Computer Science ›› 2020, Vol. 47 ›› Issue (6): 247-251.doi: 10.11896/jsjkx.190400020

• Computer Network • Previous Articles     Next Articles

Energy Optimization Oriented Resource Management in Mobile Cloud Computing

JIN Xiao-min, HUA Wen-qiang   

  1. School of Computer Science and Technology,Xi’an University of Posts and Telecommunications,Xi’an 710121,China
    Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing,Xi’an University of Posts and Telecommunications,Xi’an 710121,China
  • Received:2019-04-03 Online:2020-06-15 Published:2020-06-10
  • About author:JIN Xiao-min,born in 1990,Ph.D,lecturer,is a member of China Computer Federation.His main research interests include mobile cloud computing and edge computing.
  • Supported by:
    This work was supported by the Special Scientific Research Program of Education Department of Shaanxi Province (19JK0806),Key Research and Development Program of Shaanxi Province (2019ZDLGY07-08),Young Teachers Research Foundation of Xi’an University of Posts and Telecommunications,and Special Funds for Construction of Key Disciplines in Universities in Shaanxi

Abstract: As an extension of the traditional cloud computing,mobile cloud computing (MCC) breaks through the bottleneck of mobile device resources and enhances its capabilities by computation offloading.However,MCC faces many problems while bringing advantages.The problem of resource managementis related to the benign operation of MCC,and it is the key to determining whether MCC can be scaled up.To solve the problem of resource management in MCC,firstly,a resource management model aiming at optimizing energy consumption of the cloud resource operator is established,which is a constrained combinatorial optimization problem.Then a resource management strategy solution algorithm based on the heuristic adaptive simulated annealing genetic algorithm is proposed.This algorithm initializes the population by using the first fit algorithm and combines the adaptive algorithm and the simulated annealing algorithm to optimize its genetic operations.Simulation shows that the proposed algorithm can obtain the approximate optimal resource management strategy and has advantages of fast convergence rate and not easy to fall into local optimal solutions.The simulation experiments also compare the resource management effects of the traditional round robin algorithm and the first fit algorithm,and the results show that these two algorithms are not suitable for resource management in MCC.

Key words: Mobile cloud computing, Resource management, Energy optimization, Genetic algorithm, Simulated annealing

CLC Number: 

  • TP393
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