Abstract
Microservice is a new architecture for application development that makes applications more flexible to deploy, extend and update compared to monolithic architectures. As container-based clouds rapidly gained popularity in recent years, more microservices are deployed in containers and composed of complex and elaborated applications for users. The challenges of microservices deployment in a container-based clouds arise from two-level resource allocations to not only minimize the overall energy consumption but also to reduce the communication data volume between microservices in physical networks to improve application performance. However, there is still a lack of research that considers these two important challenges jointly during microservice composition and resource allocation. Motivated by this, in this work, we propose a genetic algorithm-based algorithm, namely EC-GGA, to not only minimize the energy consumption in cloud data centers but also minimize the communication data volume of applications. We compare EC-GGA with several state-of-the-art algorithms to demonstrate the effectiveness of our proposed algorithm.
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Fang, Z., Ma, H., Chen, G., Hartmann, S. (2023). Energy-Efficient and Communication-Aware Resource Allocation in Container-Based Cloud with Group Genetic Algorithm. In: Monti, F., Rinderle-Ma, S., Ruiz Cortés, A., Zheng, Z., Mecella, M. (eds) Service-Oriented Computing. ICSOC 2023. Lecture Notes in Computer Science, vol 14419. Springer, Cham. https://doi.org/10.1007/978-3-031-48421-6_15
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