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.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
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.
A novel scheduling algorithm for cloud computing environmentSouvik Pal
Cloud computing is the most recent computing paradigm, in the
Information Technology where the resources and information are provided
on-demand and accessed over the Internet. An essential factor in the cloud computing
system is Task Scheduling that relates to the efficiency of the entire cloud
computing environment. Mostly in a cloud environment, the issue of scheduling is
to apportion the tasks of the requesting users to the available resources. This paper
aims to offer a genetic based scheduling algorithm that reduces the waiting time of
the overall system. However the tasks enter the cloud environment and the users
have to wait until the resources are available that leads to more queue length and
increased waiting time. This paper introduces a Task Scheduling algorithm based
on genetic algorithm using a queuing model to minimize the waiting time and
queue length of the system.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
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.
Resource scheduling is a most important functioning area for the cloud manager and challenge as well. It plays very vital role to maintain the scalability in the cloud resources and ‘on demand’ availability of cloud. The challenges arise because the Cloud Service Provider (CSP) has to pretend to have infinite resource while he has limited amount of resource. Resource allocation in cloud computing means managing resources in such a way that every demand (task) must be fulfilled along with considering the parameter like throughput, cost, make span, availability, utilization of resource, time and reliability. The Modified Resource Allocation Mutation PSO (MRAMPSO) strategy based on the resource scheduling and allocation of cloud is proposed. In this paper MRAMPSO schedules the task with help of Extended Multi Queue (EMQ) by considering the resource availability and reschedule the task that fails to allocate. This approach is compared with slandered PSO and Longest Cloudlet to Fastest Processor (LCFP) algorithm to show that MRAMPSO can save execution time, makes span, transmission cost and round trip time.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
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.
A novel scheduling algorithm for cloud computing environmentSouvik Pal
Cloud computing is the most recent computing paradigm, in the
Information Technology where the resources and information are provided
on-demand and accessed over the Internet. An essential factor in the cloud computing
system is Task Scheduling that relates to the efficiency of the entire cloud
computing environment. Mostly in a cloud environment, the issue of scheduling is
to apportion the tasks of the requesting users to the available resources. This paper
aims to offer a genetic based scheduling algorithm that reduces the waiting time of
the overall system. However the tasks enter the cloud environment and the users
have to wait until the resources are available that leads to more queue length and
increased waiting time. This paper introduces a Task Scheduling algorithm based
on genetic algorithm using a queuing model to minimize the waiting time and
queue length of the system.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
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.
Resource scheduling is a most important functioning area for the cloud manager and challenge as well. It plays very vital role to maintain the scalability in the cloud resources and ‘on demand’ availability of cloud. The challenges arise because the Cloud Service Provider (CSP) has to pretend to have infinite resource while he has limited amount of resource. Resource allocation in cloud computing means managing resources in such a way that every demand (task) must be fulfilled along with considering the parameter like throughput, cost, make span, availability, utilization of resource, time and reliability. The Modified Resource Allocation Mutation PSO (MRAMPSO) strategy based on the resource scheduling and allocation of cloud is proposed. In this paper MRAMPSO schedules the task with help of Extended Multi Queue (EMQ) by considering the resource availability and reschedule the task that fails to allocate. This approach is compared with slandered PSO and Longest Cloudlet to Fastest Processor (LCFP) algorithm to show that MRAMPSO can save execution time, makes span, transmission cost and round trip time.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
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.
Optimization of workload prediction based on map reduce frame work in a cloud...eSAT 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
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.
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.
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.
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 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.
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.
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.
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.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
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
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
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.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
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.
Optimization of workload prediction based on map reduce frame work in a cloud...eSAT 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
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.
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.
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.
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 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.
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.
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.
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.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
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
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
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.
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.
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
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.
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
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.
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
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.
Fuzzy based control using lab view for miso temperature processeSAT 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.
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
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.
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.
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
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.
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.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
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.
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.
