations. The existing job scheduling algorithms in grid computing are mainly concentrated on the system’s performance rather than the user satisfaction. This research work presents a new algorithm that mainly focuses on better meeting the deadlines of the statically available jobs as expected by the users. This algorithm also concentrates on the better utilization of the available heterogeneous resources.
Keywords: Task Scheduling, Computational Grid, Adaptive Scheduling and User Deadline.
This document summarizes and compares various scheduling algorithms used in cloud computing environments. It begins with an introduction to cloud computing and the need for scheduling algorithms in cloud environments. It then describes several existing scheduling algorithms, including compromised-time-cost scheduling, particle swarm optimization-based heuristic, improved cost-based algorithm, resource-aware scheduling, innovative transaction intensive cost-constraint scheduling, scalable heterogeneous earliest-finish-time algorithm, and multiple QoS constrained scheduling strategy of multi-workflows. These algorithms aim to optimize metrics such as execution time, cost, deadline, load balancing, and quality of service. The document concludes by comparing the different scheduling strategies.
Similar to GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGORITHM IN GRID COMPUTING (20)
11th International Conference on Computer Science, Engineering and Informati...ijgca
11th International Conference on Computer Science, Engineering and Information
Technology (CSEIT 2024) will provide an excellent international forum for sharing knowledge
and results in theory, methodology and applications of Computer Science, Engineering and
Information Technology. The Conference looks for significant contributions to all major fields of
the Computer Science and Information Technology in theoretical and practical aspects. The aim
of the conference is to provide a platform to the researchers and practitioners from both academia
as well as industry to meet and share cutting-edge development in the field.
SERVICE LEVEL AGREEMENT BASED FAULT TOLERANT WORKLOAD SCHEDULING IN CLOUD COM...ijgca
Cloud computing is a concept of providing user and application oriented services in a virtual environment.
Users can use the various cloud services as per their requirements dynamically. Different users have
different requirements in terms of application reliability, performance and fault tolerance. Static and rigid
fault tolerance policies provide a consistent degree of fault tolerance as well as overhead. In this research
work we have proposed a method to implement dynamic fault tolerance considering customer
requirements. The cloud users have been classified in to sub classes as per the fault tolerance requirements.
Their jobs have also been classified into compute intensive and data intensive categories. The varying
degree of fault tolerance has been applied consisting of replication and input buffer. From the simulation
based experiments we have found that the proposed dynamic method performs better than the existing
methods.
SERVICE LEVEL AGREEMENT BASED FAULT TOLERANT WORKLOAD SCHEDULING IN CLOUD COM...ijgca
Cloud computing is a concept of providing user and application oriented services in a virtual environment.
Users can use the various cloud services as per their requirements dynamically. Different users have
different requirements in terms of application reliability, performance and fault tolerance. Static and rigid
fault tolerance policies provide a consistent degree of fault tolerance as well as overhead. In this research
work we have proposed a method to implement dynamic fault tolerance considering customer
requirements. The cloud users have been classified in to sub classes as per the fault tolerance requirements.
Their jobs have also been classified into compute intensive and data intensive categories. The varying
degree of fault tolerance has been applied consisting of replication and input buffer. From the simulation
based experiments we have found that the proposed dynamic method performs better than the existing
methods.
11th International Conference on Computer Science, Engineering and Informatio...ijgca
11th International Conference on Computer Science, Engineering and Information Technology (CSEIT 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science, Engineering and Information Technology. The Conference looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.
Load balancing functionalities are crucial for best Grid performance and utilization. Accordingly,this
paper presents a new meta-scheduling method called TunSys. It is inspired from the natural phenomenon of
heat propagation and thermal equilibrium. TunSys is based on a Grid polyhedron model with a spherical
like structure used to ensure load balancing through a local neighborhood propagation strategy.
Furthermore, experimental results compared to FCFS, DGA and HGA show encouraging results in terms
of system performance and scalability and in terms of load balancing efficiency.
