1. The document proposes a new framework for scheduling multiple DAG applications on a cluster of processors. It involves finding the optimal and maximum number of processors that can be allotted to each DAG.
2. Regression analysis is used to model the reduction in makespan for each additional processor allotted to a DAG. This information helps determine the best way to share available processors among submitted DAGs.
3. The framework receives DAG submissions, allocates processors to each DAG, and schedules tasks on the allotted processors. The goal is to maximize resource utilization and minimize overall completion time. Experiments show this approach performs better than other methods in literature.
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.
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...IRJET Journal
This document proposes a scheduling algorithm for allocating resources in cloud computing based on the Project Evaluation and Review Technique (PERT). It aims to address issues like starvation of lower priority tasks. The algorithm models task allocation as a directed acyclic graph and uses PERT to schedule critical and non-critical tasks, prioritizing higher priority tasks. The algorithm is evaluated against other scheduling methods and shows improvements in reducing completion time and optimizing resource allocation for all tasks.
This document proposes a genetic algorithm called Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA) to optimize the cost of executing workflows in the public cloud. It discusses how genetic algorithms can be applied to the workflow scheduling problem to generate optimal schedules. The WSGA represents potential scheduling solutions as chromosomes, uses a fitness function to evaluate scheduling costs, and applies genetic operators like selection, crossover and mutation to evolve new schedules over multiple iterations. The goal is to minimize total execution cost while meeting workflow dependencies and deadline constraints. An experimental setup is described and the WSGA approach is claimed to reduce costs more than other heuristic scheduling algorithms for communication-intensive workflows.
A novel scheduling algorithm for cloud computing environmentSouvik Pal
The document describes a proposed genetic algorithm-based scheduling approach for cloud computing environments. It aims to minimize waiting time and queue length. The algorithm first permutes task burst times and finds minimum waiting times using FCFS and genetic algorithms. It then applies a queuing model to the sequences with minimum waiting time from each approach. Experimental results on 4 sample tasks show the genetic algorithm reduces waiting time compared to FCFS. The genetic operators of selection, crossover and mutation are applied to evolve optimal task scheduling sequences.
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.
This document presents a scheduling strategy that performs dynamic job grouping at runtime to optimize the execution of applications with many fine-grained tasks on global grids. The strategy groups individual jobs into larger "job groups" based on the processing requirements of each job, the capabilities of available grid resources, and a defined granularity size. It aims to minimize overall job execution time and cost while maximizing resource utilization. The strategy is evaluated through simulations using the GridSim toolkit, which models grid resources and application scheduling.
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.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
This document describes an enhanced adaptive scoring job scheduling algorithm with replication strategy for grid environments. The algorithm aims to improve upon an existing adaptive scoring job scheduling algorithm by identifying whether jobs are data-intensive or computation-intensive. It then divides large jobs into subtasks, replicates the subtasks, and allocates the replicas to clusters based on a computed cluster score in order to improve resource utilization and job completion times. The algorithm is evaluated through simulation using the GridSim toolkit.
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.
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...IRJET Journal
This document proposes a scheduling algorithm for allocating resources in cloud computing based on the Project Evaluation and Review Technique (PERT). It aims to address issues like starvation of lower priority tasks. The algorithm models task allocation as a directed acyclic graph and uses PERT to schedule critical and non-critical tasks, prioritizing higher priority tasks. The algorithm is evaluated against other scheduling methods and shows improvements in reducing completion time and optimizing resource allocation for all tasks.
This document proposes a genetic algorithm called Workflow Scheduling for Public Cloud Using Genetic Algorithm (WSGA) to optimize the cost of executing workflows in the public cloud. It discusses how genetic algorithms can be applied to the workflow scheduling problem to generate optimal schedules. The WSGA represents potential scheduling solutions as chromosomes, uses a fitness function to evaluate scheduling costs, and applies genetic operators like selection, crossover and mutation to evolve new schedules over multiple iterations. The goal is to minimize total execution cost while meeting workflow dependencies and deadline constraints. An experimental setup is described and the WSGA approach is claimed to reduce costs more than other heuristic scheduling algorithms for communication-intensive workflows.
A novel scheduling algorithm for cloud computing environmentSouvik Pal
The document describes a proposed genetic algorithm-based scheduling approach for cloud computing environments. It aims to minimize waiting time and queue length. The algorithm first permutes task burst times and finds minimum waiting times using FCFS and genetic algorithms. It then applies a queuing model to the sequences with minimum waiting time from each approach. Experimental results on 4 sample tasks show the genetic algorithm reduces waiting time compared to FCFS. The genetic operators of selection, crossover and mutation are applied to evolve optimal task scheduling sequences.
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.
This document presents a scheduling strategy that performs dynamic job grouping at runtime to optimize the execution of applications with many fine-grained tasks on global grids. The strategy groups individual jobs into larger "job groups" based on the processing requirements of each job, the capabilities of available grid resources, and a defined granularity size. It aims to minimize overall job execution time and cost while maximizing resource utilization. The strategy is evaluated through simulations using the GridSim toolkit, which models grid resources and application scheduling.
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.
An enhanced adaptive scoring job scheduling algorithm with replication strate...eSAT Publishing House
This document describes an enhanced adaptive scoring job scheduling algorithm with replication strategy for grid environments. The algorithm aims to improve upon an existing adaptive scoring job scheduling algorithm by identifying whether jobs are data-intensive or computation-intensive. It then divides large jobs into subtasks, replicates the subtasks, and allocates the replicas to clusters based on a computed cluster score in order to improve resource utilization and job completion times. The algorithm is evaluated through simulation using the GridSim toolkit.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
This document describes a proposed grouping based job scheduling algorithm for grid computing that aims to maximize resource utilization and minimize job processing times. It discusses related work on job scheduling algorithms and then presents the steps of the proposed algorithm. The algorithm uses shortest job first, first-in first-out, and round robin scheduling to process jobs in groups. The algorithm is evaluated experimentally in MATLAB and shown to reduce total job processing time compared to using only first-in first-out scheduling. Graphs demonstrate the processing time improvements achieved by the combined scheduling approach.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
This document summarizes a research paper that proposes a new algorithm called KD-Tree approach for efficient virtual machine (VM) allocation in cloud computing environments. The algorithm aims to minimize the response time for allocating VMs to user requests. It does this by adopting a KD-Tree data structure to index physical host machines, allowing the scheduler to quickly find the host that can accommodate a new VM request with the minimum latency in O(Log n) time. The proposed approach is evaluated through simulations using the CloudSim toolkit and is shown to outperform an existing linear scheduling strategy (LSTR) algorithm in terms of reducing VM allocation times.
