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OptCaching: A Stackelberg Game and Belief Propagation Based Caching Scheme for Joint Utility Optimization in Fog Computing

机译:OptCaching:基于Stackelberg游戏和基于信仰传播的雾化计算中的联合实用优化缓存方案

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Fog Computing which extends the cloud computing paradigm to the edge of the network provides great opportunities for applications with stringent latency requirement. How to allocate the limited caching resources of Fog Nodes (FNs) influences the performance of the fog computing system. In contrast to previous works on caching resource allocation with users' utility as the only consideration, we propose OptCaching which jointly optimize the utility of all network participants including Content Provider (CP), Internet Service Provider (ISP) and users. With caching incentive introduced, utility functions of these three roles are defined. Our joint utility optimization caching scheme is conducted in two stages combining global and local decision making. Firstly, interaction between CP and ISP is modeled as a non-cooperative hierarchy Stackelberg game to make decision on incentive caching prices and global caching amount aiming at optimizing the utility of all network participants. Secondly, for the purpose of further optimizing the utility of users, a belief propagation based cache placement algorithm which utilizes global caching amount constraint and local information is conducted by FNs to reduce users' average download delay. Mathematical analysis and simulation results show that the utility of CP, ISP and users are jointly optimized at Stackelberg equilibrium. The utility of users is further optimized by belief propagation based cache placement algorithm with users' average download delay reduced by 33.7% compared with global popularity based caching strategy.
机译:雾计算将云计算范例扩展到网络边缘为具有严格延迟要求的应用程序提供了很大的机会。如何分配雾节点的有限缓存资源(FNS)影响雾计算系统的性能。与以前的工作与用户实用程序为唯一的考虑进行缓存资源分配,我们提出了选择OptCaching,该OptCaching共同优化了所有网络参与者的实用程序,包括内容提供商(CP),Internet服务提供商(ISP)和用户。随着缓存激励引入,定义了这三个角色的实用功能。我们的联合公用事业优化缓存方案在两个阶段进行了相结合的全球和局部决策。首先,CP和ISP之间的交互被建模为非合作层次结构Stackelberg游戏,以决定激励缓存价格和全球高速缓存金额,旨在优化所有网络参与者的效用。其次,为了进一步优化用户的实用性,通过FNS进行使用全局高速缓存量约束和本地信息的基于信仰传播的高速缓存放置算法,以减少用户的平均下载延迟。数学分析和仿真结果表明,CP,ISP和用户的效用在Stackelberg均衡中共同优化。与基于全球受欢迎的缓存策略相比,通过基于信仰传播的高速缓存放置算法进一步优化了用户的实用性,与用户的平均下载延迟减少了33.7%。

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