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
RSDC (Reliable Scheduling Distributed in Cloud Computing)IJCSEA Journal
In this paper we will present a reliable scheduling algorithm in cloud computing environment. In this algorithm we create a new algorithm by means of a new technique and with classification and considering request and acknowledge time of jobs in a qualification function. By evaluating the previous algorithms, we understand that the scheduling jobs have been performed by parameters that are associated with a failure rate. Therefore in the roposed algorithm, in addition to previous parameters, some other important parameters are used so we can gain the jobs with different scheduling based on these parameters. This work is associated with a mechanism. The major job is divided to sub jobs. In order to balance the jobs we should calculate the request and acknowledge time separately. Then we create the scheduling of each job by calculating the request and acknowledge time in the form of a shared job. Finally efficiency of the system is increased. So the real time of this algorithm will be improved in comparison with the other algorithms. Finally by the mechanism presented, the total time of processing in cloud computing is improved in comparison with the other algorithms.
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
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.
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.
Max Min Fair Scheduling Algorithm using In Grid Scheduling with Load Balancing IJORCS
This paper shows the importance of fair scheduling in grid environment such that all the tasks get equal amount of time for their execution such that it will not lead to starvation. The load balancing of the available resources in the computational grid is another important factor. This paper considers uniform load to be given to the resources. In order to achieve this, load balancing is applied after scheduling the jobs. It also considers the Execution Cost and Bandwidth Cost for the algorithms used here because in a grid environment, the resources are geographically distributed. The implementation of this approach the proposed algorithm reaches optimal solution and minimizes the make span as well as the execution cost and bandwidth cost.
Demand-driven Gaussian window optimization for executing preferred population...IJECEIAES
Scheduling is one of the essential enabling technique for Cloud computing which facilitates efficient resource utilization among the jobs scheduled for processing. However, it experiences performance overheads due to the inappropriate provisioning of resources to requesting jobs. It is very much essential that the performance of Cloud is accomplished through intelligent scheduling and allocation of resources. In this paper, we propose the application of Gaussian window where jobs of heterogeneous in nature are scheduled in the round-robin fashion on different Cloud clusters. The clusters are heterogeneous in nature having datacenters with varying sever capacity. Performance evaluation results show that the proposed algorithm has enhanced the QoS of the computing model. Allocation of Jobs to specific Clusters has improved the system throughput and has reduced the latency.
Cost-Efficient Task Scheduling with Ant Colony Algorithm for Executing Large ...Editor IJCATR
The aim of cloud computing is to share a large number of resources and pieces of equipment to compute and store knowledge and information for great scientific sources. Therefore, the scheduling algorithm is regarded as one of the most important challenges and problems in the cloud. To solve the task scheduling problem in this study, the ant colony optimization (ACO) algorithm was adapted from social theories with a fair and accurate resource allocation approach based on machine performance and capacity. This study was intended to decrease the runtime and executive costs. It was also meant to optimize the use of machines and reduce their idle time. Finally, the proposed method was compared with Berger and greedy algorithms. The simulation results indicate that the proposed algorithm reduced the makespan and executive cost when tasks were added. It also increased fairness and load balancing. Moreover, it made the optimal use of machines possible and increased user satisfaction. According to evaluations, the proposed algorithm improved the makespan by 80%.
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.
The Impact of Data Replication on Job Scheduling Performance in Hierarchical ...graphhoc
In data-intensive applications data transfer is a primary cause of job execution delay. Data access time depends on bandwidth. The major bottleneck to supporting fast data access in Grids is the high latencies of Wide Area Networks and Internet. Effective scheduling can reduce the amount of data transferred across the internet by dispatching a job to where the needed data are present. Another solution is to use a data replication mechanism. Objective of dynamic replica strategies is reducing file access time which leads to reducing job runtime. In this paper we develop a job scheduling policy and a dynamic data replication strategy, called HRS (Hierarchical Replication Strategy), to improve the data access efficiencies. We study our approach and evaluate it through simulation. The results show that our algorithm has improved 12% over the current strategies
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Runway Orientation Based on the Wind Rose Diagram.pptx
An enhanced adaptive scoring job scheduling algorithm with replication strategy in grid environment
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 03 Issue: 04 | Apr-2014, Available @ http://www.ijret.org 680
AN ENHANCED ADAPTIVE SCORING JOB SCHEDULING ALGORITHM
WITH REPLICATION STRATEGY IN GRID ENVIRONMENT
S. K. Aparnaa1
, K. Kousalya2
1
Student, Kongu Engineering College, Perundurai, Erode-638052
2
Faculty, Kongu Engineering College, Perundurai, Erode-638052
Abstract
Grid computing is a form of distributed computing that involves coordinating and sharing data storage and network resource. The
goal of grid job scheduling is to achieve high system throughput and match the job to the appropriate available computing resource.