The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
The task scheduling is a key process in large-scale distributed systems like cloud computing infrastructures
which can have much impressed on system performance. This problem is referred to as a NP-hard problem
because of some reasons such as heterogeneous and dynamic features and dependencies among the
requests. Here, we proposed a bi-objective method called DWSGA to obtain a proper solution for
allocating the requests on resources. The purpose of this algorithm is to earn the response quickly, with
some goal-oriented operations. At first, it makes a good initial population by a special way that uses a bidirectional
tasks prioritization. Then the algorithm moves to get the most appropriate possible solution in a
conscious manner by focus on optimizing the makespan, and considering a good distribution of workload
on resources by using efficient parameters in the mentioned systems. Here, the experiments indicate that
the DWSGA amends the results when the numbers of tasks are increased in application graph, in order to
mentioned objectives. The results are compared with other studied algorithms.
Service Request Scheduling in Cloud Computing using Meta-Heuristic Technique:...IRJET Journal
This document discusses using the Teaching Learning Based Optimization (TLBO) meta-heuristic technique for service request scheduling between users and cloud service providers. TLBO is a nature-inspired algorithm that mimics the teacher-student learning process. It is compared to other meta-heuristic algorithms like Genetic Algorithm. The key steps of TLBO involve initializing a population, evaluating fitness, selecting the best solution as teacher, and updating the population through teacher and learner phases until termination criteria is met. The document proposes using number of users and virtual machines as parameters for TLBO scheduling in cloud computing. MATLAB simulation results show the initial and final iterations converging to an optimal scheduling solution.
Scheduling Divisible Jobs to Optimize the Computation and Energy Costsinventionjournals
ABSTRACT : The important challenge in cloud computing environment is to design a scheduling strategy to handle jobs, and to process them in a heterogeneous environment with shared data centers. In this paper, we attempt to investigate a new analytical framework model that enables an existing private cloud data-center for scheduling jobs and minimizing the overall computation and energy cost together. Our model is based on Divisible Load Theory (DLT) model to derive closed-form solution for the load fractions to be assigned to each machines considering computation and energy cost. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user’s job. Finally, we quantify the performance of the strategies via rigorous simulation studies.
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneou...rahulmonikasharma
This document discusses the challenges of dynamic resource allocation and task scheduling in heterogeneous cloud environments. It outlines that resource allocation involves deciding how to allocate resources to tasks to maximize utilization, while task scheduling assigns tasks to processors to minimize execution time. The major challenges are optimizing allocated resources to minimize costs while meeting customer demands and application requirements. Allocating resources dynamically in heterogeneous cloud environments is difficult due to issues like resource contention, scarcity, and fragmentation. The document also discusses approaches to resource modeling, allocation, offering, discovery and monitoring that algorithms must address to effectively allocate resources on demand.
The document discusses optimization of resource allocation in cloud environments using a modified particle swarm optimization (PSO) approach. It proposes a Modified Resource Allocation Mutation PSO (MRAMPSO) strategy that uses an Extended Multi Queue Scheduling algorithm to schedule tasks based on resource availability and reschedules failed tasks. The MRAMPSO strategy is compared to standard PSO and other algorithms to show it can reduce execution time, makespan, transmission cost, and round trip time.
A location based least-cost scheduling for data-intensive applicationsIAEME Publication
This document summarizes a research paper that proposes a location-based least-cost scheduling algorithm for transferring multiple data-intensive files simultaneously to multiple compute nodes in a grid environment. The proposed model includes an optimized meta-scheduler that receives multiple files, predicts the optimal number of parallel TCP streams to use for each file transfer based on sampling, and schedules the files to compute nodes using a greedy algorithm that considers location and cost. Experimental results showed the optimized model achieved better transfer times and throughput compared to non-optimized transfers.
The document discusses using a genetic algorithm to schedule tasks in a cloud computing environment. It aims to minimize task execution time and reduce computational costs compared to the traditional Round Robin scheduling algorithm. The proposed genetic algorithm mimics natural selection and genetics to evolve optimal task schedules. It was tested using the CloudSim simulation toolkit and results showed the genetic algorithm provided better performance than Round Robin scheduling.
A Framework and Methods for Dynamic Scheduling of a Directed Acyclic Graph on...IDES Editor
The data flow model is gaining popularity as a
programming paradigm for multi-core processors. Efficient
scheduling of an application modeled by Directed Acyclic
Graph (DAG) is a key issue when performance is very
important. DAG represents computational solutions, in which
the nodes represent tasks to be executed and edges represent
precedence constraints among the tasks. The task scheduling
problem in general is a NP-complete problem[2]. Several static
scheduling heuristics have been proposed. But the major
problem in static list scheduling is the inherent difficulty in
exact estimation of task cost and edge cost in a DAG and also
its inability to consider and manage with runtime behavior of
tasks. This underlines the need for dynamic scheduling of a
DAG. This paper presents how in general, dynamic scheduling
of a DAG can be done. Also proposes 4 simple methods to
perform dynamic scheduling of a DAG. These methods have
been simulated and experimented using a representative set
of DAG structured computations from both synthetic and real
problems. The proposed dynamic scheduler performance is
found to be in comparable with that of static scheduling
methods. The performance comparison of the proposed
dynamic scheduling methods is also carried out.
Optimized Assignment of Independent Task for Improving Resources Performance ...ijgca
Grid computing has emerged from category of distributed and parallel computing where the
heterogeneous resources from different network are used simultaneously to solve a particular problem that
need huge amount of resources. Potential of Grid computing depends on my issues such as security of
resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling
is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing
resources and is an NP-complete problem. To achieve the promising potential of grid computing, an
effective and efficient job scheduling algorithm is proposed, which will optimized two important criteria to
improve the performance of resources i.e. makespan time & resource utilization. With this, we have
classified various tasks scheduling heuristic in grid on the basis of their characteristics.
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.
This document evaluates the performance of a hybrid differential evolution-genetic algorithm (DE-GA) approach for load balancing in cloud computing. It first provides background on cloud computing and load balancing. It then describes the DE-GA approach, which uses differential evolution initially and switches to genetic algorithm if needed. The results show that the hybrid DE-GA approach improves performance over differential evolution and genetic algorithm alone, reducing makespan, average response time, and improving resource utilization. The study demonstrates the benefits of the hybrid evolutionary algorithm for an important problem in cloud computing.
