Budgeted Data Caching based on k-Median in Mobile Edge Computing

In mobile edge computing (MEC), edge servers are deployed at base stations to provide highly accessible computational resources and storage capacities to nearby mobile devices. Caching data on edge servers can ensure the service quality and network latency for those mobile devices. However, an app vendor needs to ensure that the data caching cost does not exceed its data caching budget. In this paper, we present the budgeted edge data caching (BEDC) problem as a constrained optimization problem to maximize the overall reduction in data retrieval for all its app users within the budget, and prove that it is NP-hard. Then, we provide an approach named IP-BEDC for solving the BEDC problem optimally based on Integer Programming. We also provide an O(k) -approximation algorithm, namely α-BEDC, to find near-optimal solutions to the BEDC problems efficiently. Our proposed approaches are evaluated on a real-world data set and a synthesized data set. The results demonstrate that our approaches can solve the BEDC problem effectively and efficiently while significantly outperforming five representative approaches.