Energy Aware Task Offloading Approach in Mobile Cloud Computing Environment using Hybridized Optimization Algorithm with Multi-Objective Functions

Abstract

Mobile Cloud Computing (MCC) becomes an emerging computing paradigm, where Mobile Devices (MDs) are in the place for offloading task to the nearest resource-rich cloud servers. To promote the system’s performance, the MCC is performed. However, it holds with more overhead complexity in storage and energy, which degrades the network efficiency. Hence the scholar concentrates on decreasing the overhead issue by applying the task offloading process. The major issue in this mechanism is having most cost-effective communication among the devices. This research paper suggests a new optimization strategy for performing the offloading task in MCC. The developed hybrid approach offloads the task to the nearby server to enhance the performance of the MCC by finishing the task within the deadline. A new cost function is derived with the adoption of the average delay of tasks, the energy consumption level, battery lifetime, processing capabilities, storage capacity, response time, communication cost, etc for optimizing the task offloading. Thus, a new task offloading is optimized via a newly recommended hybrid optimizer with the adoption of Probability Condition of Satin Bowerbird Forensic Optimization (PCSBFO), which is developed with the combination of Satin Bowerbird Optimization (SBO) and Forensic- Based Investigation (FBI) to achieve optimal solutions. Additionally, the developed PCSBFO considers the multi-objective constraints such as average delay, energy consumption, and offloading expenditure for ensuring the quality of service, and satisfactory level of the end user in the MCC. This suggested lightweight paradigm addresses the difficulties and minimizes the efforts while developing, deploying, and managing to offload using optimization algorithms to help better available frameworks. Further, the creation of APAs is done to enable the mobile applications to extract maximum utility out of the volumes of available resources. The experiment results show that the suggested hybrid optimization-based task …

Keywords

Mobile cloud computing / task offloading / optimal allocation ratio / probability condition of satin bowerbird forensic optimization / multi-objective formulation

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Systems Engineering Society of China and Springer-Verlag GmbH Germany