Agent-based resource scheduling in a cloud environment using the intelligent technique

Regarding trading in cloud computing services, Infrastructure As a Service(IaaS) and Software As a Service(SaaS), suppliers and clients of cloud-based software applications have access to an increasingly diverse trading market. How SaaS providers implement application auto-scaling becomes increasingly essential in varied and dynamic environments. SaaS companies employ cloud resources provided by IaaS providers to deliver various SaaS services to their customers. In order to maximize their utility, the best behavioral strategy is to minimize renting costs while providing enough processing capacity to meet customer needs. Different virtual machine (VM) instances are offered by public IaaS providers such as Amazon, each with a unique set of price options. In addition, customer service requests change over time. For SaaS providers, this challenge is first formulated in this research. Then we present a Q-learning-based cloud resource allocation technique that may reduce the waiting time required for allocating resources and thereby minimize the renting costs under dynamic IaaS facility adjustments.