11th International Conference on Computer Science and Information Technology ...ijgca
11th International Conference on Computer Science and Information Technology (CSIT 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science and Information Technology. The Conference looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
AN INTELLIGENT SYSTEM FOR THE ENHANCEMENT OF VISUALLY IMPAIRED NAVIGATION AND...ijgca
Technological advancement has brought the masses unprecedented convenience, but unnoticed by many, a
population neglected through the age of technology has been the visually impaired population. The visually
impaired population has grown through ages with as much desire as everyone else to adventure but lack
the confidence and support to do so. Time has transported society to a new phase condensed in big data,
but to the visually impaired population, this quick-pace living lifestyle, along with the unpredictable nature
of natural disaster and COVID-19 pandemic, has dropped them deeper into a feeling of disconnection from
the society. Our application uses the global positioning system to support the visually impaired in
independent navigation, alerts them in face of natural disasters, and reminds them to sanitize their devices
during the COVID-19 pandemic
13th International Conference on Data Mining & Knowledge Management Process (...ijgca
13th International Conference on Data Mining & Knowledge Management Process (CDKP 2024) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.
Call for Papers - 15th International Conference on Wireless & Mobile Networks...ijgca
15th International Conference on Wireless & Mobile Networks (WiMoNe 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Wireless & Mobile computing Environment. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Wireless, Mobile Networks & its applications had received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Call for Papers - 4th International Conference on Big Data (CBDA 2023)ijgca
4th International Conference on Big Data (CBDA 2023) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data.
Call for Papers - 15th International Conference on Computer Networks & Commun...ijgca
15th International Conference on Computer Networks & Communications (CoNeCo 2023) looks for significant contributions to the Computer Networks & Communications for Wired and Wireless Networks in theoretical and practical aspects. Original papers are invited on Computer Networks, Network Protocols and Wireless Networks, Data Communication Technologies, and Network Security. The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Call for Papers - 15th International Conference on Computer Networks & Commun...ijgca
15th International Conference on Computer Networks & Communications (CoNeCo 2023) looks for significant contributions to the Computer Networks & Communications for Wired and Wireless Networks in theoretical and practical aspects. Original papers are invited on Computer Networks, Network Protocols and Wireless Networks, Data Communication Technologies, and Network Security. The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Call for Papers - 9th International Conference on Cryptography and Informatio...ijgca
9th International Conference on Cryptography and Information Security (CRIS 2023) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of cryptography, coding and Information security.
Call for Papers - 9th International Conference on Cryptography and Informatio...ijgca
9th International Conference on Cryptography and Information Security (CRIS 2023) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of cryptography, coding and Information security.
Call for Papers - 4th International Conference on Machine learning and Cloud ...ijgca
4th International Conference on Machine learning and Cloud Computing (MLCL 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Cloud computing. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Call for Papers - 11th International Conference on Data Mining & Knowledge Ma...ijgca
11th International Conference on Data Mining & Knowledge Management Process (DKMP 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Data Mining and knowledge management process. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern data mining concepts and establishing new collaborations in these areas.
Call for Papers - 4th International Conference on Blockchain and Internet of ...ijgca
4th International Conference on Blockchain and Internet of Things (BIoT 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Blockchain and Internet of Things. The Conference looks for significant contributions to all major fields of the Blockchain and Internet of Things in theoretical and practical aspects.
Call for Papers - International Conference IOT, Blockchain and Cryptography (...ijgca
The 4th International Conference on Cloud, Big Data and Web Services (CBW 2023) will take place from March 25-26, 2023 in Sydney, Australia. The conference aims to facilitate the exchange of innovative ideas and research related to cloud computing, big data, and web services. Authors are invited to submit papers by February 18, 2023 on topics including cloud platforms, big data analytics, and web service models and architectures. Selected papers will be published in related journals.
Call for Paper - 4th International Conference on Cloud, Big Data and Web Serv...ijgca
4th International Conference on Cloud, Big Data and Web Services (CBW 2023) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and Web services. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and web services.
Call for Papers - International Journal of Database Management Systems (IJDMS)ijgca
The International Journal of Database Management Systems (IJDMS) is a bi monthly open access peer-reviewed journal that publishes articles which contributenew results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this filed and establishing new collaborations in these areas.
