Abstract
Cloud computing has become an important alternative for solving large scale resource- intensive problems in science, engineering, and analytics. Resource management play an important role in improving the quality of service (QoS). This paper is concerned with the investigation of scheduling strategies for divisible loads with deadlines constraints upon heterogeneous processors in a cloud computing environment. The workload allocation approach presents in this paper is using Divisible Load Theory (DLT). It is based on the fact that the computation can be partitioned into some arbitrary sizes and each partition can be processed independently of each other. Through series of simulation against the baseline strategies, it can be found that the worker selection order in the service pool and the amount of fraction load assigned to each of them have significant effects on the total computation cost.
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