Earlier stage for straggler detection and handling using combined CPU test an...IJECEIAES
This document summarizes a research paper that proposes a new framework called the combinatory late-machine (CLM) framework to facilitate early detection and handling of straggler tasks in MapReduce jobs. Straggler tasks significantly increase job execution time and energy consumption. The CLM framework combines CPU testing and the Longest Approximate Time to End (LATE) methodology to calculate a straggler tolerance threshold earlier. This allows for prompt mitigation actions. The paper reviews related work on straggler detection techniques and discusses the proposed methodology, which estimates task finish times based on progress scores. It aims to correlate straggler detection with system attributes like resource utilization that could cause delays.
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.
Energy efficient resource management for high-performance clustersXiao Qin
In the past decade, high-performance cluster computing platforms have been widely used to solve challenging and rigorous engineering tasks in industry and scientific applications. Due to extremely high energy cost,reducing energy consumption has become a major
concern in designing economical and environmentally friendly cluster computing
infrastructures for many high-performance applications. The primary focus of this talk is to illustrate how to improve energy efficiency of clusters and storage systems without significantly degrading performance. In this talk, we will first describe a general architecture
for building energy-efficient cluster computing platforms. Then, we will outline several energyefficient scheduling algorithms designed for high-performance clusters and large-scale storage systems. The experimental results using both synthetic and real world applications
show that energy dissipation in clusters can be reduced with a marginal degradation of system performance.
We looked at the data. Here’s a breakdown of some key statistics about the nation’s incoming presidents’ addresses, how long they spoke, how well, and more.
The document discusses how startup entrepreneurs think and operate. It notes that startups like Airbnb and Uber were started due to identifying shortages or problems. It emphasizes that startups focus on providing customer benefit, eliminating waste, and creating value. It also highlights that startups operate with speed, embracing failure fast and pivoting quickly, with transparency and by breaking rules. Startups succeed by moving rapidly, with minimal processes and instead prioritizing speed above all else.
This document discusses how emojis, emoticons, and text speak can be used to teach students. It provides background on the origins of emoticons in 1982 as ways to convey tone and feelings in text communications. It then suggests that with text speak and emojis, students can translate, decode, summarize, play with language, and add emotion to language. A number of websites and apps that can be used for emoji-related activities, lessons, and discussions are also listed.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
This document describes a proposed grouping based job scheduling algorithm for grid computing that aims to maximize resource utilization and minimize job processing times. It discusses related work on job scheduling algorithms and then presents the steps of the proposed algorithm. The algorithm uses shortest job first, first-in first-out, and round robin scheduling to process jobs in groups. The algorithm is evaluated experimentally in MATLAB and shown to reduce total job processing time compared to using only first-in first-out scheduling. Graphs demonstrate the processing time improvements achieved by the combined scheduling approach.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
This document summarizes a research paper that proposes a new algorithm called KD-Tree approach for efficient virtual machine (VM) allocation in cloud computing environments. The algorithm aims to minimize the response time for allocating VMs to user requests. It does this by adopting a KD-Tree data structure to index physical host machines, allowing the scheduler to quickly find the host that can accommodate a new VM request with the minimum latency in O(Log n) time. The proposed approach is evaluated through simulations using the CloudSim toolkit and is shown to outperform an existing linear scheduling strategy (LSTR) algorithm in terms of reducing VM allocation times.
The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
The task scheduling is a key process in large-scale distributed systems like cloud computing infrastructures
which can have much impressed on system performance. This problem is referred to as a NP-hard problem
because of some reasons such as heterogeneous and dynamic features and dependencies among the
requests. Here, we proposed a bi-objective method called DWSGA to obtain a proper solution for
allocating the requests on resources. The purpose of this algorithm is to earn the response quickly, with
some goal-oriented operations. At first, it makes a good initial population by a special way that uses a bidirectional
tasks prioritization. Then the algorithm moves to get the most appropriate possible solution in a
conscious manner by focus on optimizing the makespan, and considering a good distribution of workload
on resources by using efficient parameters in the mentioned systems. Here, the experiments indicate that
the DWSGA amends the results when the numbers of tasks are increased in application graph, in order to
mentioned objectives. The results are compared with other studied algorithms.
Service Request Scheduling in Cloud Computing using Meta-Heuristic Technique:...IRJET Journal
This document discusses using the Teaching Learning Based Optimization (TLBO) meta-heuristic technique for service request scheduling between users and cloud service providers. TLBO is a nature-inspired algorithm that mimics the teacher-student learning process. It is compared to other meta-heuristic algorithms like Genetic Algorithm. The key steps of TLBO involve initializing a population, evaluating fitness, selecting the best solution as teacher, and updating the population through teacher and learner phases until termination criteria is met. The document proposes using number of users and virtual machines as parameters for TLBO scheduling in cloud computing. MATLAB simulation results show the initial and final iterations converging to an optimal scheduling solution.
Scheduling Divisible Jobs to Optimize the Computation and Energy Costsinventionjournals
ABSTRACT : The important challenge in cloud computing environment is to design a scheduling strategy to handle jobs, and to process them in a heterogeneous environment with shared data centers. In this paper, we attempt to investigate a new analytical framework model that enables an existing private cloud data-center for scheduling jobs and minimizing the overall computation and energy cost together. Our model is based on Divisible Load Theory (DLT) model to derive closed-form solution for the load fractions to be assigned to each machines considering computation and energy cost. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user’s job. Finally, we quantify the performance of the strategies via rigorous simulation studies.
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneou...rahulmonikasharma
This document discusses the challenges of dynamic resource allocation and task scheduling in heterogeneous cloud environments. It outlines that resource allocation involves deciding how to allocate resources to tasks to maximize utilization, while task scheduling assigns tasks to processors to minimize execution time. The major challenges are optimizing allocated resources to minimize costs while meeting customer demands and application requirements. Allocating resources dynamically in heterogeneous cloud environments is difficult due to issues like resource contention, scarcity, and fragmentation. The document also discusses approaches to resource modeling, allocation, offering, discovery and monitoring that algorithms must address to effectively allocate resources on demand.
The document discusses optimization of resource allocation in cloud environments using a modified particle swarm optimization (PSO) approach. It proposes a Modified Resource Allocation Mutation PSO (MRAMPSO) strategy that uses an Extended Multi Queue Scheduling algorithm to schedule tasks based on resource availability and reschedules failed tasks. The MRAMPSO strategy is compared to standard PSO and other algorithms to show it can reduce execution time, makespan, transmission cost, and round trip time.
