Joint optimization of service chain caching and task offloading in mobile edge computing

Abstract Caching and offloading in Mobile Edge Computing (MEC) are hot topics recently. Existing caching strategies at the edge ignore the programming ability of edge network and design strategies independently thus network resource is under utilization and the quality of experience (QOE) for end users is far from satisfactory. In this paper, we design intelligently joint caching and offloading strategies under the assumption that applications can be in the form of divisible service chain. Different from common approaches that target on reducing response latency only for users, our system take the leasing cost into consideration thus is more efficient for Application Service Providers (ASP). To fulfill our design, we novelly utilize open Jackson queuing network to formulate this joint optimization problem under long term cost restrictions and design a pipeline of algorithm to search for the optimal solution. More specifically, we design a cost adaptive algorithm derived from lypunov drift-plus-penalty function so that the long-term problem can be optimized in the slot-by-slot basis. Moreover, we propose to exploit resource-based utility function and device-number-based relative distance to jointly find optimal caching and offloading scheme. Extensive simulation results demostrate that our approach can effectively reduce the average service lantency of the MEC system and keep a low average leasing cost.