A performance analysis method for autonomic computing systems

In an autonomic computing system, an autonomic manager makes tuning, load balancing, or provisioning decisions based on a predictive model of the system. This article investigates performance analysis techniques used by the autonomic manager. It looks at the complexity of the workloads and presents algorithms for computing the bounds of performance metrics for distributed systems under asymptotic and nonasymptotic conditions, that is, with saturated and nonsaturated resources. The techniques used are hybrid in nature, making use of performance evaluation and linear and nonlinear programming models. The workloads are characterized by the workload intensity , which represents the total number of users in the system, and by the workload mixes , which depict the number of users in each class of service. The results presented in this article can be applied to distributed transactional systems. Such systems serve a large number of users with many classes of services and can thus be considered as representative of a large class of autonomic computing systems.