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إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
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GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGORITHM IN GRID COMPUTING
1. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
DOI: 10.5121/ijgca.2012.3405 55
GROUPING BASED JOB SCHEDULING ALGORITHM
USING PRIORITY QUEUE AND HYBRID ALGORITHM
IN GRID COMPUTING
Pinky Rosemarry1
, Ravinder Singh2
, Payal Singhal3
and Dilip Sisodia4
Department of Computer Engineering,
Govt. Engineering College, Ajmer, India
1
Pinkyrose001@gmail.com
2
ravikaviya@gmail.com
3
payalsinghal8@gmail.com
4
sisodia.dilip@gmail.com
ABSTRACT
Grid computing enlarge with computing platform which is collection of heterogeneous computing
resources connected by a network across dynamic and geographically dispersed organization to form a
distributed high performance computing infrastructure. Grid computing solves the complex computing
problems amongst multiple machines. Grid computing solves the large scale computational demands in a
high performance computing environment. The main emphasis in the grid computing is given to the
resource management and the job scheduler .The goal of the job scheduler is to maximize the resource
utilization and minimize the processing time of the jobs. Existing approaches of Grid scheduling doesn’t
give much emphasis on the performance of a Grid scheduler in processing time parameter. Schedulers
allocate resources to the jobs to be executed using the First come First serve algorithm. In this paper, we
have provided an optimize algorithm to queue of the scheduler using various scheduling methods like
Shortest Job First, First in First out, Round robin. The job scheduling system is responsible to select best
suitable machines in a grid for user jobs. The management and scheduling system generates job schedules
for each machine in the grid by taking static restrictions and dynamic parameters of jobs and machines
into consideration. The main purpose of this paper is to develop an efficient job scheduling algorithm to
maximize the resource utilization and minimize processing time of the jobs. Queues can be optimized by
using various scheduling algorithms depending upon the performance criteria to be improved e.g. response
time, throughput. The work has been done in MATLAB using the parallel computing toolbox.
KEYWORDS
Grid Computing, Job Scheduling, Job Grouping.
1. INTRODUCTION
Grid computing, originally motivated by wide-area sharing of computational resources [1], has
evolved to be mainstream technologies for enabling large-scale virtual organization [2]. During
the beginning to mid 1990’s distributed computing was holding a big account on the research
projects. One of the researches on it was that to develop a tool that will allow it to act like a single
big computer. Ian Foster of the US department of energy’s Argonne National labs and university
of Chicago has given a demonstration to create one super “meta computer” called I way.The term
“Grid” refers to systems and application that integrate resources and services distributed across
multiple control domains [3].Computational grids provide large-scale resource sharing, such as
personal computer, clusters, MPPs, Data Base, and online instructions, which may be cross-
domain, dynamic and heterogeneous[4]. Grid computing needs to support various services:
2. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
56
security, uniform access, resource management, job scheduling, application composition,
computational economy, and accounting. In a Grid computing environment, scheduler is
responsible for selecting the suitable machines or computing resources in Grid for processing jobs
to achieve high system throughput, but there exist several applications with a large number of
lightweight jobs [5].Job scheduling with light weight gives low performance in terms of
processing time and communication time. So to achieve high performance, jobs are to be
scheduled in group instead of light weight jobs. The main purpose of this paper is to develop an
efficient job scheduling algorithm to maximize the resource utilization and minimize processing
time of the jobs, how they are grouped and allocated to resources in dynamic environment. This
paper is organized as follows section 2, discusses related work, section 3 the scheduling activity,
section 4 the proposed Algorithm, section 5 describes our experiments and the results and section
6 gives the conclusion of the paper.
2. RELATED WORK
The paper proposes [5] an algorithm which is designed for minimize overhead time and
computational time. It is a big challenge to design an efficient scheduler. There exists some
grouping based scheduling and non grouping based scheduling. To minimize total processing
time by reducing overhead time and computational time grouping based job scheduling is used.
