Abstract
Cloud computing technology refers to on-demand access to services, applications, and infrastructure that runs on a distributed network utilizing virtualized resources. In the cloud model, an efficient task scheduling algorithm plays an important role in order to achieve better functioning in general and resource utilization of the cloud. The end-users submit tasks, and the scheduling algorithm needs to allocate them to the available resources on time. Task scheduling issue is considered as NP-hard problems, and metaheuristics algorithms demonstrate high efficiency in solving such problems, thus, in this work, we propose enhanced flower pollination algorithm for the task scheduling. The major focus of this study is to reduce the makespan. We compared the results of the proposed method to other similar approaches, such as PBACO, ACO, Min-Min, and FCFS allocation strategies. The obtained results from the experiment show that the proposed EEFPA scheduler has the potential to allocate submitted tasks by the user to the available resources on the cloud.
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Bezdan, T., Zivkovic, M., Antonijevic, M., Zivkovic, T., Bacanin, N. (2021). Enhanced Flower Pollination Algorithm for Task Scheduling in Cloud Computing Environment. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_16
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DOI: https://doi.org/10.1007/978-981-15-7106-0_16
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