Dynamic QoS Management and Optimization in Service-Based Systems

TLDR

Service‑based systems that are dynamically composed at runtime are a leading software engineering paradigm, yet their Quality of Service still requires adaptive and predictable management. To meet this need, we present QoSMOS, a tool‑supported framework for developing adaptive service‑based systems. QoSMOS translates high‑level QoS requirements into probabilistic temporal logic, automatically analyzes them to identify optimal configurations, and enforces dynamic adaptation for reliability and performance, making it applicable to both new and legacy systems. Its effectiveness and scalability were demonstrated through simulations and experiments on an adaptive remote medical assistance system.

Abstract

Service-based systems that are dynamically composed at runtime to provide complex, adaptive functionality are currently one of the main development paradigms in software engineering. However, the Quality of Service (QoS) delivered by these systems remains an important concern, and needs to be managed in an equally adaptive and predictable way. To address this need, we introduce a novel, tool-supported framework for the development of adaptive service-based systems called QoSMOS (QoS Management and Optimization of Service-based systems). QoSMOS can be used to develop service-based systems that achieve their QoS requirements through dynamically adapting to changes in the system state, environment, and workload. QoSMOS service-based systems translate high-level QoS requirements specified by their administrators into probabilistic temporal logic formulae, which are then formally and automatically analyzed to identify and enforce optimal system configurations. The QoSMOS self-adaptation mechanism can handle reliability and performance-related QoS requirements, and can be integrated into newly developed solutions or legacy systems. The effectiveness and scalability of the approach are validated using simulations and a set of experiments based on an implementation of an adaptive service-based system for remote medical assistance.