Abstract
Resource management for cloud computing environments that are characterized by many layers emerges as a critical task for cloud computing providers. Such providers are compelled by the demands and strategies of stochastic customers to adopt dynamic resource management for the top-bottom scaling of the cloud resources on the basis of variable needs. Resource management in the infrastructure as a service layer relies on virtual machine (VM) characteristics, such as estimated VM classes. Given that a cloud provider offers a variety of VM classes that differ as regards the size of computing resources (e.g., central processing unit, memory, and input/output devices), optimizing cloud resources to maximize cloud revenue is a challenging dilemma. More specifically, the dynamic management of resources in cloud spot markets is confronted with various severe obstacles. In consideration of these issues, this study investigated a dynamic resource management model for cloud spot markets and put forward an efficient model that manages spare resources for the purpose of expanding cloud revenue. The model estimates the available spare capacity of a spot market, evaluates the maximum expected revenue of stagnant VMs on the basis estimated cumulative capacity, and locates the optimum VM combinations that bear complementary workloads and capacities and can coexist in a certain host. Our model also improves the understanding of cloud resource scaling and generates inferences that can be adopted in managing cloud resources for all layers as well as Reserved and On-Demand markets.
Highlights
Many cloud computing providers that offer infrastructure as a service (IaaS) implement various pricing schemes, such as on-demand, reserved, and spot pricing
We emphasize that the proposed dynamic resource management method is meant exclusively for stagnant resources through which the greatest return is earned in a spot market
We closely followed the steps and techniques involved in dynamic resource allocation for spot markets (DRASM) that were discussed in Section III to demonstrate the effects of dynamic allocation in view of aggregate capacity
Summary
Many cloud computing providers that offer infrastructure as a service (IaaS) implement various pricing schemes, such as on-demand, reserved, and spot pricing. Amazon Elastic Compute Cloud [1] adopts these schemes, as does Microsoft, which provides Rreserved and Pay-as-you-go pricing for its Azure virtual machine instances [2]. Spot pricing for VM instances has raised considerable debate about accompanying techniques and objectives Such discussion has been directed, for example, toward Amazon and Google cloud services. The authors concluded that these companies offer VM instances for a limited time at a dynamic price, depending on the state of cloud resources and regardless of claims that they provide cost-saving services to customers. The key role of resource allocation is to adjust a given set of assets in response to the law of supply and demand, for the purpose of maximizing utilization levels on one hand and minimizing resource consumption on the other These tasks are accomplished in service of expanded cloud profit.
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