Multi-Objective Optimization of Deployment Topologies for Distributed Applications

Abstract

Modern applications are typically implemented as distributed systems comprising several components. Deciding where to deploy which component is a difficult task that today is usually assisted by logical topology recommendations. Choosing inefficient topologies allocates the wrong amount of resources, leads to unnecessary operation costs, or results in poor performance. Testing different topologies to find good solutions takes a lot of time and might delay productive operations. Therefore, this work introduces a software-based deployment topology optimization approach for distributed applications. We use an enhanced performance model generator that extracts models from operational monitoring data of running applications. The extracted model is used to simulate performance metrics (e.g., resource utilization, response times, throughput) and runtime costs of distributed applications. Subsequently, we introduce a deployment topology optimizer, which selects an optimized topology for a specified workload and considers on-premise, cloud, and hybrid topologies. The following three optimization goals are presented in this work: (i) minimum response time for an optimized user experience, (ii) approximate resource utilization around certain peaks, and (iii) minimum cost for running the application. To evaluate the approach, we use the SPECjEnterpriseNEXT industry benchmark as distributed application in an on-premise and in a cloud/on-premise hybrid environment. The evaluation demonstrates the accuracy of the simulation compared to the actual deployment by deploying an optimized topology and comparing measurements with simulation results.