Distributed Redundant Placement for Microservice-based Applications at the Edge

Multi-access edge computing (MEC) is booming as a promising paradigm to push the computation and communication resources from cloud network provide services perform computations. With container technologies, mobile devices with small memory footprint can run composite microservice-based applications without time-consuming backbone. Service placement at of importance put MEC theory into practice. However, current state-of-the-art research does not sufficiently take property consideration. Besides, although Kubernetes has certain abilities heal failures, high availability cannot be ensured due heterogeneity variability sites. To deal these problems, we propose distributed redundant framework SAA-RP GA-based Server Selection (GASS) algorithm for sequential combinatorial structure. We formulate stochastic optimization problem uncertainty microservice request considered, then decide each microservice, how it should deployed many instances well on which sites place them. Benchmark policies are implemented in two scenarios, where redundancy allowed not, respectively. Numerical results based real-world dataset verify that GASS significantly outperforms all benchmark policies.