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

Microservices 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...

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Vydáno v:Software, practice & experience Ročník 54; číslo 4; s. 546 - 565
Hlavní autoři: Kumar, Mohit, Samriya, Jitendra Kumar, Dubey, Kalka, Gill, Sukhpal Singh
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.04.2024
Wiley Subscription Services, Inc
Témata:
Algorithms
Ant colony optimization
Cloud computing
Computer memory
Consumption
fine‐tuned sunflower whale optimization algorithm
microservices
Optimization algorithms
Quality of service
Resource allocation
Resource scheduling
Resource utilization
ISSN:0038-0644, 1097-024X
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Shrnutí:Microservices 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.
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ISSN:0038-0644
1097-024X
DOI:10.1002/spe.3211