Artificial Flora Optimization Algorithm for Task Scheduling in Cloud Computing Environment
Cloud computing is a relatively new computing paradigm that enables provision of storage and computing resources over a network to end-users. Task scheduling represents the allocation of tasks to be executed to the available resources. In this paper, we propose a scheduling algorithm, named artificial flora scheduler, with an aim to improve task scheduling in the cloud computing environments. The artificial flora belongs to the category of swarm intelligence metaheuristics that have proved to be very effective in solving NP hard problems, such as task scheduling. Based on the obtained simulation results and comparison with other approaches from literature, a conclusion is that the proposed scheduler efficiently optimizes execution of the submitted tasks to the cloud system, by reducing the makespan and the execution costs.
KeywordsTask scheduling Makespan Cloud computing Artificial flora Swarm intelligence Optimization
This work was supported by the Ministry of Education and Science of Republic of Serbia, Grant No. III-44006.
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