MaxStd: A Task Scheduling Heuristic for Heterogeneous Computing Environment

Munir, Ehsan Ullah; Li, Jianzhong; Shi, Shengfei; Zou, Zhaonian; Yang, Di

doi:10.3923/itj.2008.679.683

Cited by 20 publications

(10 citation statements)

References 4 publications

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“…Hence the task having high standard deviation is assigned to the machine which has minimum completion time [7]. Figure 1 illustrates MaxStd heuristic.…”

Section: Maximum Standard Deviation (Maxstd)mentioning

confidence: 99%

An improved ant algorithm for grid scheduling problem

Bagherzadeh

MadadyarAdeh

2009

2009 14th International CSI Computer Conference

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Grid computing is a promising technology for future computing platforms and is expected to provide easier access to remote computational resources that are usually locally limited. Scheduling is one of the active research topics in grid environments. The goal of grid task scheduling is to achieve high system throughput and to allocate various computing resources to applications. The Complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. Many different methods have been proposed to solve this problem. Some of these methods are based on heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper we introduce a new task scheduling algorithm based on Ant Colony Optimization (ACO). According to the experimental results, the proposed algorithm confidently demonstrates its competitiveness with previously proposed algorithms.

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“…Hence the task having high standard deviation is assigned to the machine which has minimum completion time [7]. Figure 1 illustrates MaxStd heuristic.…”

Section: Maximum Standard Deviation (Maxstd)mentioning

confidence: 99%

An improved ant algorithm for grid scheduling problem

Bagherzadeh

MadadyarAdeh

2009

2009 14th International CSI Computer Conference

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“…The task having low standard deviation of task execution has less variation in execution time on different machines and hence, its delayed assignment for scheduling will not affect overall makespan much. Hence the task having high standard deviation is assigned to the machine which has minimum completion time [14].…”

Section: Maximum Standard Deviationmentioning

confidence: 99%

A New Heuristic Approach for Scheduling Independent Tasks on Heterogeneous Computing Systems

Rafsanjani¹,

Bardsiri²

2012

IJMLC

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Abstract-Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous computing systems. The problem of mapping meta-tasks to a machine is shown to be NP-complete. The NP-complete problem can be solved only using heuristic approach. There are a number of heuristic algorithms that were tailored to deal with scheduling of independent tasks. Different criteria can be used for evaluating the efficiency of scheduling algorithms. The most important of them are makespan, flowtime and resource utilization. In this paper, a new heuristic algorithm for scheduling meta-tasks in heterogeneous computing system is presented. The proposed algorithm improves the performance in both makespan and effective utilization of resources by reducing the idle time of the machine. The performance analyses show that the proposed algorithm has a better resource utilization rate and reduced makespan than the other known algorithms.

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“…As a well-abstracted model, optimal scheduling of tasks for multiple processors is a NP-complete problem [7], and thus many heuristics and meta-heuristics are proposed in literatures. Heuristics are directly designed for tasks scheduling, such as MinMin [8], Sufferage [9] and MaxStd [10], while meta-heuristics are combinatorial optimization techniques used indirectly for task scheduling, and the representative meta-heuristics include Genetic Algorithm (GA) [8], Simulated Annealing (SA) [8], Particle Swarm Optimization (PSO) [11] and Chemical Reaction Optimization (CRO) [6]. These algorithms work at task level, and require the prediction of each task's execution time on each machine, forming an ETC (Expected Time to Compute) matrix [8].Though both heuristics and meta-heuristics are classic solutions to traditional HTC (High Throughput Computing) [1], they are not suitable for MTC.…”

Section: Related Workmentioning

confidence: 99%

An Application-Level Scheduling with Task Bundling Approach for Many-Task Computing in Heterogeneous Environments

Xiao

Zhang

Chen

et al. 2012

Lecture Notes in Computer Science

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Many-Task Computing (MTC) is a widely used computing paradigm for large-scale task-parallel processing. One of the key issues in MTC is to schedule a large number of independent tasks onto heterogeneous resources. Traditional task-level scheduling heuristics, like Min-Min, Sufferage and MaxStd, cannot readily be applied in this scenario. As most of MTC tasks are usually fine-grained, the resource management overhead would be prominent and the multi-core nodes might become hard to be fully utilized. In this paper we propose an application-level scheduling with task bundling approach that utilizes the knowledge of both applications and tasks to overcome these difficulties. Furthermore we adapt the traditional task-level heuristics to our model for MTC scheduling. Experimental results show that these application-level scheduling approaches, when equipped with task bundling, can deliver good performance for Many-Task Computing in terms of both Makespan and Flowtime.

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MaxStd: A Task Scheduling Heuristic for Heterogeneous Computing Environment

Cited by 20 publications

References 4 publications

An improved ant algorithm for grid scheduling problem

An improved ant algorithm for grid scheduling problem

A New Heuristic Approach for Scheduling Independent Tasks on Heterogeneous Computing Systems

An Application-Level Scheduling with Task Bundling Approach for Many-Task Computing in Heterogeneous Environments

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