Optimizing Selection of Competing Services with Probabilistic Hierarchical Refinement

Abstract

Recently, many large enterprises (e.g., Netflix, Amazon) have decomposed their monolithic application into services, and composed them to fulfill their business functionalities. Many hosting services on the cloud, with different Quality of Service (QoS) (e.g., availability, cost), can be used to host the services. This is an example of competing services. QoS is crucial for the satisfaction of users. It is important to choose a set of services that maximize the overall QoS, and satisfy all QoS requirements for the service composition. This problem, known as optimal service selection, is NP-hard. Therefore, an effective method for reducing the search space and guiding the search process is highly desirable. To this end, we introduce a novel technique, called Probabilistic Hierarchical Refinement (PROHR). PROHR effectively reduces the search space by removing competing services that cannot be part of the selection. PROHR provides two methods, probabilistic ranking and hierarchical refinement, that enable smart exploration of the reduced search space. Unlike existing approaches that perform poorly when QoS requirements become stricter, PROHR maintains high performance and accuracy, independent of the strictness of the QoS requirements. PROHR has been evaluated on a publicly available dataset, and has shown significant improvement over existing approaches.