Real-Time QoS Control for Service Orchestration

Service orchestration has become the predominant paradigm that enables businesses to combine and integrate services offered by third parties. For the commercial viability of orchestrated services, it is crucial that they are offered at sharp price-quality ratios. A complicating factor is that many attractive third-party services often show highly variable service quality. This raises the need for mechanisms that promptly adapt the orchestration to changes in the quality delivered by third party services. In this paper, we propose a real-time QoS control mechanism that dynamically optimizes service orchestration in real time by learning and adapting to changes in third party service response time behaviors. Our approach combines the power of learning and adaptation with the power of dynamic programming. The re¬sults show that real-time service re-compositions lead to dramatic savings of cost, while meeting the service quality requirements of the end-users. The challenge here is to respond to signi?cant response-time changes in a timely manner, while not wasting CPU cycles on unnecessary orchestration updates. Experimental results performed in a test-lab environment demonstrate that a few orchestration updates are suf?cient to achieve this.