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Proactive Data Caching and Replacement in the Edge Computing Environment

机译:主动数据缓存和更换边缘计算环境

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Mobile data traffic is exploding in recent years with the exponential growth of mobile users. In the edge computing environment where edge servers are deployed around mobile users, caching data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache and replacement problem with the consideration of the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack proactive data caching and replacement problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching and replacement problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. We also propose an online algorithm to solve problems in large-scale scenarios. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users.
机译:近年来,移动数据流量正在爆炸,随着移动用户的指数增长。在Edge Computing环境中,边缘服务器部署在移动用户周围的边缘服务器上,Edge服务器上的缓存数据可以确保移动用户对这些数据的快速访问,并减少移动用户和集中云之间的数据流量。在考虑降低网络延迟和改进移动设备的能源效率时,现有研究考虑数据缓存和更换问题。在本文中,我们从服务提供商的角度攻击边缘计算环境中的主动数据缓存和替换问题,他们希望最大限度地缓解其数据的场地。此问题很复杂,因为数据缓存以成本产生益处,并且通常会有折衷。在本文中,我们将数据缓存和替换问题作为整数编程问题,并最大限度地提高服务提供商的收入,同时满足数据访问延迟的约束。我们还提出了一种在线算法来解决大规模场景中的问题。在包含边缘服务器和移动用户的位置的真实数据集中进行了广泛的实验。

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