Independent Task Scheduling based on Meta-Heuristic Algorithm in Cloud Computing

Abstract

Task scheduling is a very important topic in the context of cloud computing because it affects the quality of the offered cloud computing services. In the literature, many strategies have been presented for task scheduling handling, with the focus of the available algorithms on reducing execution time while ignoring other quality of service (QoS). This paper has presented an optimal scheduling algorithm to minimize execution time and energy consumption based on the Flower Pollination Algorithm (FPA) and Henon's chaotic map for scheduling independent tasks. First, Henon's chaotic map has been employed to generate the initial solutions, and then FPA has been used to schedule cloudlets on appropriate virtual resources. Tests have been performed on two data sets. The results of the proposed scheduling algorithm have been studied and compared with those of other existing algorithms in the literature. The results show that the proposed scheduling algorithm has a better result than the competing algorithms in terms of both execution time and energy consumption.