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.

Voltage scaling is a fundamental technique in the energy efficient computing field. Recent studies tackling this topic show degraded system reliability as frequency scales. To address this conflict, the subject of reliability aware power management (RAPM) has been extensively explored and is still under investigation. Heterogeneous Computing Systems (HCS) provide high performance potential which attracts researchers to consider these systems. Unfortunately, the existing scheduling algorithms for precedence constrained tasks with shared deadline in HCS do not adequately consider reliability conservation. In this study, we design joint optimization schemes of energy efficiecy and system reliability for directed acyclic graph (DAG) by adopting the shared recovery technique, which can achieve high system reliability and noticeable energy preservation. To the best of our knowledge, this is the first time to address the problem in HCS. The extensive comparative evaluation studies for both randomly generated and some real-world applications graphs show that our scheduling algorithms are compelling in terms of enhancement of both system reliability and energy saving.