Completion time driven hyper-heuristic approach for optimisation of scientific workflow scheduling in cloud environment / Ehab Nabiel Mohammad

Ehab Nabiel , Mohammad (2018) Completion time driven hyper-heuristic approach for optimisation of scientific workflow scheduling in cloud environment / Ehab Nabiel Mohammad. PhD thesis, University of Malaya.

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Abstract

Effective management of Scientific Workflow Scheduling (SWFS) processes in a cloud environment remains a challenging task when dealing with large and complex Scientific Workflow Applications (SWFAs). The cost optimisation of SWFS approaches is affected by the inherent nature of SWFA as well as various types of scenarios that depend on the number of available virtual machines and size of SWFA datasets. However, current meta-heuristic based SWFS approaches lack the provision of satisfactory optimal solution, considering limited computational resources (e.g., virtual machines), longer execution time and high computational cost for a complex SWFA. Thus, the main objective of this research is to propose a Completion Time Driven Hyper-Heuristic (CTDHH) approach for cost optimisation of SWFS in a cloud environment. The first stage (i.e. formulation stage) of the research methodology involves an in-depth analysis of different cost optimisation perspectives of SWFS including aspects, parameters, challenges and approaches. The second stage (i.e. approach development stage) is the development of the proposed CTDHH approach, which includes two main parts, the cost optimisation model of SWFS and the dynamic hyper-heuristic algorithm. The proposed approach enhances the native random selection way of existing hyper-heuristic approaches by incorporating the best computed workflow completion time to pick a suitable algorithm from the pool of lowlevel heuristic algorithms after each run. The third and last stage (i.e. evaluation and analysis stage) aims at evaluating the proposed approach by considering two different experimental cloud environments: simulation-based environment and real-world based environment. The performance of the proposed approach is evaluated by comparing it with four population-based approaches and an existing hyper-heuristic approach named Hyper-Heuristic Scheduling Algorithm (HHSA). Based on the results of the experiments, the proposed approach has proven to yield the most effective performanc

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