Adaptive Management of Composite Services under Percentile-Based Service Level Agreements

Abstract

We present a brokering service for the adaptive management of composite services. The goal of this broker is to dynamically adapt at runtime the composite service configuration, to fulfill the Service Level Agreements (SLAs) negotiated with different classes of requestors, despite variations of the operating environment. Differently from most of the current approaches, where the performance guarantees are characterized only in terms of bounds on average QoS metrics, we consider SLAs that also specify upper bounds on the percentile of the service response time, which are expected to better capture user perceived QoS. The adaptive composite service management is based on a service selection scheme that minimizes the service broker cost while guaranteeing the negotiated QoS to the different service classes. The optimal service selection is determined by means of a linear program that can be efficiently solved. As a result, the proposed approach is scalable and lends itself to an efficient implementation.

Traffic Management for Cloud Federation

“…A number of solutions have been proposed for the problem of dynamic, runtime QoS-aware service selection and composition within SOA [46][47][48][49]. These (proactive) solutions aim to adapt the service composition dynamically at runtime.…”

Dynamic adaptation of service compositions with variability models

“…This approach can hinder reasoning about adaptations with complex and error-prone scripts ). The second group focuses on low-level implementation mechanisms for self-adaptation (Erradi and Maheshwari, 2005, Cardellini et al, 2010, Mosincat and Binder, 2008. This approach lacks support for analyzing the inherent variability of dynamic adaptation at design time.…”

Analysis of Revenue Improvements with Runtime Adaptation of Service Composition Based on Conditional Request Retries

“…the optimal compostion does not change at runtime. More recent work in this area focuses on dynamic, runtime composition solutions and adaptations [9,10,12]. For each task invocation, the orchestrator dynamically binds the task of the abstract composition to an actual implementation (i.e., concrete service), selecting it from the pool of service providers that offer it.…”

“…Due to the dynamic service composition it may happen that every composite service request is served by different composition. The service selection is driven by the solution of a suitable optimization problem, which is reduced to the linear optimization problem [9], or the optimization is based on evolutionary computation [10] or is based on the principles of dynamic programming [12]. However, none of [9,10,12] consider the possible applicability of retry mechanisms, i.e.…”