The complexity of scheduling problem increases with heterogeneous nature of grid and is highly difficult to schedule effectively.
Existing algorithm does not adapt to the dynamic grid environment. In order to utilize the power of grid completely and to assign job
to the resource dynamically an efficient algorithm called Adaptive Scoring Job Scheduling (ASJS) was introduced. However the
bandwidth and storage capacity occupied by data intensive and computational intensive job is high and each time the user have to
specify whether the job is computational intensive or data intensive. . Due to this problem the jobs are not completed in time. To
provide a solution to that problem Enhanced Adaptive Scoring Job scheduling algorithm is introduced. The jobs are identified
whether it is data intensive or computational intensive and based on that the jobs are scheduled. The jobs are allocated by computing
Cluster Score (CS). The jobs that are submitted by the user is divided into sub tasks and replicated. By using this strategy the job
occupies lower storage capacity and bandwidth. Due to the dynamic nature of grid environment, each time the status of the resources
changes and each time the Cluster Score (CS) is computed and the jobs are replicated and allocated to the most appropriate
resources.
Keywords: Grid Computing, Resources, Scheduling. Replication
----------------------------------------------------------------------***--------------------------------------------------------------------
1. INTRODUCTION
Grid can be classified as computational grid and data grid. The
computational grid facilitates efficient computation power and
CPU available. The data grid facilitates efficient storage and
distributions of data. A computational grid is a collection of
heterogeneous computing nodes for computation intensive
jobs. A data grid connects geographically distributed computer
and storage resources, enabling users to share data and other
resources.
Grid computing aims at aggregating resources such as Central
Processing Unit (CPU) speed, load and storage space to solve
a single task. Grid computing combines the power of both
parallel computing and distributed computing. Distributed
computing supports resource sharing and parallel computing
supports computing power.
Scheduling is defined as the process of allocating jobs by
selecting best resource from collection of resources. The main
purpose of scheduling is to balance the entire system and
complete the execution of jobs as soon as possible. In grid,
many users may face hundreds of computers to utilize and it is
impossible for anyone to assign jobs manually in grids.
A good job scheduling algorithm should adjust according to
the changing the status of the entire environment and types of
jobs. Some characteristics that are intrinsic to grids should be
considered during scheduling, such as resources, dynamic
nature of machines, network load, bandwidth latency and
topology.
Data replication is an important optimization step to manage
large data by replicating data in various sites. The major
challenge is a decision problem i.e. how many replicas should
be created and where replicas should be stored. Hence new
methods are needed to create replicas that increase availability
without using unnecessary storage and bandwidth.
2. RELATED WORK
Paranhos et al (2003) [2] presented algorithm to increase the
performance of the system and to schedule Bag-of-Tasks
(BOT) application. Workqueue with Replication (WQR)
algorithm is used to solve the problem. The tasks are
replicated the replica which completes the job first is
considered as a valid execution of task and other replicas are
cancelled. Although the performance of the system is
increased it wastes a lot of computing power.
