One of the key problems in migrating multi-component enterprise applications to Clouds is selecting the best mix of VM images and Cloud infrastructure services. A migration process has to ensure that Quality of Service (QoS) requirements are met, while satisfying conflicting selection criteria, e.g. throughput and cost. When selecting Cloud services, application engineers must consider heterogeneous sets of criteria and complex dependencies across multiple layers impossible to resolve manually. To overcome this challenge, we present the generic recommender framework CloudGenius and an implementation that leverage well known multi-criteria decision making technique Analytic Hierarchy Process to automate the selection process based on a model, factors, and QoS requirements related to enterprise applications. In particular, we introduce a structured migration process for multi-component enterprise applications, clearly identify the most important criteria relevant to the selection problem and present a multi-criteria-based selection algorithm. Experiments with the software prototype CumulusGenius show time complexities.
Cloud computing promises a radical shift in the provisioning of computing resources within the enterprise. This paper describes the challenges that decision makers face when assessing the feasibility of the adoption of cloud computing in their organizations, and describes our Cloud Adoption Toolkit, which has been developed to support this process. The toolkit provides a framework to support decision makers in identifying their concerns, and matching these concerns to appropriate tools techniques that can be used to address them. Cost Modeling is the most mature tool in the toolkit, and this paper shows its effectiveness by demonstrating how practitioners can use it to examine the costs of deploying their IT systems on the cloud. The Cost Modeling tool is evaluated using a case study of an organization that is considering the migration of some of its IT systems to the cloud. The case study shows that running systems on the cloud using a traditional ‘always on’ approach can be less cost effective, and the elastic nature of the cloud has to be used to reduce costs. Therefore, decision makers have to model the variations in resource usage and their systems' deployment options to obtain accurate cost estimates. Copyright © 2011 John Wiley & Sons, Ltd. This paper describes two tools that aim to support decision making during the migration of IT systems to the cloud. The first is a modeling tool that produces cost estimates of using public IaaS clouds. The tool enables IT architects to model their applications, data and infrastructure requirements in addition to their computational resource usage patterns. The tool can be used to compare the cost of different cloud providers, deployment options and usage scenarios. The second tool is a spreadsheet that outlines the benefits and risks of using IaaS clouds from an enterprise perspective, this tool provides a starting point for risk assessment. Two case studies were used to evaluate the tools. The tools were useful as they informed decision makers about the costs, benefits and risks of using the cloud. Cloud computing is a computing paradigm which allows access of computing elements and storages on-demand over the Internet. Virtual Appliances, pre-configured, ready-to-run applications are emerging as a breakthrough technology to solve the complexities of service deployment on Cloud infrastructure. However, an automated approach to deploy required appliances on the most suitable Cloud infrastructure is neglected by previous works which is the focus of this work. In this paper, we propose an effective architecture using ontology-based discovery to provide QoS aware deployment of appliances on Cloud service providers. In addition, we test our approach on a case study and the result shows the efficiency and effectiveness of the proposed work.