Mitigating the Effects of Partial Resource Failures for Cloud Providers

Competition for users on a global market is fierce, forcing enterprises to provide for better, faster services while offering the same more cheaply. At the same time, users choose to remain oblivious of the infrastructure behind the service – only demanding that it works. Cloud service failures and inefficient management of such failures can result in significant financial cost, loss of reputation for providers, and drive key customers away. At the same time failure situations can never be completely avoided. To mitigate their effects we present a decision model for providers to help them decide which jobs to keep running and which to cancel in order to minimize loss of revenue and key customers during partial resource failures. The results of the evaluation of the model and its extension show its ability to significantly improve revenue. Furthermore the model can also help to reduce the number of cancelled jobs

CLOUD SERVICE REVENUE MANAGEMENT

Successful Internet service offerings can only thrive if customers are satisfied with service performance. While large service providers can usually cope with fluctuations of customer visits retaining acceptable Quality of Service, small and medium-sizes enterprises face a big challenge due to limited resources in the IT infrastructure. Popular services, such as justin.tv and SmugMug, rely on external resources provided by cloud computing providers in order to satisfy their customers demands at all times. The paradigm of cloud computing refers to the delivery model of computing services as a utility in a pay-as-you-go manner. In this paper, we provide and computationally evaluate decision models and policies that can help cloud computing providers increase their revenue under the realistic assumption of scarce resources and under both informational certainty and uncertainty of customers? resource requirement predictions. Our results show that in both cases under certainty and under uncertainty applying the dynamic pricing policy significantly increases revenue while using the client classification policy substantially reduces revenue. We also show that, for all policies, the presence of uncertainty causes losses in revenue; when the client classification policy is applied, losses can even amount to more than 8%

Serious Gaming – Spiele als experimentgestützte Evaluationsmethode

Diese Arbeit präsentiert das Konzept des Serious Gaming, eine experimentelle Evaluations-methode. Serious Gaming kann traditionelle Evaluationsmethoden ergänzen und das Evalua-tionsergebnis verbessern. Web 2.0 bietet hierbei großes Potenzial zur Probandenakquirierung. In dieser Arbeit wird das Konzept erläutert, die Ergänzungsmöglichkeiten traditioneller Methoden aufgezeigt, ein Leitfaden für die Konstruktion eines Serious Game entwickelt, die Distribution im Web 2.0 erläutert und das Konzept anhand einer Fallstudie illustriert. Die Fallstudie offenbart empirische Ergebnisse zur Eignung des Web 2.0 zur Spieldistribution

Extended resource management using client classification and economic enhancements

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.Postprint (published version

MANAGEMENT OF CLOUD INFRASTRUCTURES: POLICY-BASED REVENUE OPTIMIZATION

Competition on global markets forces many enterprises to make use of new applications, reduce process times and at the same time cut the costs of their IT-infrastructure. To achieve this, it is necessary to maintain a high degree of flexibility with respect to the IT-infrastructure. Facing this challenge the idea of Cloud computing has been gaining interest lately. Cloud services can be accessed on demand without knowledge of the underlying infrastructure and have already succeeded in helping companies deploy products faster. Using Cloud services the New York Times managed to convert scanned images containing 11 million articles into PDF within 24 hours at a cost of merely 240 US-$. However Cloud providers will only offer their services, if they can realize sufficient benefit. To achieve this, the efficiency of Cloud infrastructure management must be increased. To this end we propose the use of concepts from revenue management and further enhancements

A model of preference elicitation: The case of distributed resource allocation

Market mechanisms are deemed promising for distributed resource allocation settings by explicitly involving users into the allocation process. The market considers the users’ and providers’ valuations to generate efficient resource allocations and prices. In theory, valuations are assumed to be known to the user. In practice, however, this is not the case. It is a complex burden for both users and providers to assess their true valuation for a certain combination of resources and services and to efficiently communicate this valuation to the market. This paper contributes to the theory of designing distributed allocation models in that (i) we propose a model for preference elicitation, which allows users and providers to assess their valuations as a function of their resource requirements and strategic considerations, (ii) we show how this model can be encoded within so-called bidding agents which interact with the market on behalf of the user, and (iii) we evaluate our approach in a numerical experiment to illustrate how the bidding agent adapts to the dynamic market situation. As this evaluation shows, the model outperforms technical schedulers and can thus be used for decision support in electronic markets

Revenue Management for Cloud Computing Providers: Decision Models for Service Admission Control under Non-probabilistic Uncertainty

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. Allow-ing 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 mod-els that aim at maximizing the revenue of Cloud providers while taking in-formational uncertainty regarding resource requirements into account. Our evaluation shows that policy-based approaches statistically significantly out-perform 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