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 03 Issue: 04 | Apr-2014, Available @ http://www.ijret.org 681
Sheng et al (2005) [9] proposed an adaptive and dynamic
scheduling method called the Most Fit Task First Scheduling
(MFTF) for a class of computational grids, which are
characterized by heterogeneous computing nodes and dynamic
task arrivals. This method calculates the expected task
execution time in order to know which task is suitable for the
node. The most suitable resource to a task is assigned based on
the fitness value. The larger the fitness value will indicate the
greater suitability between the task and node. Therefore, the
load of the resources with greater speed becomes heavy and
more number of tasks will be queued.
Yan et al (2005) [3] proposed an algorithm called improved
ant algorithm for job scheduling in grid computing. This
algorithm is based on an adaptive scheduling heuristics and
load balancing component to improve the job finishing rate
and load balancing rate. The load balancing rate which is
related to job finishing rate is introduced to change the
pheromone. This will make the job finishing rate for different
resource being similar and the load balancing will be
improved.
Figueria and Tan Trieu (2008) dealt with storage capacity
planning for data grids. Due to massive size of data the task of
managing and distributing data quickly is a problem and hence
the planning of storage capacity in data grids are carried out.
The storage capacity for each node is minimum and this is
assigned based on the network topology and workload
characteristics. The data replication strategies are used to
reduce the bandwidth consumption. The dynamic replication
strategy replicates file based on the notion that recently
accessed files are more likely to be accessed again by the
nearby nodes.
Chang et al (2009) [5] dealt with an algorithm called Balanced
Ant Colony Optimization(BACO) algorithm which is used to
balance the load of the entire system and to minimize the
makespan time for the given set of jobs. The pheromone
indicator for each resource and each job is calculated based
the estimated transmission time and execution time. The jobs
are assigned to each resource based on pheromone indicator
and executes the job. This algorithm does not consider the real
status of the resource it assigns resource not only to good
performance but also to bad ones.
Abdi et al (2010) [1] dealt with an algorithm called
Hierarchical Replication Strategy (HRS) to improve data
access and efficiencies. The bandwidth between the regions
are considered as the main factor replica selection and
deletion. The Replica Manager controls the data which is
transferred in each region. This algorithm reduces the
execution time of the job by reducing the job access time.
Syed et al (2012) [6] proposed an algorithm called Dynamic
Multilevel Hybrid Scheduling Algorithm(MHS) using Median
and Dynamic Multilevel Hybrid Scheduling Algorithm using
Square Root(MHR) to provide solution to fixed quantum
problem. The main aim of this algorithm is to execute the jobs
with minimum turnaround time and with minimum response
time. The following are the features of this algorithm. First,
this algorithm favours the shortest job for execution. Second,
execute the job on the basis of a dynamic time quantum, to
fairly distribute processor time among grid jobs. And third
feature is that they always execute the longest job, thus
avoiding starvation.
Wei et al (2012) [9] dealt with an algorithm to improve the
grid task scheduling called as an improved ant algorithm. This
algorithm focuses on task scheduling in unsuccessful
situations, improve robustness and successful probability of
task allocation to the resource and shorten the completion time
of the task. The task finds the resource by using resource
selection rule and if the task find the resource but fails to
complete then using redistribution rule the tasks are again
redistributed.
Chang et al (2012) [4] dealt with an algorithm called Adaptive
Scoring Job Scheduling (ASJS) algorithm to shorten the
completion time and enhance the system throughput. Both the
data intensive jobs and computation intensive jobs are
assigned to the cluster. The jobs are scheduled based the
selection of the most appropriate resource. The jobs are
allocated to the cluster which has the highest Cluster Score
(CS) and the highest the cluster which has the highest
computation power. Each time the status of the resource
changes the global and local update rules are performed.
Based on the global and local update the jobs are scheduled.
3. PROPOSED WORK
The proposed algorithm computes the Cluster Score (CS), the
jobs that are submitted by the user is divided into sub tasks
and replicated. The replicated tasks are allocated to the cluster
which has the highest score.
3.1 Proposed Scheduling Architecture
In the proposed system, the user submits the job to the grid
scheduler and based on the Cluster Score(CS) which is
computed jobs are allocated to the cluster of resource. The
grid scheduler identifies whether it is a data intensive job or
computational intensive job by using job information specified
by the user. The computational intensive job needs more
computing power, the CPU_Available value will be large
compared to other jobs and data intensive job needs more
transmission power. These jobs need more bandwidth to
transmit the job. Based on these conditions the jobs are
identified and they are allocated.
