A Cost-Effective Workload Allocation Strategy for Cloud-Native Edge Services

Nowadays IoT applications consist of a collection of loosely coupled modules, namely microservices, that can be managed and placed in a heterogeneous environment consisting of private and public resources. It follows that distributing the application logic introduces new challenges in guaranteeing performance and reducing costs. However, most existing solutions are focused on reducing pay-per-use costs without considering a microservice-based architecture. We propose a cost-effective workload allocation for microservice-based applications. We model the problem as an integer programming problem and we formulate an efficient and near-optimal heuristic solution given the NP-hardness of the original problem. Numerical results demonstrate the good performance of the proposed heuristic in terms of cost reduction and performance with respect to optimal and state-of-the-art solutions. Moreover, an evaluation conducted in a Kubernetes cluster running in an OpenStack ecosystem confirms the feasibility and the validity of the proposed solution.