Adaptive Microservice Deployment for Data Partition-Based Applications in MEC

Recently, Mobile Edge Computing (MEC) has used lightweight container-based microservices to provide resources for artificial intelligence applications, which will be decomposed into multiple dependent components, forming a Directed Acyclic Graph (DAG). In MEC, users will partition the input of the computation-intensive tasks into multiple sub-tasks for parallel execution acceleration. To satisfy concurrency, app vendors must deploy multiple container replicas for a microservice. Due to the limited capacity of edge servers, containers need to be deployed into geographically distributed and heterogeneous edge servers, resulting in significant inter-edge server traffic. To this end, we propose an adaptive scheme to guide microservice deployment for data partition-based applications in the MEC. We model the multi-replica microservice deployment problem as an integer programming problem to minimize operation costs. We propose a Deterministic Local Search-based Microservice Deployment algorithm (DLSMD), that chooses a superior neighborhood solution iteratively to solve it. We also formulate a more general problem considering both computing and communication time to minimize the total completion time and devise a Heuristic Microservice Deployment (HMD) algorithm to solve it. Extensive simulation results show that DLSMD and HMD outperform other benchmarks, achieving up to $4\times$ and $3.12\times$ speedups in terms of the inter-server traffic and total completion time reduction, respectively.