A location based least-cost scheduling for data-intensive applicationsIAEME Publication
This document summarizes a research paper that proposes a location-based least-cost scheduling algorithm for transferring multiple data-intensive files simultaneously to multiple compute nodes in a grid environment. The proposed model includes an optimized meta-scheduler that receives multiple files, predicts the optimal number of parallel TCP streams to use for each file transfer based on sampling, and schedules the files to compute nodes using a greedy algorithm that considers location and cost. Experimental results showed the optimized model achieved better transfer times and throughput compared to non-optimized transfers.
The document discusses using a genetic algorithm to schedule tasks in a cloud computing environment. It aims to minimize task execution time and reduce computational costs compared to the traditional Round Robin scheduling algorithm. The proposed genetic algorithm mimics natural selection and genetics to evolve optimal task schedules. It was tested using the CloudSim simulation toolkit and results showed the genetic algorithm provided better performance than Round Robin scheduling.
A Framework and Methods for Dynamic Scheduling of a Directed Acyclic Graph on...IDES Editor
The data flow model is gaining popularity as a
programming paradigm for multi-core processors. Efficient
scheduling of an application modeled by Directed Acyclic
Graph (DAG) is a key issue when performance is very
important. DAG represents computational solutions, in which
the nodes represent tasks to be executed and edges represent
precedence constraints among the tasks. The task scheduling
problem in general is a NP-complete problem[2]. Several static
scheduling heuristics have been proposed. But the major
problem in static list scheduling is the inherent difficulty in
exact estimation of task cost and edge cost in a DAG and also
its inability to consider and manage with runtime behavior of
tasks. This underlines the need for dynamic scheduling of a
DAG. This paper presents how in general, dynamic scheduling
of a DAG can be done. Also proposes 4 simple methods to
perform dynamic scheduling of a DAG. These methods have
been simulated and experimented using a representative set
of DAG structured computations from both synthetic and real
problems. The proposed dynamic scheduler performance is
found to be in comparable with that of static scheduling
methods. The performance comparison of the proposed
dynamic scheduling methods is also carried out.
Optimized Assignment of Independent Task for Improving Resources Performance ...ijgca
Grid computing has emerged from category of distributed and parallel computing where the
heterogeneous resources from different network are used simultaneously to solve a particular problem that
need huge amount of resources. Potential of Grid computing depends on my issues such as security of
resources, heterogeneity of resources, fault tolerance & resource discovery and job scheduling. Scheduling
is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing
resources and is an NP-complete problem. To achieve the promising potential of grid computing, an
effective and efficient job scheduling algorithm is proposed, which will optimized two important criteria to
improve the performance of resources i.e. makespan time & resource utilization. With this, we have
classified various tasks scheduling heuristic in grid on the basis of their characteristics.
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.
This document evaluates the performance of a hybrid differential evolution-genetic algorithm (DE-GA) approach for load balancing in cloud computing. It first provides background on cloud computing and load balancing. It then describes the DE-GA approach, which uses differential evolution initially and switches to genetic algorithm if needed. The results show that the hybrid DE-GA approach improves performance over differential evolution and genetic algorithm alone, reducing makespan, average response time, and improving resource utilization. The study demonstrates the benefits of the hybrid evolutionary algorithm for an important problem in cloud computing.
Earlier stage for straggler detection and handling using combined CPU test an...IJECEIAES
This document summarizes a research paper that proposes a new framework called the combinatory late-machine (CLM) framework to facilitate early detection and handling of straggler tasks in MapReduce jobs. Straggler tasks significantly increase job execution time and energy consumption. The CLM framework combines CPU testing and the Longest Approximate Time to End (LATE) methodology to calculate a straggler tolerance threshold earlier. This allows for prompt mitigation actions. The paper reviews related work on straggler detection techniques and discusses the proposed methodology, which estimates task finish times based on progress scores. It aims to correlate straggler detection with system attributes like resource utilization that could cause delays.
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.
Energy efficient resource management for high-performance clustersXiao Qin
In the past decade, high-performance cluster computing platforms have been widely used to solve challenging and rigorous engineering tasks in industry and scientific applications. Due to extremely high energy cost,reducing energy consumption has become a major
concern in designing economical and environmentally friendly cluster computing
infrastructures for many high-performance applications. The primary focus of this talk is to illustrate how to improve energy efficiency of clusters and storage systems without significantly degrading performance. In this talk, we will first describe a general architecture
for building energy-efficient cluster computing platforms. Then, we will outline several energyefficient scheduling algorithms designed for high-performance clusters and large-scale storage systems. The experimental results using both synthetic and real world applications
show that energy dissipation in clusters can be reduced with a marginal degradation of system performance.
We looked at the data. Here’s a breakdown of some key statistics about the nation’s incoming presidents’ addresses, how long they spoke, how well, and more.
The document discusses how startup entrepreneurs think and operate. It notes that startups like Airbnb and Uber were started due to identifying shortages or problems. It emphasizes that startups focus on providing customer benefit, eliminating waste, and creating value. It also highlights that startups operate with speed, embracing failure fast and pivoting quickly, with transparency and by breaking rules. Startups succeed by moving rapidly, with minimal processes and instead prioritizing speed above all else.
This document discusses how emojis, emoticons, and text speak can be used to teach students. It provides background on the origins of emoticons in 1982 as ways to convey tone and feelings in text communications. It then suggests that with text speak and emojis, students can translate, decode, summarize, play with language, and add emotion to language. A number of websites and apps that can be used for emoji-related activities, lessons, and discussions are also listed.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
RSDC (Reliable Scheduling Distributed in Cloud Computing)IJCSEA Journal
This document summarizes the PPDD algorithm for scheduling divisible loads originating from multiple sites in distributed computing environments. The PPDD algorithm is a two-phase approach that first derives a near-optimal load distribution and then considers actual communication delays when transferring load fractions. It guarantees a near-optimal solution and improved performance over previous algorithms like RSA by avoiding unnecessary load transfers between processors.
A Novel Framework and Policies for On-line Block of Cores Allotment for Multi...ijcsa
Computer industry has widely accepted that future performance increases must largely come from increasing the number of processing cores on a die. This has led to NoC processors. Task scheduling is one of the most challenging problems facing parallel programmers today which is known to be NP-complete. A good principle is space-sharing of cores and to schedule multiple DAGs simultaneously on NoC processor. Hence the need to find optimal number of cores for a DAG for a particular scheduling method and further which region of cores on NoC, to be allotted for a DAG . In this work, a method is proposed to find near-optimal minimal block of cores for a DAG on a NoC processor. Further, a time efficient framework and three on-line block allotment policies to the submitted DAGs are experimented. The objectives of the policies, is to improve the NoC throughput. The policies are experimented on a simulator and found to deliver better performance than the policies found in literature..
