Joint Service Deployment and Task Offloading for Datacenters With Edge Heterogeneous Servers

Abstract

Mobile edge computing (MEC) can improve execution efficiency and reduce overhead for offloading computing tasks to edge servers with more resources. In the microservice system, the current research only considers the cross segment communication cost of computing tasks, does not consider the case of the same end, and ignores the discovery and invocation optimization of associated services. In this paper, we propose CACO, which is a novel content-aware classification offloading framework for MEC based on correlation matrix. CACO first designs an adaptive service discovery model, which can make timely response and adjustment to the changes of the external environment. It then investigates an efficient affinity matrix based service discovery algorithm, which expresses the association relationship between services by constructing a service association matrix. In addition, CACO constructs a relational model by giving different weight coefficients to the delay and energy loss, which improves the delay and energy loss of message processing in a satisfying manner. Simulation results indicate that CACO reduces the total traffic of redundant messages by 46.2% $\sim$76.5%, respectively compared with state-of-the-art solutions. Testbed benchmarks show that it can also improve the stability by reducing control overhead by 34.5% $\sim$81.6% .