On the other hand maximizing resource utilization, without grouping based scheduling is used.
Overall processing time is reduced with the help of Modified grouping based job scheduling in
computational grid.To achieve better performance the concept of grouping based job scheduling
is extended.
An agent based dynamic resource scheduling strategy [6] focuses on maximize the processing
time of jobs. The process of selecting a job is based on maximum heap tree. The processed
outsource model is a hierarchical two layer approach in which top layer is called grid level an d
other is called cluster level. In this model with FCFS-Job Grouping Strategy has of two major
parts, first part provides resource management, which selects highest computational power cluster
at the grid level and the second part provides FCFS-job grouping strategy that makes efficient
utilization of the selected cluster by submitting a matching group of jobs from a FCFS job queue.
But this paper does not consider bandwidth and file size constraint except computational power of
the resource.
In paper [7] adaptive fine grained job scheduling algorithm (AFJS) is described which focuses on
scheduling lightweight jobs. Before this algorithm, many other algorithms were developed which
considered either the application characteristics or resource characteristics but not both. AFJS
algorithm is the algorithm that considers both the characteristics. This algorithm starts with
obtaining information about the resources and the resource monitoring mechanism is based on
GRIM prototype and GRIR protocol. The algorithm has a constraint which says that the
processing time of the coarse-grained job should not exceed the expected time and to ensure that
the execution of a parallel program is faster than sequential execution, the calculation time should
exceed communication time.
In paper author proposed a grouping based fine grained job scheduling algorithm [8] starts with
obtaining information about the resources. In this light weight jobs are grouped as coarse grained
jobs. The grouping based algorithm utilized algorithm utilized resourced efficiently of integrates
greedy algorithm of FCFS algorithm. This model reduces the total processing time of jobs,
maximizes the utilization of the resources and reduces the network latency. The time complexity
scheduling algorithm is very high. It does not pay any attention to memory size constraint and
preprocessing time of job grouping is high.
3. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
57
In bandwidth aware job grouping based scheduling algorithm the job grouping concept [9] is
explored coupled with bandwidth aware scheduling. This algorithm focuses on grouping
independent jobs having small processing requirement into jobs with larger processing
requirements and then schedules them according to network conditions. The concept of
bandwidth was used for performing load balancing at stream control transmission protocol
(SCTP) layer. Its main objective was to provide the in-order delivery over multiple paths. This
approach reduces total job processing time as compared to job scheduling without grouping.
A Dynamic job grouping based scheduling algorithm [10] group the jobs according to MIPS of
the resource. It selects resource in first come first serve order. It selects jobs and group the jobs
and assigns the group jobs in FCFS order and compare to resource if group job MI is less that to
resource MIPS of this process continues until the resource MIPS is less to group job.
Scheduling framework for Bandwidth-aware strategy [11] schedules jobs in grid systems by
taking of their computational capabilities and the communication capabilities of the resource’s
into consideration. It uses network bandwidth of resources to determine the priority of each
resource. The job grouping approach is used in the framework where the scheduler retrieves
information of the resources processing capability. The scheduler selects the first resource and
groups independent fine-grained jobs together based on chosen resources processing capability.
These jobs are grouped in such a way that maximizes the utilization of the resources and reduces
the total processing time. After grouping, all the jobs are sent to the corresponding resource’s
whose connection can be finished earlier which implies that the smallest request is issued through
the fastest connection giving best transmission rate or bandwidth. However, this strategy does not
take dynamic characteristics of the resources into account, and preprocessing time of job
grouping and resource selection are also high.
Hierarchical job scheduling approach used two levels Scheduling [12]global scheduling & local
scheduling .The global scheduler uses separate queues for different type of the jobs for
scheduling with the FCFS,SJF and first fit (FF) and the local scheduler uses same queue for
different type of the jobs.
Optimal Resource Constraint (ORC) scheduling algorithm [13] includes the combination of both
the Best fit allocation and Round Robin scheduling to allocate the jobs in queue pool. This
algorithm improved the efficiency of load balancing and dynamicity capability of the grid
resources.