An Improved Parallel Activity scheduling algorithm for large datasetsIJERA Editor
Parallel processing is capable of executing a large number of tasks on a multiprocessor at the same time period, and it is also one of the emerging concepts. Complex and computational problems can be resolved in an efficient way with the help of parallel processing. The parallel processing system can be divided into two categories depending on the nature of tasks such are homogenous parallel system and the heterogeneous parallel processing system. In the homogeneous environment, the number of processors required for executing different tasks is similar in capacity. In case of heterogeneous environments, tasks are allocated to various processors with different capacity and speed. The main objective of parallel processing is to optimize the execution speed and to shorten the duration of task execution with independent of environment. In this proposed work, an optimized parallel project selection method was implemented to find the optimal resource utilization and project scheduling. The execution speeds of the task increases and the overall average execution time of the task decreases by allocating different tasks to various processors with the task scheduling algorithm.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A novel methodology for task distributionijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
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.
This document discusses scheduling in cloud computing. It proposes a priority-based scheduling protocol to improve resource utilization, server performance, and minimize makespan. The protocol assigns priorities to jobs, allocates jobs to processors based on completion time, and processes jobs in parallel queues to efficiently schedule jobs in cloud computing. Future work includes analyzing time complexity and completion times through simulation to validate the protocol's efficiency.
A popular programming model for running data intensive applications on the cloud is map reduce. In
the Hadoop usually, jobs are scheduled in FIFO order by default. There are many map reduce
applications which require strict deadline. In Hadoop framework, scheduler wi t h deadline
con s t ra in t s has not been implemented. Existing schedulers d o not guarantee that the job will be
completed by a specific deadline. Some schedulers address the issue of deadlines but focus more on
improving s y s t em utilization. We have proposed an algorithm which facilitates the user to
specify a jobs deadline and evaluates whether the job can be finished before the deadline.
Scheduler with deadlines for Hadoop, which ensures that only jobs, whose deadlines can be met are
scheduled for execution. If the job submitted does not satisfy the specified deadline, physical or
virtual nodes can be added dynamically to complete the job within deadline[8].
Qo s aware scientific application scheduling algorithm in cloud environmentAlexander Decker
The document describes a QoS-aware scientific application scheduling algorithm for cloud environments. It proposes an algorithm that ranks tasks in a workflow and uses a user preference fitness function to select resources based on the user's desired quality of service, such as time and cost. The algorithm is compared to other similar works through several scenarios, and results show the proposed algorithm has better efficiency. Key aspects considered include task dependencies, data sizes, compute times, data transfer times, workflow makespan, resource costs and attributes.
Qo s aware scientific application scheduling algorithm in cloud environmentAlexander Decker
This document summarizes a research paper that proposes a scheduling algorithm for scientific applications in cloud environments. The algorithm aims to schedule tasks in workflows based on user preferences for quality of service (QoS), like time and cost. It ranks tasks and uses an UPFF function to select resources that meet the user's desired QoS. The algorithm is compared to other similar algorithms through scenarios, and results show it has better efficiency. The full paper provides more details on scientific workflows, cloud computing, related work on workflow scheduling algorithms, and defines the problem of scheduling tasks to resources while considering costs and times.
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.
LOAD BALANCING LARGE DATA SETS IN A HADOOP CLUSTERijdpsjournal
With the interconnection of one to many computers online, the data shared by users is multiplying daily. As
a result, the amount of data to be processed by dedicated servers rises very quickly. However, the
instantaneous increase in the volume of data to be processed by the server comes up against latency during
processing. This requires a model to manage the distribution of tasks across several machines. This article
presents a study of load balancing for large data sets on a cluster of Hadoop nodes. In this paper, we use
Mapreduce to implement parallel programming and Yarn to monitor task execution and submission in a
node cluster.
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.
Reconfiguration Strategies for Online Hardware Multitasking in Embedded SystemsCSEIJJournal
An intensive use of reconfigurable hardware is expected in future embedded systems. This means that the
system has to decide which tasks are more suitable for hardware execution. In order to make an efficient
use of the FPGA it is convenient to choose one that allows hardware multitasking, which is implemented by
using partial dynamic reconfiguration. One of the challenges for hardware multitasking in embedded
systems is the online management of the only reconfiguration port of present FPGA devices. This paper
presents different online reconfiguration scheduling strategies which assign the reconfiguration interface
resource using different criteria: workload distribution or task’ deadline. The online scheduling strategies
presented take efficient and fast decisions based on the information available at each moment. Experiments
have been made in order to analyze the performance and convenience of these reconfiguration strategies.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Task Scheduling using Hybrid Algorithm in Cloud Computing Environmentsiosrjce
The document summarizes a proposed hybrid task scheduling algorithm called PSOCS that combines particle swarm optimization (PSO) and cuckoo search (CS) for scheduling tasks in cloud computing environments. The PSOCS algorithm aims to minimize task completion time (makespan) and improve resource utilization. It was tested in a simulation using CloudSim and showed reductions in makespan and increases in utilization compared to PSO and random scheduling algorithms.
This document summarizes a research paper that proposes a hybrid task scheduling algorithm for cloud computing environments called PSOCS. PSOCS combines the Particle Swarm Optimization (PSO) algorithm and Cuckoo Search (CS) algorithm to optimize task scheduling and minimize completion time while increasing resource utilization. The paper describes PSO and CS algorithms individually, then defines the proposed PSOCS algorithm. It evaluates PSOCS using a simulation and finds it reduces makespan and increases utilization compared to PSO and random allocation algorithms.
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.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
2. 64 Computer Science & Information Technology (CS & IT)
distributed computing, powerful machines are now available at low prices. This is visible in large
spreading of cluster with hundreds of homogeneous/heterogeneous processors connected by high
speed interconnection network [2]. This democratization of cluster calls for new practical
administration tools.
The task scheduling problem is to allocate resources (processors) to the tasks and to establish an
order for the tasks to be executed by resources. There are two different types of task scheduling:
static and dynamic. Static strategies define a schedule at compile time based on estimated time
required to execute tasks and to communicate data. Static schedule can be generated only when
the application behaviour is fully deterministic and this has the advantage of being more efficient
and a small overhead during runtime. The full global knowledge of application in the form of
DAG will help to generate a better schedule. Dynamic strategies, on the other hand are applied
when tasks are generated during runtime. Tasks are assumed to be non-preemptive.
