Optimization of Microservice Composition Based on Artificial Immune Algorithm Considering Fuzziness and User Preference

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. INDEX TERMS Fuzzy analytic hierarchy process (FAHP), microservice, microservice group, quality of service (QoS), QoS attribute, the parallel cooperative short-term memory injection multi-clone clonal selection algorithm (ParaCoSIMCSA).

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

“…Salomie et al [169] introduced a hybrid genetic operator in the clonal selection , process to avoid the local entrapment pitfall. Moreover, Gao et al [170] introduced a new artificial immune algorithm based on the immune memory clone and clone selection algorithm by incorporation the fuzzy triangular numbers in QoS modeling.…”

“…The advance in virtualization technology gives rise to microservices technology that serves endusers by clustering traditional monolithic architecture into a group of services in this uncertain and inter-related context. Microservice composition is the challenge of determining optimal solutions while providing the highest user experience possible [202]. Moreover, mobility and uncertainty governing the edge environment create new issues that have not been anticipated previously.…”

Energy-Aware Task Allocation for Multi-Cloud Networks

“…Gao et al [17] have expressed a problem for multi-cloud environments that considers the clustering of services and their correlation effects of the CSPs within the cloud or among the clouds. Shi et al [18] have analyzed the system performance as well as the budget control in multi-cloud on a global scale.…”

An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers

“…According to equation 15, select the physical machine with the highest fitness value for deployment (c) Re-add process in crossover operation The mutation process is for local fine-tuning so that individuals cover the optimal global solution [22]. Therefore, the mutation operator used in the algorithm calculates each gene's fitness value according to Formula ( 14) and finds the gene with the smallest fitness value as the mutation object.…”