A meta-controller method for improving run-time self-architecting in SOA systems

Abstract

This paper builds on SASSY, a system for automatically generating SOA software architectures that optimize a given utility function of multiple QoS metrics. In SASSY, SOA software systems are automatically re-architected when services fail or degrade. Optimizing both architecture and service provider selection presents a pair of nested NP-hard problems. Here we adapt hill-climbing, beam search, simulated annealing, and evolutionary programming to both architecture optimization and service provider selection. Each of these techniques has several parameters that influence their efficiency. We introduce in this paper a meta-controller that automates the run-time selection of heuristic search techniques and their parameters. We examine two different meta-controller implementations that each use online learning. The first implementation identifies the best heuristic search combination from various prepared combinations. The second implementation analyzes the current self-architecting problem (e.g. changes in performance metrics, service degradations/failures) and looks for similar, previously encountered re-architecting problems to find an effective heuristic search combination for the current problem. A large set of experiments demonstrates the effectiveness of the first meta-controller implementation and indicates opportunities for improving the second meta-controller implementation.