ACEA: A Queueing Model-Based Elastic Scaling Algorithm for Container Cluster

• Published in: Wireless communications and mobile computing Vol. 2021; no. 1
• Main Authors: Li, Kui, Ji, Yi-mu, Liu, Shang-dong, Yao, Hai-chang, Li, Hang, You, Shuai, Shao, Si-si
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Adaptive algorithms; Algorithms; Cloud computing; Clusters; Containers; Optimization; Particle swarm optimization; Performance evaluation; Resource utilization; Scaling; Scheduling; Solution space
• ISSN: 1530-8669; 1530-8677; 1530-8677
• DOI: 10.1155/2021/6621094

Elastic scaling is one of the techniques to deal with the sudden change of the number of tasks and the long average waiting time of tasks in the container cluster. The unreasonable resource supply may lead to the low comprehensive resource utilization rate of the cluster. Therefore, balancing the relationship between the average waiting time of tasks and the comprehensive resource utilization rate of the cluster based on the number of tasks is the key to elastic scaling. In this paper, an adaptive scaling algorithm based on the queuing model called ACEA is proposed. This algorithm uses the hybrid multiserver queuing model (M/M/s/K) to quantitatively describe the relationship among number of tasks, average waiting time of tasks, and comprehensive resource utilization rate of cluster and builds the cluster performance model, evaluation function, and quality of service (QoS) constraints. Particle swarm optimization (PSO) is used to search feasible solution space determined by the constraint relation of ACEA quickly, so as to improve the dynamic optimization performance and convergence timeliness of ACEA. The experimental results show that the algorithm can ensure the comprehensive resource utilization rate of the cluster while the average waiting time of tasks meets the requirement.