3. SCHEDULING ACTIVITY
• Find available parallel computing resources from the defined parallel configuration (job
manger).
• Create job object in the scheduler and in the client.
• Create new task in job.
• Calculating the minimum MI among the jobs.
• Sort the jobs in array with minimum MI and the Shortest time job is executed first
• The time slot is defined for all the jobs to be executed and the Job with higher MI above
time slot allocated to it executed using round robin algorithm.
• The time required for each job using all three algorithms is plotted on the graph
• The execution time required for all jobs using all three algorithms(Shortest Job First, First
Come First Serve, Round Robin) is plotted which is less than the execution time required
for all jobs using only one algorithm (Shortest Job First).
4. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
58
Figure 1. Scheduling Activity
Job schedule
Create job
Find resource in the grid
Create Task
Calculating Time of each task
Creating shortest job first queue
Sending jobs to FIFO queue
Then to round robin
Execution of jobs on worker
Results in workspace of client
5. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
59
4. PROPOSED ALGORITHM
4.1. Algorithm Steps
1. find Resource: Find available parallel computing resources
2. Create Job: Create parallel job object as many as your requirement
3. Create Task: create task for job to be performed for each job.
4. tic; %calculating execution time for each job start
time
5. create Task(job1, @rand, 1, {{3,3}}); %for example
6. time_1=toc; %end time
7. time_array=[execution times of all jobs in array]
8. job_array=[parallel job objects in array];
9. g=[1 2 3 4 5];
10. shortest_job=[];
11. -----------------------------------Shortest job first-----------------------------------------------------
-
12. for k=1:No. of jobs
13. shortest_job_time=min(time_array); %min execution time job
14. for i=1: No. of jobs
15. if(execution time of jobs in array is min);
16. Then creating array of jobs with minimum MI jobs
17. l(k)=g(i); %for displaying the job no. that had been executed
18. And making the minimum MI job maximum to get next min value
19. end;
20. end;
21. end;
22. -------------------------------------FIFO and Round Robin---------------------------------
23. timeout=0.02; %time slot for each job
24. round_robin=[];
25. for i=1: No. of jobs
26. if(time slot allocated to each job is greater than execution time of respective job)
27. tic;
28. send the job to the job scheduler for computing
29. execution_time(i)=toc;
30. w(i)=shortest_job(i); %array of jobs executed within allocated time slot
31. end;
32. end;
33. for i=1: No. of jobs
34. if(time slot allocated to each job less than execution time of respective job from original job
array)
35. tic;
36. send the job to the job scheduler for computing
37. execution_time(i)=toc;
38. x(i)=m(i); % array of jobs executed out of allocated time slot
39. end;
40. end;
41. round_robin=[array of combination of jobs executed within and out of allocated time slot];
6. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
60
4.2 Description of the Algorithm
The find Resource function performs two functions firstly it identifies the available parallel
computing resources and job managers. Job managers create on object representing a job manager
in your local MATLAB session. To find a specific job manager use parameter –value pairs for
matching. For an example MyJobManager is the name of the job manager while MyJMhost is the
host name of the machine running the job manager lookup service. The create job function creates
parallel job as many as required using job manager configuration. The job is actually created on
job manager but it executes in client session.
The Create task function defines the task to be executed by the job for each job. Tasks are
assigned to the jobs, once the job is created by the job manager. Tasks define the function to be
evaluated by the workers during the running of the job. The tic variable calculates the execution
time for each job in the task from the start time. Create task has four parameters the first
parameter defined the state of the job the second parameters defined its function the third
parameters gives start time of the jobs and the fourth parameter gives the running time. In this
example, each task will generate a 3 by 3 matrix of random numbers.
