A Self-Healing Model for QoS-aware Web Service Composition

Abstract

In the Web Service Composition (WSC) domain, Web Services (WSs) execute in a highly dynamic environment, as a result, the Quality of Service (QoS) of a WS is constantly evolving, and this requires tracking of the global optimization overtime to satisfy the users’ requirements. In order to make a WSC adapt to such QoS changes of WSs, we propose a self-healing model for WSC. Self-healing is the automatic discovery, and healing of the failure of a composite WS by itself due to QoS changes without interruption in the WSC and any human intervention. To the best of our knowledge, almost all the existing self-healing models in this domain substitute the faulty WS with an equivalent one without paying attention to the WS selection processes to achieve global optimization. They focus only on the WS substitution strategy. In this paper, we propose a self-healing model where we use our hybrid approach to find the optimal WSC by using Parallel Genetic Algorithm based on Q-learning, which we integrate with K-means clustering (PGAQK). The components of this model are organized according to IBM’s Monitor, Analyse, Plan, Execute, and Knowledge (MAPE-K) reference model. The PGAQK approach considers as a module in the Execute component. WS substitution strategy has also been applied in this model that substitutes the faulty WS with another equivalent one from a list of candidate WSs by using the K-means clustering technique. K-means clustering is used to prune the WSs in the search space to find the best WSs for the environment changes. We implemented this model over the NET Framework using C# programming language. A series of comparable experiments showed that the proposed model outperforms improved GA to achieve global optimization. Our proposed model also can dynamically substitute the faulty WSs with other equivalent ones in a time-efficient manner