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

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 withprevailing policies. This situation remains a ignificant concern for the majority of cloudproviders, as they face challenges in selecting the correct resource scheduling lgorithmfor a particular workload. The authors use the emergent artificial intelligence algorithmsdeep RM2, deep reinforcement learning, and deep reinforcement learning for Taas