Workflows have recently emerged as a paradigm for representing complex scientific
computations [26]. Few widely used example workflows are Montage (Fig. 2), cybershake,
LIGO, SIPHT. Workflows represented by one of many workflow programming languages can be
translated into DAG, in general. Thus workflow scheduling is essentially a problem of scheduling
DAG. Although much work has been done in scheduling single workflow [3], multiple workflow
scheduling is not receiving deserved attention. Few initial studies are found in the literature [4, 5].
Because of huge computing power of a cluster and the inability of a single DAG to utilize all
processors on cluster, multiple DAG applications need to be executed concurrently. Thus a
scheduler to deal with multi-user jobs with the objectives of maximizing resource utilization and
minimizing overall DAG completion time is essential. The contributions of this paper are 1) a
new method to find minimum, optimal and maximum number of processors that can be allotted
for a DAG and this information is used to find one best way to share available processors among
multiple DAGs 2) a framework to receive submission of DAGs, find the allotment for each
submitted DAG and schedule tasks on allotted processors, with the objectives of maximizing
resource utilization and minimizing overall completion time.
1.1. Application Model
The data flow model is gaining popularity as a programming paradigm for parallel processors.
When the characteristics of an application is fully deterministic, including task's execution time,
size of data communication between tasks, and task dependencies, the application can be
represented by directed acyclic graph (DAG) as shown in Fig.1. Each node in DAG represents a
task to be performed and the edges indicate inter-task dependencies. Node weight stands for the
computation cost of the corresponding task and the edge cost represents the volume of data to be
communicated between the corresponding nodes. The node and edge weights are usually
obtained by estimation or profiling. Communication-to-Computation (CCR) is the ratio of
average communication cost to the average computation cost of a DAG. This characterizes the
nature of DAG. The objective of scheduling is to map tasks onto processors and order their
execution so that task dependencies are satisfied and minimum overall completion time is
achieved. Makespan is the total time required to complete a DAG.
1.2. Platform
A cluster with ‘P’ homogeneous processors, each of which is a schedulable resource is
considered. Processors are interconnected by a high speed and low latency network. A processor
can communicate with several other processors simultaneously with multi-port model.
3. Computer Science & Information Technology (CS & IT) 65
Figure 1. A Typical DAG Figure 2. Montage – a Workflow
2. RELATED WORK
Extensive work has been done on scheduling a single DAG [6, 7, 8]. Zhao et al.[4] have proposed
few methods to schedule multiple DAGs. One approach is to combine several DAGs into one by
making the entry nodes of all DAGs, immediate successors of new entry node and then use
standard methods to schedule the combined DAG. Another way is to consider tasks from each
DAG in round robin manner for scheduling. They have proposed other policies to optimize both
makespan and fairness. The key idea is to evaluate, after scheduling a task, the slowdown value of
each DAG against other DAGs and make a decision on which DAG must be considered next for
scheduling.
A list scheduling method to schedule multi-user jobs is developed by Barbosa et al. [9] with an
aim to maximize the resource usage by allowing a floating mapping of processors to a given job,
instead of the common mapping approach that assigns a fixed set of processors to a user job for a
period of time. A master DAG where each node is a user job and each edge representing a priority
of one job over another is constructed using all submitted DAGs. A list scheduling algorithm [6]
is used to schedule all tasks of Master DAG. The master DAG is created based on job priorities
and deadlines
Bittencourt et al. [10] have used Path Clustering Heuristic (PCH) to cluster tasks and the entire
cluster is assigned to a single machine. They have proposed four heuristics which differ in the
order tasks of multiple DAGs are considered for scheduling. The methods are sequential, Gap
search method, Interleave algorithm and Group DAGs method. A meta-scheduler for multiple
DAGs [11] merges multiple DAGs into one to improve the overall parallelism and optimize idle
time of resources. The efforts are limited to the static case and they do not deal with dynamic
workloads.
Duan et al. [12] have proposed a scheduling algorithm based on the adoption of game theory and
idea of sequential cooperative game. They provide two novel algorithms to schedule multiple
DAGs which work properly for applications that can be formulated as a typical solvable game.
Zhifeng et al. [13] addresses the problem of dynamic scheduling multiple DAGs from different
4. 66 Computer Science & Information Technology (CS & IT)
users. They expose a similar approach from Zhao et al. [4] without merging DAGs. Their
algorithm is similar to G-heft algorithm.
An application which can exploit both task and data parallelism can be structured as Parallel Data
Graph (PTG) in which task can be either sequential or data parallel. Data parallelism means
parallel execution of the same code segment but on different sections of data, distributed over
several processors in a network. A DAG is a special case of PTG where task can only be
sequential task. Thus PTG scheduling is quite similar to DAG scheduling. Not much work is
carried out in Multiple PTG scheduling. Tapke et al. [5] have proposed an approach where each
PTG is given a maximum constraint on number of processors it can use and tasks are scheduled
using a known PTG scheduling algorithm. The size of each processor subset is determined
statically according to various criteria pertaining to the characteristics of PTG like maximum
width, total absolute work to be done and proportional work to be carried out.
Sueter et al. [14] have focused on developing strategies that provide a fair distribution of
resources among Parallel Task Graphs (PTG), with the objectives of achieving fairness and
makespan minimization. Constraints are defined according to four general resource sharing
policies: unbounded Share(S), Equal Share (ES), Proportional Share (PS), and Weighted
Proportional Share (WPS). S policy uses all available resources. ES policy uses equal resources
for each PTG. PS and WPS use resources proportional to the work of each PTG, where the work
is considered as critical path cost by width of PTG.
A study of algorithms to schedule multiple PTGs on a single homogeneous cluster is carried out
by Casanova et al. [15]. Therein it is shown that best algorithms in terms of performance and
fairness all use the same principle of allocating a subset of processors to each PTG and that this
subset remains fixed throughout the execution of the whole batch of PTGs. The basic idea in job
schedulers [19] is to queue jobs and to schedule them one after the other using some simple rules
like FCFS (First Come First Served) with priorities. Jackson et al. [20] extended this model with
additional features like fairness and backfilling.
Online scheduling of multiple DAGs is addressed in [16]. Authors have proposed two strategies
based on aggregating DAGs into a single DAG. A modified FCFS and Service-On-Time (SOT)
scheduling are applied. FCFS appends arriving DAGs to an exit node of the single DAG, while
SOT appends arriving DAGs to a task whose predecessors have not completed execution. Once
the single DAG has been built, scheduling is carried out by HEFT.
An Online Workflow Management (OWM) strategy [18] for scheduling multiple mix-parallel
workflows is proposed. DAG tasks are labelled, sorted, and stored into independent buffers.
