RWWO: an effective strategy for workflow scheduling in cloud computing with predicted energy using Deep Maxout Network

Abstract

Workflow scheduling is the recent researching area in the cloud environment, in which user satisfaction based on the cost and bandwidth is the most challenging task. Several research methods are devised to minimize the execution time and cost, which compromises the attributes. Hence, this research introduces an effective task scheduling mechanism in a cloud environment utilizing the Regressive Whale Water Optimization (RWWO) algorithm, which is derived by the integration of Regressive Whale Optimization (RWO) and Water Cycle Algorithm (WCA). The fitness parameters utilized are Quality of Service (QoS), resource utilization, and predicted energy. However, predicted energy is determined using Deep Maxout Network. Moreover, the proposed RWWO + Deep Maxout Network achieved a minimum task scheduling time of 0.0208, minimum task scheduling cost of 0.0017, minimum predicted energy of 0.1971, and maximum resource utilization of 0.9999.

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