A Self-Healing Model for QoS-aware Web Service Composition
Doaa Elsayed1, Eman Nasr3, Alaa
El Ghazali4, and Mervat Gheith2
1Department of Information Systems and Technology, Cairo
University, Egypt
2Department of Computer Science, Cairo University,
Egypt
3Independent Researcher, Egypt
4Department of
Computer and Information Systems, Sadat Academy for Management Sciences, Egypt
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.
Keywords: Web
service composition, self-healing, quality of service, user requirements;
K-means clustering.
Received June 29, 2018; accepted
January 28, 2020