A Hybrid Chemical Reaction Optimization Scheme for Task Scheduling on Heterogeneous Computing Systems

Abstract

Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. Scheduling for directed acyclic graph (DAG) tasks with the objective of minimising makespan has become an important problem in a variety of applications on heterogeneous computing platforms, which involves making decisions about the execution order of tasks and task-to-processor mapping. Recently, the chemical reaction optimization (CRO) method has proved to be very effective in many fields. In this paper, an improved hybrid version of the CRO method called HCRO (hybrid CRO) is developed for solving the DAG-based task scheduling problem. In HCRO, the CRO method is integrated with the novel heuristic approaches and a new selection strategy proposed. More Specifically, we make the following contributions. (1) A Gaussian random walk approach is proposed to search for optimal local candidate solution. (2) A left or right rotating shift method based on the theory of maximum Hamming distance is used to guarantee that our HCRO algorithm can escape from local optima. (3) A novel selection strategy based on the normal distribution and a pseudo-random shuffle approach are developed to keep the molecular diversity. Moreover, an exclusive-OR (XOR) operator between two strings is introduced to reduce the chance of cloning before new molecules are generated. Both simulation and real-life experiments have been conducted in this paper to verify the effectiveness of HCRO. The results show that the HCRO algorithm schedules the DAG tasks much better than the existing algorithms in terms of makespan and speed of convergence.

Task Scheduling Techniques for Asymmetric Multi-Core Systems

“…Genetic algorithms [26] group tasks into generations and schedule them according to a randomized genetic technique. Chemical reaction algorithms [27], [28] mimic molecular interactions to map tasks to processors. Some of these guidedrandom approaches are designed for heterogeneous systems [26], [27].…”

A Static Packet Scheduling Approach for Fast Collective Communication by Using PSO

“…As recent reports, Guo et al show fault-tolerant task allocation by using PSO method [17]. Xu et al present application of the chemical reaction optimization (CRO) method to the task allocation problems in directed asynchronous graphs (DAGs) [18]. From a packet communication point of view, these task allocation approaches stand on spacial arrangement of packet communication routes.…”

Maximally Distant Codes Allocation Using Chemical Reaction Optimization with Enhanced Exploration

“…Another hybrid was built on a local search to solve the Travelling Salesman problem (TSP) as in [21], by integrating the Lin-Kernighan (LK) local search, claiming better tradeoff between the exploration abilities of CRO and the exploitation abilities of LK local searcher, resulting in more efficient algorithm. The hybrid reported in [22], called Hybrid CRO (HCRO), was developed to solve task scheduling problems, by integrating a selection strategy with standard CRO. Both simulation and real-life experiments proved that the HCRO algorithm task scheduling was much better than the existing algorithms in terms of makespan and speed of convergence.…”