Abstract
Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resourceconstrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requirement and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real testbed and demonstrate that eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting transmission power for wireless channel conditions in cloud computing.
Original language | English |
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Journal | IEEE Transactions on Mobile Computing |
DOIs | |
Publication status | Published - Feb 2019 |
Keywords
- Clocks
- Cloud computing
- computation offloading
- Dynamic scheduling
- Energy consumption
- energy-efficiency cost
- Mobile cloud computing
- Mobile handsets
- Processor scheduling
- resource allocation
- Task analysis
ASJC Scopus subject areas
- Software
- Computer Networks and Communications
- Electrical and Electronic Engineering