Cloud computing promises the flexible delivery of computing services in a pay-as-you-go manner. It allows customers to easily scale their infrastructure and save on the overall cost of operation. However Cloud service offerings can only thrive if customers are satisfied with service performance. Allowing instantaneous access and flexible scaling while maintaining the service levels and offering competitive prices poses a significant challenge to Cloud Computing providers. Furthermore services will remain available in the long run only if this business generates a stable revenue stream. To address these challenges we introduce novel policy-based service admission control models that aim at maximizing the revenue of Cloud providers while taking informational uncertainty regarding resource requirements into account. Our evaluation shows that policy-based approaches statistically significantly outperform first come first serve approaches, which are still state of the art. Furthermore the results give insights in how and to what extent uncertainty has a negative impact on revenue.
Abstract. As competition on global markets increases the vision of utility computing gains more and more interest. To attract more providers it is crucial to improve the performance in commercialization of resources. This makes it necessary to not only base components on technical aspects, but also to include economical aspects in their design. This work presents an framework for an Economically Enhanced Resource Manager (EERM) which features enhancements to technical resource management like dynamic pricing and client classification. The introduced approach is evaluated considering various economic design criteria and example scenarios. Our preliminary results, e.g. an increase in achieved revenue from 77% to 92% of the theoretic maximum in our first scenario, show that our approach is very promising.
Commercialization of Grid resources will become more and more important as utility computing and the deployment of Grids gains momentum. This results in the necessity to not only base Grid components on technical aspects, but also to include economical aspects in their design. This paper presents a framework that links technical and economical aspects to the management of computational resources. Economic enhancements like dynamic pricing and client classification are introduced based on a technical resource management environment and positioned within this resulting in a proposed architecture for an Economically Enhanced Resource Manager (EERM). The introduced approach is evaluated considering various economic design criteria and example scenarios.
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