symbiotic organisms search algorithm for resource discovery in cloud computing environment

What is it about?

The dynamicity, coupled with the uncertainty that occurs between advertised resources and users’ resource requirement queries, remains significant problems that hamper the discovery of candidate resources in a cloud computing environment. Network size and complexity continue to increase dynamically which makes resource discovery a complex, NP-hard problem that requires efficient algorithms for optimum resource discovery. Several algorithms have been proposed in literature but there is still room for more efficient algorithms especially as the size of the resources increases. This paper proposes a soft-set symbiotic organisms search (SSSOS) algorithm, a new hybrid resource discovery solution. Softset theory has been proved efficient for tackling uncertainty problems that arises in static systems while symbiotic organisms search (SOS) has shown strength for tackling dynamic relationships that occur in dynamic environments in search of optimal solutions among objects. The SSSOS algorithm innovatively combines the strengths of the underlying techniques to provide efficient management of tasks that need to be accomplished during resource discovery in the cloud. The effectiveness and efficiency of the proposed hybrid algorithm is demonstrated through empirical simulation study and benchmarking against recent techniques in literature. Results obtained reveal the promising potential of the proposed SSSOS algorithm for resource discovery in a cloud environment.

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Why is it important?

The technical contributions of this paper are summarized as follows: • We propose a soft set model for tackling uncertainty properties in cloud resource parameters. • Wealso present a method of searching, matching and selecting candidate resources for executing cloud users’ jobs. • The paper also presents a hybrid resource discovery framework for cloud job scheduling. • We further present an evaluation of the proposed framework in comparison with other related state-of-the-art resource discovery methods.

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