Automated Configuration for Agile Software Environments

Abstract

The increasing use of the DevOps paradigm in software systems has substantially increased the frequency of configuration parameter setting changes. Ensuring the correctness of such settings is generally a very challenging problem due to the complex interdependencies, and calls for an automated mechanism that can both run quickly and provide accurate settings. In this paper, we propose an efficient discrete combinatorial optimization technique that makes two unique contributions: (a) an improved and extended metaheuristic that exploits the application domain knowledge for fast convergence, and (b) the development and quantification of a discrete version of the classical tunneling mechanism to improve the accuracy of the solution. Our extensive evaluation using available workload traces that do include configuration information shows that the proposed technique can provide a lower-cost solution (by ~60%) with faster convergence (by ~48%) as compared to the traditional metaheuristic algorithms. Also, our solution succeeds in finding a feasible solution in approximately 30% more cases than the baseline algorithm.