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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In a multiple-services mobile edge computing network environment, the requirements and popularity of services, the computing requirement and the amount of data transferred from users to edge servers are dynamic with time. How to adaptively adjust the caching subset of total service types in the resource-limited edge server and determine the task offloading destination and resource allocation decisions to obtain the optimum overall system performance is a challenge problem. To solve this challenge, this paper firstly converted it into a Markov decision process, then proposed a soft actor-critic (SAC) -based deep reinforcement learning algorithm to jointly determine not only the discrete decisions of service caching and task offloading but also the continuous allocation of bandwidth and computing resource. The algorithm employed an effective trick of converting multiple continuous action outputs into discrete action choices to deal with the key design challenge that arised from continuous-discrete hybrid action space while improving the accuracy of algorithm decisions. Additionally, the algorithm integrated an efficient reward function that added auxiliary reward terms to improve resource utilization. Extensive numerical results show that compared with other baseline algorithms, the proposed algorithm can effectively reduce the long-term average completion delay of tasks while accessing excellent performance in terms of stability. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the main means of protecting information security, access control can effectively guarantee users' legitimate access to resources. With the development of mobile Internet, security issues in multi-domain environments such as cross-domain and cross-system are facing severe challenges. In order to meet the requirements of cloud computing multi-domain environment, this paper proposed an access control model for cloud computing multi-domain environment based on role access control technology. The model used Bayesian theory to derive the trusted expectation value of the visitor, and then compared it with the preset access threshold to determine whether the user' s access request was allowed. The access permission also was dynamically change with the user's credibility, which avoided the risk of malicious attack by users who had previously obtained high trust value due to the change of trust value. The experimental results show that the model can not only reduce the number of access requests for high-risk users, but also meet the demand for dynamic authorization for users. Therefore, the model can effectively solve the security problems in the multi-domain environment of cloud computing. [ABSTRACT FROM AUTHOR]

Aiming at the performance optimization problem of the virtual machine migration process in mobile cloud computing, this paper proposed a pre-copy transmission strategy based on the optimal stopping theory. The strategy solved the optimal stopping model which looked for the optimal transmission rate with the optimal stopping theory,to obtain the optimal transmission rate. So that it resulted in the reduction of the total amount of migration data and the total time in the virtual machine migration process. In the simulation experiment .this paper compared the proposed transmission strategy with the referenced transmission strategies, and analyzed the performance of different transmission strategies. The experimental results show' that the proposed strategy get less total amount of migration data and total time,and can effectively improve the performance of the migration process. [ABSTRACT FROM AUTHOR]