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Fig 1: Overall architecture diagram
The proposed architecture which is illustrated in Fig 1 shows
how jobs are allocated to the cluster of resources. Job 1, Job
2…Job n are the ‘n’ number of jobs which can be a
computational intensive or data intensive jobs. The grid
scheduler allocates the job to the cluster of resource by
computing ATP,ACP. Suppose if the storage capacity and
bandwidth need for the job is high the job is divided in sub
task.Each subtask is replicated The cluster of resources
include CPU Speed, Load, CPU Available, Computation
power (CP) and Memory Available.
3.2 Enhanced Adaptive Scoring Job Scheduling
Algorithm with Replication Strategy
In Enhanced Adaptive Scoring Job Scheduling algorithm
along with ATP (Average Transmission Power) and ACP
(Average Computation Power) [4] of each resource is
calculated and the total size of the jobs are divided and the sub
tasks are replicated. The replicated jobs are then assigned to
the cluster which has the highest cluster score. The following
algorithm gives a detailed description about how jobs are
scheduled.
Step 1.The Average Transmission Power (ATP) which is
calculated using the formula
ATP =
1
_ / 1
m
j
Bandwidth available m
Bandwidth_available - bandwidth which is available between
each cluster
m - number of clusters
Step 2 The Average Computation Power (ACP) which is
calculated using the formula
ACP =
1
_ *(1 ) /
n
k
CPU Speed load n
CPU_Speed - speed of the resource in a cluster
load - current load of the cluster
n - number of resources in the cluster
Step 3 Let Sj be the total size of the job which is divided into
subtasks
Sj= {S1,S2………Sn}
S-Subtasks
n-number of subtasks
Step 4 For each sub task Sj the replica of the tasks are
generated
Rj= {RF1,RF2………RFn}
RF-Replicated file
n-number of replicated file
Step 5 The Cluster Score (CS) is computed by using the
formula
CS=α.ATP+β.ACP
α-coefficient value of ATP
β-coefficient value of ACP
The coefficient values should always be equal to1 ie, α+β=1
Step 6 The replicated tasks are allocated to the Cluster which
has the highest Cluster Score (CS)
Step 7: The replica which finishes the job first is considered
and other replicas are terminated.
The CP value is also calculated by taking the CPU Available
value and dividing the value by 10 and the normalized value is
divided with other CPU Available value to get CP value.
For example, 2 jobs Job1 and Job2 .Let Job1 be a data
intensive job whose size is 200 MB and Job 2 be a
computational intensive job. The Table 1 shows the status of
the resource. The bandwidth between the Cluster A and
Cluster B is 8.57 and bandwidth between Cluster B and
Cluster C is 9.43 and bandwidth between Cluster A and
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Volume: 03 Issue: 04 | Apr-2014, Available @ http://www.ijret.org 683
Cluster C is 10.57. Assume value of α=0.5, β=0.5 such that the
sum of their values is equal to 1. Based on the job information
which is specified by the user the values of α, β In the Table 1
the CPU Available which is calculated by using the formula
In the Table 1 the CPU Available which is calculated by using
the formula
CPU_Available=CPU_Speed*(1-load)
Similarly the CP value is also calculated by taking the highest
CPU Available value. The normalization range is set from 1 to
10.
Each time the status of the resource changes and based on the
CPU Available and load information of the cluster the CP is
calculated. The Job1 whose size is 200 MB is divided into
subtask. Each subtask is replicated as RF1,RF2….RFn are
allocate to the cluster. The replica which finishes the job first
is considered and other replicas are terminated.
Based on the status of the resource given in Table 1 the
formulas are applied to obtain the computation result for ATP,
ACP,CS which is given in Table 2
The initial status of the resources which are considered here is
R1,R2,R3,R4,R5,R6 and there are three clusters Cluster A,
Cluster B and Cluster C. There are two resources that are
grouped under each cluster. The resources that are considered
are CPU Speed, Load, CPU Available and Memory Available.
The resources that are grouped under each cluster need not
have to be uniform. Each cluster can contain multiple number
of resources. Based on which the jobs are scheduled.
The replicas are allocated to the cluster and the replica which
finishes the job first is considered and other replicas are
terminated.
Table 1 Initial status of the resource
Cluster A Cluster B Cluster C
R1 R2 R3 R4 R5 R6
CPU
Speed
(MHz)
3200 3000 3100 3100 340
0
2800
Load(%
)
30 25 25 20 25 20
CPU
Avail
(MHz)
2240 2250 2325 2480 255
0
2240
CP 8.78 8.82 9.12 9.73 10 8.78
Based on the initial status of the resource the ATP, ACP
values are calculated.
Table 2 Result of Computation after assigning Job 1
Cluster A Cluster B Cluster C
ATP 9 9.57 10
ACP 8 9.43 9.37
CS 8.50 9.64 9.45
The Cluster B has the highest Cluster Score (CS) but the size
of the job is 200 MB and the memory available in Cluster B is
20 MB hence the jobs are divided into tasks S1,S2…Sn and
each subtask is replicated RF1,RF2..RFn. The replicas are
allocated to the cluster. The replica which finishes the job first
is considered and other replicas are terminated. Similarly for n
number of jobs the CS is computed and allocated. The make
span of n number of jobs are computed.
3.3 Algorithm Implementation
The following are the modules that are used for the
implementation of the algorithm
Fig.2 Algorithm Implementation
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Volume: 03 Issue: 04 | Apr-2014, Available @ http://www.ijret.org 684
4. SIMULATION ENVIRONMENT
The simulation tool which is used for the proposed system is
GridSim. The GridSim simulator provides a platform and
enable the users to model and simulate the characteristic of
grid resource. It supports job scheduling and distributes
diverse set of resources for job scheduling. GridSim is a Java-
based toolkit.
5. RESULTS AND ANALYSIS
The simulation parameters that are used for our proposed
algorithm are
Parameter Value
Number of Jobs 1000
Number of Resources 250
Computing Power 1500-5000
Number of Clusters 9
Size of memory(MB) 500
Jobs Submitted 100-200
Based on the above simulation parameter the ATP, ACP and
CS values are calculated. The jobs are replicated and allocated
to the cluster. The replica which finishes the job first is
considered and other replicas are terminated.
Table 3: Makespan of jobs
Number of
Jobs
Time in milliseconds
α=0.3, β=0.7 α=0.7, β=0.3
Job 1 150.2345 200.789
Job 2 220.65 100.567
Job 3 300.267 350.789
Job 4 430.567 234.78
In the above Table 3 the makespan for four jobs are calculated
Compared to the previous Adaptive Scoring Job Scheduling
Algorithm the makespan time is reduced after using the
Enhanced Adaptive Scoring Job Scheduling Algorithm with
replication strategy. Similarly the makespan can be calculated
for 1000 jobs
Here the values α=0.3, β=0.7 represent the value for
computational intensive jobs and α=0.7, β=0.3 represent the
value of data intensive jobs. The makespan time of both the
jobs are calculated.
6. CONCLUSIONS
The proposed Enhanced Adaptive Scoring Job Scheduling
Algorithm with replication strategy method schedule jobs in
dynamic heterogeneous grid environment. The algorithm
divides the jobs into subtasks. The subtasks are replicated and
the replicated job is assigned to the cluster. Jobs that are
considered in this methodology are independent and the jobs
are allocated to the cluster by computing cluster score. The job
whether it is data intensive or computational intensive can also
be identified without user specification and based on that the
jobs can be scheduled.
ACKNOWLEDGEMENTS
I extend my gratitude to my supervisor, Dr.K.Kousalya M.E.,
Ph.D., for her valuable ideas and suggestions, which have
been very helpful in the project
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