Business-oriented autonomic load balancing for multitiered Web sites

Autonomic computing systems are able to adapt to changing environments (such as changes in the workload intensity or component failures) in a way that preserves highlevel operational goals, such as service level objectives. This paper focuses on autonomic computing systems that are self-optimizing and self-configuring. More specifically, the paper presents the detailed design of an autonomic load balancer (LB) for multi-tiered Web sites. It is assumed that customers can be categorized into distinct classes (gold, silver, and bronze) according to their business value to the site. While the example used in the paper is that of an auction site, the approach can be easily applied to any other Web site. The autonomic LB is able to dynamically change its request redirection policy as well as its resource allocation policy, which determines the allocation of servers to server clusters, in a way that maximizes a business-oriented utility function. The autonomic LB was evaluated through very detailed and comprehensive simulation experiments and was compared against a round-robin LB and against a situation where each customer category has a dedicated number of servers. The results showed that the autonomic LB outperforms the other load balancing approaches in terms of providing a higher utility for highly dynamic workloads.