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Title: Optimization of microservice composition based on artificial immune algorithm considering fuzziness and user preference
Authors: Gao, M
Chen, M
Liu, A
Ip, WH 
Yung, KL 
Issue Date: 2020
Source: IEEE access, 2020, v. 8, 8979424, p. 26385-26404
Abstract: Microservices is a new paradigm in cloud computing that separates traditional monolithic applications into groups of services. These individual services may correlate or cross multi-clouds. Compared to a monolithic architecture, microservices are faster to develop, easier to deploy, and maintain by leveraging modern containers or other lightweight virtualization. To satisfy the requirements of end-users and preferences, appropriate microservices must be selected to compose complicated workflows or processes from within a large space of candidate services. The microservice composition should consider several factors, such as user preference, correlation effects, and fuzziness. Due to this problem being NP-hard, an efficient metaheuristic algorithm to solve large-scale microservice compositions is essential. We describe a microservice composition problem for multi-cloud environments that considers service grouping relations and corresponding correlation effects of the service providers within intra- or inter-clouds. We use the triangular fuzzy number to describe the uncertainty of QoS attributes, the improved fuzzy analytic hierarchy process to calculate multi-attribute QoS, construct fuzzy weights related to user preferences, and transform the multi-optimal problem into a single-optimal problem. We propose a new artificial immune algorithm based on the immune memory clone and clone selection algorithms. We also introduce several optimal strategies and conduct numerical experiments to verify effects and efficiencies. Our proposed method combines the advantages of monoclone, multi-clone, and co-evolution, which are suitable for the large-scale problems addressed in this paper.
Keywords: Fuzzy analytic hierarchy process (FAHP)
Microservice group
QoS attribute
Quality of service (QoS)
The parallel cooperative short-term memory injection multi-clone clonal selection algorithm (ParaCoSIMCSA)
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.2971379
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
The following publication M. Gao, M. Chen, A. Liu, W. H. Ip and K. L. Yung, "Optimization of Microservice Composition Based on Artificial Immune Algorithm Considering Fuzziness and User Preference," in IEEE Access, vol. 8, pp. 26385-26404, 2020, is available at
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