Using Genetic Algorithms to Optimize Job Scheduling in Google Cloud Platform
Pawan Kumar, Senthil Pandi S, S. Prabhakar Karthikeyan
Abstract
Optimizing performance and resource utilization in cloud computing environments—especially on Google Cloud Platform (GCP)—requires efficient job scheduling and resource allocation. This study investigates Genetic Algorithm (GA) as a substitute method for GCP resource allocation and job scheduling. Motivated by natural selection principles, GA repeatedly refines possible task-to-resource allocations in the cloud. GA use population-based optimization to produce close to optimum solutions by taking into account several parameters including cost, execution time, and resource consumption. The study evaluates GA's efficacy in diverse and dynamic cloud settings by contrasting it with other algorithms, such as DBSCAN, Google Cloud Dataproc, and Google Kubernetes Engine. GA has benefits include addressing constraints, exploring several solution spaces, and adapting to complicated optimization problems. Insights into the scalability and performance of GA in GCP settings are obtained by testing and analysis, which helps academics and cloud practitioners choose the best job scheduling schemes. Through improved system efficiency and scalability on GCP, the research advances our understanding of effective resource allocation in cloud computing.