QoS‐aware resource scheduling using whale optimization algorithm for microservice applications

Abstract

AbstractMicroservices is a structural approach, where multiple small set of services are composed and processed independently with lightweight communication mechanism. To accomplish the end‐user demand in minimum delay and cost without violating the service level agreement (SLA) constraints and overhead is a challenging issue in cloud computing. In addition, existing framework tries to deploy the microservice over the best computing resource for latency‐sensitive applications, but long boot‐time, and low resource utilization still remains a challenging task. To find the solution for aforementioned issues, we propose a Quality of Service (QoS) aware resource allocation model based on a Fine‐tuned Sunflower Whale Optimization Algorithm (FSWOA) that find the best resources for microservice deployment and fulfill the objectives of users as well as service provider. The proposed technique deploys the container‐based services over the physical machine based upon the capacity, to execute the micro services by utilizing the CPU and memory maximally. The proposed work aims is to distribute the workload in efficient manner and avoid the wastage of resources that leads to optimize the QoS parameters. The experimental results conducted in simulation environment demonstrates that proposed approach perform superior over baseline approaches and reduces the time, memory consumption, CPU consumption, and service cost up to 4.26%, 11.29%, 17.07% and 24.22% compared to SFWAO, GA, PSO and ACO.