Open AccessResource scheduling approach in cloud Testing as a Service using deep reinforcement learning algorithms
Author(s) -
Karthik Priyadarsini,
Sekhar Karthik
Publication year - 2021
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/cit2.12041
Subject(s) - cloud computing , virtualization , computer science , scheduling (production processes) , reinforcement learning , pooling , emulation , distributed computing , quality of service , virtual machine , elasticity (physics) , artificial intelligence , computer network , operating system , engineering , operations management , materials science , economics , composite material , economic growth
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, OS 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.