Resource scheduling approach in cloud Testing as a Service using deep reinforcement learning algorithms

摘要

Many organizations around the world use cloud computing Testing as Service(Taas)for their services.Cloud computing is principally based on the idea of on-demand delivery of computation,storage,applications,and additional resources.It depends on delivering user services through Internet connectivity.In addition,it uses a pay-as-you-go business design to deliver user services.It offers some essential characteristics including on-demand service,resource pooling,rapid elasticity,virtualization,and measured services.There are various types of virtualization,such as full virtualization,para-virtualization,emulation,〇S virtualization,and application virtualization.Resource scheduling in Taas is among the most challenging jobs in resource allocation to mandatory tasks/jobs based on the required quality of applications and projects.Because of the cloud environment,uncertainty,and perhaps heterogeneity,resource allocation cannot be addressed with prevailing policies.This situation remains a significant concern for the majority of cloud providers,as they face challenges in selecting the correct resource scheduling algorithm for a particular workload.The authors use the emergent artificial intelligence algorithms deep RM2,deep reinforcement learning,and deep reinforcement learning for Taas cloud scheduling to resolve the issue of resource scheduling in cloud Taas.