Self-adaptation of service level in distributed systems

International audienceModern distributed systems are diverse and dynamic, and consequently difficult to manage using traditional approaches, which rely on an extensive initial knowledge of the system. On the performance front, these systems often offer multiple opportunities for dynamically degrading or improving service level based on workload intensity, to avoid overload and underload. In this context, we propose a novel approach for building distributed systems capable of autonomously deciding when and how to adapt service level. Our approach limits the knowledge that must be provided manually to a component-based representation of the system. From this representation, we build and maintain a performance profile, which allows us to (1) identify the most promising adaptations based on workload type and (2) dynamically characterize the intrinsic efficiency of each adaptation based on past attempts. We have successfully implemented and evaluated a prototype of our approach in the context of multi-tiered application servers