Labelling is based on the upward rank strategy. The sorting arranges tasks in descendent order of
the task rank. Task scheduling referred to as a rank hybrid phase determines the task execution
order. Tasks are sorted in descending order when all tasks in the queue belong to the same
workflow. Otherwise, they are sorted in ascending order. Allocation assigns idle processors to
tasks from the waiting queue.
Raphael et al. [23] have addressed online scheduling of several applications modelled as work-
flows. They have extended a well-known list scheduling heuristic (HEFT) and adapted it to the
multi-workflow context. Six different heuristics based on HEFT key ideas are proposed. These
heuristics have been designed to improve the slowdown of different applications sent from
multiple users.
Much work has not been done on scheduling multiple DAG applications. A common approach is
to schedule a single DAG on fixed number of processors [6] but methods to find the number of
processors to be used for a DAG, are not found in literature. Tapke et al. [5] have proposed
5. Computer Science & Information Technology (CS & IT) 67
methods to find maximum resource constraint for each PTG, while scheduling multiple PTGs.
But they have not restricted scheduling of a PTG to the fixed set of processors. A method
proposed by Barbosa et al. allows floating number of processors to a given job, instead of fixed
number of processors. Many existing workflow scheduling methods do not use fixed set of
processors for each DAG. Instead a task based on some heuristic is picked among tasks of all
DAGs and is scheduled on a processor where it can start earliest based on some heuristic. The
work proposed in this paper is similar to Barbosa [9] method, in the sense that a fixed set of
processor is allocated for each DAG which later can be varied during runtime with the objective
of maximizing resource usage. Their work does not address several issues like - how initial
processor allotment for each DAG is made, a method to decide number of DAGs to be scheduled
concurrently among several submitted DAGs, to deal with online submission of DAGs. This work
addresses all the above mentioned issues.
3. PROCESSOR ALLOTMENT FOR A DAG
A schedule for a DAG can be obtained with varied number of processors. By increasing the
number of processors allotted for a DAG, its makespan decreases. The gain in terms of reduction
in makespan, reduces as more number of processors are allotted to a DAG. This is due to
communication overhead and limited parallelism present in DAG. The optimal and maximum
number of processors for a DAG will help in finding processor allotment while scheduling
multiple DAGs concurrently on a cluster.
3.1. Maximum Number of Processor for a DAG
The maximum number of processors a DAG can utilize depends on its nature and degree of
parallelism present in it. The number of allotted processors, beyond which DAG’s makespan
does not decrease with any more additional processors, is the maximum number of processors
that can be utilized by a DAG. A brute force method can be used to find this, by making several
calls to scheduling method and recording the makespan for each case. But an efficient binary
search based method [24] is used in this work and its time complexity is O(log n) against O(n) of
the brute force method.
3.2. Optimal Number of Processor for a DAG
Optimal number of processors for a DAG is that number up to which every added processor is
utilized well and beyond it, they are underutilized. With increase in number of allotted
processors, DAG’s makespan decreases and average processor utilization decreases due to
communication overhead and limited parallelism. Average processor utilization can best be
measured using computing area, which is the product of makespan and the number of processors
used. In this work, computing area is used to find the optimal number of processors for a DAG.
As processor allotment to a DAG is increased, makespan decreases and computing area increases.
Initially decrease in makespan is more than increase in computing area, justifying the worthiness
of increase in processor allotment. After the processor allotment reaches a certain value, the
increase in computing area is more than the decrease in makespan for every added processor,
indicating that any further increase in processors allotment is not of significant use.
By successively increasing number of processors allotted for a DAG, makespan and computing
area are recorded. The number of processors for which decrease in makespan becomes less than
the increase in computing area, fixes the optimal number of processors for a DAG.
.
6. 68 Computer Science & Information Technology (CS & IT)
4. MULTIPLE DAGS SHARING CLUSTER
It is advantageous to schedule multiple DAGs simultaneously on a cluster instead of dedicating
the entire cluster to a single DAG, due to communication overhead. Furthermore, it is beneficial
to schedule more number of DAGs each with relatively less number of processors than
scheduling less number of DAGs each with large number of processors, because of
communication overhead. The returns, in terms of decrease in makespan, for each additional
processor differs for each DAG depending on its nature and number of processors already allotted
to it. Hence additional processors must be allotted to those DAGs which will be benefitted most
by means of reduction in makespan. To find the most benefitting DAGs, reduction in makespan
for the next added processor must be known for each DAG. Reduction in makespan for every
added processor can be best captured as an equation using regression analysis and are provided to
scheduler along with DAG. During regression analysis of large number of DAGs, it is observed
that for any DAG, makespan reduction follows either exponential or power curve. Thus for each
DAG, makespan reduction for each added processor is recorded and curve fitting is done. The
type of equation and its constants are stored along with each DAG, which then is used by the
scheduler while finding processor allotment for each DAG, while scheduling multiple DAGs. The
scheduler is invoked when a DAG arrives or a DAG completes execution. The minimum number
of processors to be allotted for each DAG is assumed to be four, by conducting the experiments
large number of times. The algorithm for the proposed scheduler is given below.
Algorithm multi_dag_scheduler()
// information submitted along each DAG – opt_proc, max_proc, eqn_type, eqn_const
//avail_proc – currently available number of free processors
// let min_core = 4
Input : submitted DAGs
Output : processor allotment and calling an instance of scheduler for each DAG
Step 1: if (arrival) then // DAG has arrived
Step 2: if (avail_proc < min_core ) then
Step 3: append to waiting queue
Step 4: else
Step 5 : allot (min_proc or max_proc or opt_proc) whichever best fits avail_proc
Step 6 : create an instance of scheduler for a DAG on allotted processors
Step 7 : endif
Step 8: else //DAG has completed
Step 9: if ( waiting queue is not empty) then
Step 10 : do_allot()
Step 11 : endif
Step 12 : end_algorithm
Algorithm do_allot()
Step 1: remove those many number of DAGs from queue beginning, whose sum of their
min_proc is less than avail_proc
Step 2 : if (sum of opt_proc of all removed DAGs is less than available processors) then
Step 3 : allot opt_proc to each removed DAG
Step 4 : else
Step 5 : for each removed DAG allot their min_proc number of processors
Step 6 : endif
7. Computer Science & Information Technology (CS & IT) 69
Step 7 : if (free processors are left) then
Step 8 : distribute those free processors among DAGs, in such a way that each processor is
added to that DAG for which it yields maximum reduction in makespan, using
equations types and their constants
Step 9 : endif
Step 10 : end_algorithm
5. EXPERIMENTAL SETUP AND RESULTS
5.1. Experiment Setup
A discrete-event based simulator is developed to simulate the arrival, scheduling, execution and
completion of DAGs. Simulation allows performing statistically significant number of
experiments for a wide range of application configurations. Poisson distribution is used to
simulate the arrival time of DAGs. Several kinds of benchmark DAGs from several sources are
used to experiment the proposed scheduler for different types of DAGs. Series-parallel DAGs
from Task Graphs For Free [22], random DAGs from Standard Task Graph Set [21], DAGs of
linear algebra applications like FFT, LU decomposition, Gauss-elimination, Laplace transform
and workflows like LIGO, cybershake, Montage, SIPHT[25]. DAGs with CCR values of 0.1,
0.4, 1 and 5 are used in experiments.