Execution time of each task in each job is calculated before it is send to queue of the job
scheduler. The execution time of these jobs is stored in the array called time array. The variable
time array is used for shortest job first using the time array, The variable ‘g’ is used for the
sequence of jobs that will execute using all the three algorithms. The jobs are arranged in
ascending order using the loop the minimum value is first stored in array in first place and that
value is made maximum in order to get next minimum value in the array .The sorted jobs are
stored in the variable called shortest job. Then these jobs are sent to the FIFO and Round_ robin
queue. All the sorted jobs in shortest job array are passed through the queue as first in first out
and are further sent to the next algorithm implementation. The time slot for each job to be
executed is defined initially. In the first for loop, all the sorted jobs who’s execution time is more
than the time slot assigned is send to queue using the summit function. Now, in second for loop
the job with time more than timeout is detected and is sorted in. This jobs whose time is more
than the defined time slot is interrupted and execute finally. To plot time on the graph the final
execution time required for all the jobs is stored in the variable ‘final execution’. All the jobs
executed within the timeout and interrupted and executed out off timeout are stored in the
variable ‘round robin’ for plotting.
5. EXPERIMENTAL EVALUATION
5.1 Experimental setup and comparison
Simulation experiments have been carried out using MATLAB v7.4. The client should have the
parallel computing toolbox for grid computing. If not should install the toolbox for parallel
computing. The scheduler or job manger manages the job sent from the client and sends to the
worker. The worker should have MDCE (Matlab Distributed Computing Server) install as
administer. The MDCE server and its installation are explained further.
7. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
61
Figure 2. Parallel Computing Configurations
Table 1: Execution time Comparison
The above table shows the execution time comparison between the FIFO algorithm and all the
three algorithms (Shortest time first, FIFO and Round robin).
NO. OF JOBS
PROCESSING TIME
Hybrid algorithm FIFO algorithm
Job 1 1.9405 2.0539
Job 2 1.7848 1.7968
Job 3 2.9623 3.3426
Job 4 2.8408 3.3287
Job 5 2.7098 2.9536
8. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
62
Figure 3.Execution time using FIFO algorithms
Figure 4. Execution time using all three algorithms (SJF, FIFO, RR)
9. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
63
Figure 5. Implementation of all three algorithms with time constraints
Figure 3 shows the execution time required using only one algorithm (FIFO), the y-axis range is
from 1.6 to 3.6. The time required is more than using all three algorithms as shown figure and the
table also. Figure 4 show the execution time required using all three algorithms (Shortest Job
First, First in First Out, Round Robin), the y axis range is from 1.6 to 3. The time required is less
as shown in figure. From figure 2 and 3 we can conclude that time required for FIRST IN FIRST
OUT ALGO is more than our hybrid algorithms, which is shown in figure 1 with their execution
timings.
6. CONCLUSIONS
An algorithm is designed for an efficient job scheduling algorithm to maximize the resource
utilization and minimize processing time of the jobs. We have proposed an efficient three
scheduling algorithm (Shortest Job First, First Come First Serve, Round Robin) for jobs and send
to the queue. We have got some better performance in terms of processing time than job
scheduling on the FIFO algorithm. Also we have implemented Shortest Job First algorithm along
with all three algorithms for performance analysis. The simulation results have shown that the
proposed model is able to achieve the mentioned objectives in grid environment. However,
allocating large number of jobs to one resource will increase the processing time. Therefore to
avoid this situation during job grouping activity, the total number of jobs group should be created
such that the processing loads among the selected resource are balanced. The comparative study
also shows that the proposed hybrid algorithm gives better performance than shortest job first
algorithm alone in terms of processing time. The proposed approach has been critically analyzed.
Additionally, the simulation environment is semi-dynamic and it can’t reflect in the real
computational grid environment sufficiently that promotes further research in the proposed area.
In future research , the resource can be managed by considering some more factors like current
load of resource, network delay, QoS(Quality of Service) requirements’ will be taken into
account
10. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
64
ACKNOWLEDGEMENTS
The authors are thankful to everyone who supported or motivated us
REFERENCES
[1] I. Foster and C. Kesselman, The Grid: Blueprint for a New Computing Infrastructure, Morgan-
Kaufmann, 1998.
[2] I. Foster, C. Kesselman and S. Tuecke, “The Anatomy of the Grid: Enabling Scalable Virtual
Organizations”, International Journal of Supercomputer Applications, Vol. 15, No. 3, 2001.
