Abstract
Workflow scheduling in clouds refers to mapping workflow tasks to the cloud resources to optimize some objective function. Workflow scheduling is a crucial component behind the process for optimal workflow enactment. It is a well-known NP-hard problem and is more challenging in the heterogeneous computing environment. Cloud environments confront several issues, including energy consumption, implementation time, emissions of heat and CO\(_2\) and running costs. The increasing complexity of the workflow applications forces researchers to explore hybrid approaches to solve the workflow scheduling problem. Efficient and effective cloud workflow planning is one of the most important approaches to address the above difficulties and make optimal use of resources. This study suggests energy awareness, based on the methodology whale optimization algorithm (WOA). Our objective is to decrease the energy consumption and maximize the throughput of computational workflows which impose a considerable loss on the quality of service guarantee (QoS). The proposed method is compared with other standard state-of-the-art techniques to analyze its performance.
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Sobhanayak, S., Mendes, I.K.A., Jaiswal, K. (2022). Bi-objective Task Scheduling in Cloud Data Center Using Whale Optimization Algorithm. In: Verma, P., Charan, C., Fernando, X., Ganesan, S. (eds) Advances in Data Computing, Communication and Security. Lecture Notes on Data Engineering and Communications Technologies, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-16-8403-6_31
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DOI: https://doi.org/10.1007/978-981-16-8403-6_31
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