5.2. Results and Analysis
5.2.1. Optimal and Maximum Number of Processors for a DAG
An efficient binary search based method [24] with time complexity of O(log(n)) is used to find
the maximum number of processors a DAG can utilize. The decrease in makespan and increase in
computing area (decrease in average processor utilization) for every added processor is used to
fix the optimal number of processors for a DAG. The plot of decrease in makespan and increase
in computing area for different number of processors, for a DAG is given in Fig.3. The crossover
point gives the optimal number of processors for that DAG. The method can be used for any kind
of DAG.
Figure 3. To find Optimal Number of Processors for a DAG
8. 70 Computer Science & Information Technology (CS & IT)
5.2.1. Multiple DAGs Scheduling
Recent works on multiple DAG scheduling [4, 5, 9, 10, 11, 12, 14] have not considered allotment
of fixed set of processors to a DAG. Instead, tasks from all DAGs are scheduled on any processor
on which they can start earliest, using some heuristic. Hence initially, it is proved experimentally
that space partitioning of processors among multiple DAGs, delivers improvement in
performance compared to combined DAGs scheduling. To experiment this, a set of DAGs of all
kinds, were scheduled on a cluster with 100 numbers of processors. The metric used is the sum of
computing area of all scheduled DAGs. To study the effect on both computation intensive and
communication intensive applications, DAGs with both low and high CCR are considered. Two
sets of DAGs each with 8 and 16 number of DAGs, under each category are considered. Thus the
four categories of DAGs are labelled as ccrl_8, ccrl_16, ccrh_8 and ccrh_16. Since the behaviour
depends on the nature of DAG, 50 sets of DAGs are considered for each category. Care is taken
to consider all different types of DAGs in the sets of DAGs. The results obtained from 50 sets are
averaged and the same is shown in Fig. 4. The performance of the proposed method is better than
combined DAGs scheduling for all four categories of DAGs. For the category ccrh_8, proposed
method shows maximum improvement of 12%, since DAGs are communication intensive and
thus scheduling tasks on fixed set of processors reduces time to complete the DAG. Performance
improvement is only 9% for the category ccrh_18, as there is less scope for further improvement
due to large number of DAGs being scheduled together.
Figure 4. Combined DAGs scheduling vs Proposed Space-sharing Schedule
The benefits of space partitioning processors which cannot be measured for DAGs with dummy
tasks are 1) as tasks of a DAG are scheduled on the same set of processors, they will be benefitted
from cache-warm and secondary memory warm. 2) an online scheduler can be used for each
DAG, after allotting a set of processors to it. 3) processor allotment for a DAG can be varied
depending on availability of processor, with the objectives of maximizing resource utilization.
A highlight of this work is to find one best way to share available processors among multiple
DAGs, using regression analysis. The proposed work is compared against policies proposed by
Tapke et al. [14] - unbounded Share(S), Equal Share (ES), Proportional Share (PS), and Weighted
Proportional Share (WPS). The strategy S which is a selfish allocations and tasks of different
DAGs are not differentiated is used as a baseline performer for other strategies as it gives an
9. Computer Science & Information Technology (CS & IT) 71
indication of performance of heuristics originally designed for single DAG. Values obtained are
normalized with the value of S strategy, to help in comparison. Performance metric used is
average makespan and resource utilization which is measured as the sum of computing area of all
DAGs scheduled together. Five categories of DAGs each with 4, 8, 12, 16 and 20 number of
DAGs are considered. Random, series-parallel, linear algebra DAGs and various workflows like
montage, SIPHT, epigenemics, LIGO are considered. 100 sets of DAGs are considered for each
category and the results obtained are averaged. The result is shown in Fig. 5 and Fig. 6. The
proposed method is better than all policies found in literature.
For less number of DAGs, performance of all methods is almost the same, as there will not be
much conflict for resources. With more number of DAGs, resource conflicts increase and the
proposed method shows considerable good performance over previous methods.
Figure 5. Normalized Average Makespan of Set of DAGs
Figure 6. Normalized Sum of Computing Area of Set of DAGs
10. 72 Computer Science & Information Technology (CS & IT)
6. CONCLUSIONS
Multiple DAGs scheduling on a cluster is not receiving the deserved attention. Few methods
found in literature performing combined DAGs scheduling. But in this work, it is proposed to
allot a fixed number of processors to each DAG and an instance of local DAG scheduler to
schedule DAG’s tasks only on the allotted fixed set of processors. A method to find the maximum
and optimal number of processors that can be allotted to a DAG is given, which will be used to
find the processor allotment for each DAG while scheduling multiple DAGs. A new framework
to schedule multiple DAGs with the objectives of maximizing resource utilization and
minimizing DAGs completion time is proposed. Regression analysis is used to find the number of
processors to be allotted to each DAG while scheduling multiple DAGs. This method is proved to
outperform other methods found in literature by around 10-15%.
The other big advantage of the proposed approach is that instead of static schedule, an online
scheduler for each DAG can be used to schedule tasks, as they are generated, onto the allotted
processor. An Online scheduler overcomes drawbacks of static schedule and is more
advantageous. Also static DAG information can be used during online scheduling to further
improve performance. Because of space sharing of processors, the number of processors allotted
to each DAG can be varied during runtime, depending on the availability of free processors. This
will improve resource utilization, hence performance of the scheduler. In future work, the idea of
online scheduler and varied processor allotment for each DAG will be experimented.
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AUTHORS
C R Venugopal received his Ph. D from IIT, Bombay. Presently serving as a Professor
in Sri Jayachamarajendra College of Engineering, Mysore, India. His main research
interests are Cloud computing, High Performance computing, VLSI Technology and
File System development. Has authored more than 50 international conference and
journal papers.
Uma B completed M. Tech. in Computer Science and Engineering from IIT, Delhi.
Currently working as a Associate Professor in Malnad College of engineering, Hassan,
India. Her research interests are Parallel Programming, High Performance Computing
and Task scheduling.