Energy efficient computational offloading framework for mobile cloud computing

Muhammad Shiraz, Abdullah Gani, Azra Shamim, Suleman Khan, Raja Wasim Ahmad

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)

Abstract

The latest developments in mobile computing technology have changed user preferences for computing. However, in spite of all the advancements in the recent years, Smart Mobile Devices (SMDs) are still low potential computing devices which are limited in memory capacity, CPU speed and battery power lifetime. Therefore, Mobile Cloud Computing (MCC) employs computational offloading for enabling computationally intensive mobile applications on SMDs. However, state-of-the-art computational offloading frameworks lack of considering the additional overhead of components migration at runtime. Therefore resources intensive and energy consuming distributed application execution platform is established. This paper proposes a novel distributed Energy Efficient Computational Offloading Framework (EECOF) for the processing of intensive mobile applications in MCC. The framework focuses on leveraging application processing services of cloud datacenters with minimal instances of computationally intensive component migration at runtime. As a result, the size of data transmission and energy consumption cost is reduced in computational offloading for MCC. We evaluate the proposed framework by benchmarking prototype application in the real MCC environment. Analysis of the results show that by employing EECOF the size of data transmission over the wireless network medium is reduced by 84 % and energy consumption cost is reduced by 69.9 % in offloading different components of the prototype application. Hence, EECOF provides an energy efficient application layer solution for computational offloading in MCC.
Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalJournal of Grid Computing
Volume13
Issue number1
Early online date13 Jan 2015
DOIs
Publication statusPublished - 1 Mar 2015
Externally publishedYes

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