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