[3] Foster, I. and Kesselman, C. Computation Grids. Foster, I. and Kesselman, C. eds. The Grid:
Blueprint for a New Computing Infrastructure, Morgan Kaufmann, 1999, 2-48.
[4] Ian Foster and Carl Kesselman, “The Grid: Blueprint for a New Computing Infrastructure,” Elsevier
Inc., Singapore, Second Edition, 2004.
[5] N. Muthuvelu, Junyan Liu, N.L.Soe, S.venugopal, A.Sulistio, and R.Buyya, “A dynamic job
grouping-based scheduling for deploying applications with fine-grained tasks on global grids,” in
Proc of Australasian workshop on grid computing, vol. 4, pp. 41–48, 2005.
[6] Raksha Sharma, Vishnu Kant Soni, Manoj Kumar Mishra,“An Agent Based Dynamic Resource
Scheduling Model With FCFS-Job Grouping Strategy In Grid Computing”, Waset, ICCGCS 2010.
In Press.
[7] Quan Liu, Yeqing Liao, “Grouping-Based Fine-grained Job Scheduling in Grid Computing”,Vol.1,
pp.556- 559, IEEE First International Workshop on Education Technology and Computer Science,
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[8] T.F Ang, W.K.Ng, T.C Ling, “A Bandwidth-Aware Job Grouping-Based Scheduling on Grid
Environment”,Information Technology Journal, vol .8, No.3, pp. 372-377, 2009.
[9] Nithiapidary Muthuvelu, Junyang Liu, “A Dynamic Job Grouping- Based Scheduling for Deploying
Application with Fine-Grained tasks on Global Grids, Australasian Workshop on Grid Computing
and e- Research, vol. 44, AusGrid -2005
[10] Ng Wai Keat,Ang Tan Fong,Ling Teck Chaw,Liew Chee Sun,“Scheduling Framework For
Bandhwidth-Aware Job Grouping-Based Scheduling In Grid Computing”,Vol.19(2), Malaysian
Journal Of Computer Science, 2006.
[11] J. Santoso; G.D. van Albada; B.A.A. Nazief and P.M.A. Sloot:“Hierarchical Job Scheduling for
Clusters of Workstations”, ASCI 2000, pp. 99-105. ASCI, Delft, June 2000.
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Constraint (ORC) Scheduling”,VOL.8 No.6, IJCSNS International Journal of Computer Science and
Network Security, June 2008
11. International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.4, December 2012
65
Authors
Pinky Rosemarry has received her B.Tech degree in CSE (Computer Science &
Engineering) in 2010 from Ajmer Institute of Technology, Ajmer affiliated to
Rajasthan Technical University, Kota and M.tech from Govt. Engineering College,
Ajmer in 2012 (RTU-Kota). She has presented several papers in international &
national conferences. Her research interest includes Grid Computing.
Mr.Ravinder Singh (Assistant Professor) received his B.E (Computer
Engineering)from Sobhasaria engineering college, Sikar in 2006.M.E (CSE) from,
Motilal Nehru National Institute of Technology, 2009 India. He has three years
teaching experience. He is presently working as Assistant Professor (CS & IT) in
Govt. engineering College, Ajmer. He has several papers published in international
and national journals/conferences. He is the guide of various projects of B.Tech.
students and also the guide of more than 5 students of M.Tech. thesis.
Payal Singhal has received her B.Tech degree in CSE (Computer Science &
Engineering) in 2010 from Ajmer Institute of Technology, Ajmer affiliated to
Rajasthan Technical University, Kota and M.tech from Govt. Engineering College,
Ajmer in 2012 (RTU-Kota). She has presented several papers in international &
national conferences. Her research interest includes Grid Computing.
Mr. Dilip Sisodia (Assistant Professor) received his B.E (Computer Engineering)
from Government Engineering College, Ajmer in 2006. He has five years teaching
experience. He is presently working as Assistant Professor (CS & IT) in Govt.
engineering College, Ajmer.He is the guide of various projects of B.Tech. students.