Self-* through self-learning: Overload control for distributed web systems

Overload control is a challenging problem for web-based applications, which are often prone to unexpected surges of traffic. Existing solutions are still far from guaranteeing the necessary responsiveness under rapidly changing operative conditions. We contribute an original self-* overload control (SOC) algorithm that self-configures a dynamic constraint on the rate of incoming new sessions in order to guarantee the fulfillment of the quality requirements specified in a service level agreement (SLA). Our algorithm is based on a measurement activity that makes the system capable of self-learning and self-configuring even in the case of rapidly changing traffic scenarios, dynamic resource provisioning or server faults. Unlike other approaches, our proposal does not require any prior information about the incoming traffic, or any manual configuration of key parameters. We ran extensive simulations under a wide range of operating conditions. The experiments show how the proposed system self-protects from overload, meeting SLA requirements even under intense workload variations. Moreover, it rapidly adapts to unexpected changes in available capacity, as in the case of faults or voluntary architectural adjustments. Performance comparisons with other previously proposed approaches show that our algorithm has better performance and more stable behavior.