Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2020-09-28 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problem of long time-consuming and high equipment energy consumption for task scheduling in mobile cloud computing environment, a task scheduling strategy based on improved bird group algorithm is proposed, a task scheduling strategy based on Improved Bird Swarm Algorithm (IBSA) is proposed. Firstly, a mobile cloud task scheduling model based on energy consumption and time is constructed. Secondly, adaptive sensing coefficients and social coefficients are proposed to prevent the algorithm from falling into a local optimum. Learning factors are optimized to optimize flight behavior and ensure that Superior ability. Finally, . the task scheduling objective function is used as the fitness function of the bird group to participate in the iterative updating of the algorithm. The simulation results show that the algorithm has good effects in mobile cloud computing task scheduling compared with ant colony algorithm, particle swarm algorithm, whale algorithm and bird swarm algorithm, which can effectively save time and reduce energy consumption.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-05-10 Cooperative journals: 《计算机应用研究》
Abstract： Mobile cloud computing can use the computation offloading to improve the energy efficiency of mobile devices and the execution delay of applications. However, in the face of the multiple service selection from cloud, the computation offloading decision is a NP problem. In order to address this problem, a genetic algorithm is designed to find the best application partitioning decision solution for computation offloading. In genetic population initialization, our algorithm combines of predefined and random chromosomes to intialize the population, which can reduce the generation of the inefficient chromosomes. At the same time, the algorithm designs a specific fitness function based on Hamming distance function for the predefined reserved populution, which can better measure the difference between chromosomes. The population crossover uses respectively the inbreeding and crossbreeding to enrich individual species. Our algorithm uses the modified genetic operations to reduce the ineffective solutions and obtain the best feasible solution in a reasonable time. We evaluated the efficiency of the proposed algorithm using graphs of real mobile applications in simulation experiments. The evaluated conclusions denote that our designed algorithm has a better performance than the comparision algorithms on the application execution energy, the execution time and the overall weight cost.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-06-19 Cooperative journals: 《计算机应用研究》
Abstract： With the popularity of cloud computing, mobile devices can store and retrieve personal data anytime and anywhere. Attribute based encryption can be used to solve the problem of mobile cloud data security. At present, the research on attribute based encryption adapting to mobile cloud is mainly focused on single authority, which does not satisfy the real property authorization situation. This paper proposed a new multi-authority attribute based encryption scheme with no central authority, each authority in the scheme could not affect each other and attributes could be added independently. In addition, the scheme uses precomputation and outsourcing decryption to reduce the computation cost of the user side. Besides, the scheme was static secure under the random oracle model. Experimental results show that the scheme can reduce the computation cost of the user side by 20%, and it is more consistent with the data sharing application scenario in the mobile cloud environment.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-24 Cooperative journals: 《计算机应用研究》
Abstract： Mobile cloud computing can significantly enhance computation capability of mobile devices by offloading computation from the mobile devices onto the cloud. How to achieve energy-efficient computation offloading remains a challenge issue. For solving this problem, with reducing energy consumption and shorting application completion time as an objective, this algorithm was proposed. The algorithm will formulate computation offloading problem into the energy-eficiency cost minimization problem while satisfying the task-dependency requirements and the completion deadline constraint. And, a dynamic and energy-aware computation offloading algorithm is presented. The algorithm consists of three sub-algorithm of computation offloading selection, clock frequency control and transimission power allocation. Experimental results show that the algorithm can effective reduce the energy-eficiency cost by optimally adjusting the CPU clock frequency of mobile devices in local computing, and adapting the transmission power in cloud computing while ensures to satisfy the constraints and enhance the scheduling efficiency.
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