Consumer side resource accounting in cloud computing

PhD ThesisCloud computing services made available to consumers range from providing basic computational resources such as storage and compute power to sophisticated enterprise application services. A common business model is to charge consumers on a pay-per-use basis where they periodically pay for the resources they have consumed. The provider is responsible for measuring and collecting the resource usage data. This approach is termed provider-side accounting. A serious limitation of this approach is that consumers have no choice but to take whatever usage data that is made available by the provider as trustworthy. This thesis investigates whether it is possible to perform consumer-side resource accounting where a consumer independently collects, for a given cloud service, all the data required for calculating billing charges. If this were possible, then consumers will be able to perform reasonableness checks on the resource usage data available from service providers as well as raise alarms when apparent discrepancies are suspected in consumption figures. Two fundamental resources of cloud computing, namely, storage and computing are evaluated. The evaluation exercise reveals that the resource accounting models of popular cloud service providers, such as Amazon, are not entirely suited to consumer-side resource accounting, in that discrepancies between the data collected by the provider and the consumer can occur. The thesis precisely identifies the causes that could lead to such discrepancies and points out how the discrepancies can be resolved. The results from the thesis can be used by service providers to improve their resource accounting models. In particular, the thesis shows how an accounting model can be made strongly consumer–centric so that all the data that the model requires for calculating billing charges can be collected independently by the consumer. Strongly consumer–centric accounting models have the desirable property of openness and transparency, since service users are in a position to verify the charges billed to them.Cultural Affairs Department, Libyan Embassy, Londo

Cloud Index Tracking: Enabling Predictable Costs in Cloud Spot Markets

Cloud spot markets rent VMs for a variable price that is typically much lower than the price of on-demand VMs, which makes them attractive for a wide range of large-scale applications. However, applications that run on spot VMs suffer from cost uncertainty, since spot prices fluctuate, in part, based on supply, demand, or both. The difficulty in predicting spot prices affects users and applications: the former cannot effectively plan their IT expenditures, while the latter cannot infer the availability and performance of spot VMs, which are a function of their variable price. To address the problem, we use properties of cloud infrastructure and workloads to show that prices become more stable and predictable as they are aggregated together. We leverage this observation to define an aggregate index price for spot VMs that serves as a reference for what users should expect to pay. We show that, even when the spot prices for individual VMs are volatile, the index price remains stable and predictable. We then introduce cloud index tracking: a migration policy that tracks the index price to ensure applications running on spot VMs incur a predictable cost by migrating to a new spot VM if the current VM's price significantly deviates from the index price.Comment: ACM Symposium on Cloud Computing 201

InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape