Federations among sets of Cloud Providers (CPs), whereby a set of CPs agree to mutually use their own resources to run the VMs of other CPs, are considered a promising solution to the problem of reducing the energy cost. In this paper, we address the problem of federation formation for a set of CPs, whose solution is necessary to exploit the potential of cloud federations for the reduction of the energy bill. We devise a distributed algorithm, based on cooperative game theory, that allows a set of CPs to cooperatively set up their federations in such a way that their individual profit is increased with respect to the case in which they work in isolation, and we show that, by using our algorithm and the proposed CPs' utility function, they are able to self-organize into Nash-stable federations and, by means of iterated executions, to adapt themselves to environmental changes. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.
Emerging Cloud computing infrastructures provide computing resources on demand based on postpaid principles. For example, the RESERVOIR project develops an infrastructure capable of delivering elastic capacity that can automatically be increased or decreased in order to cost-efficiently fulfill established Service Level Agreements. This infrastructure also makes it possible for a data center to extend its total capacity by subcontracting additional resources from collaborating data centers, making the infrastructure a federation of Clouds. For accounting and billing, such infrastructures call for novel approaches to perform accounting for capacity that varies over time and for services (or more precisely virtual machines) that migrate between physical machines or even between data centers. For billing, needs arise for new approaches to simultaneously manage postpaid and prepaid payment schemes for capacity that varies over time in response to user needs. In this paper, we outline usage scenarios and a set of requirements for such infrastructures, and propose an accounting and billing architecture to be used within RESERVOIR. Even though the primary focus for this architecture is accounting and billing between resource consumers and infrastructure provides, future support for inter-site billing is also taken into account. Cloud infrastructure providers may form Cloud federations to cope with peaks in resource demand and to make large-scale service management simpler for service providers. To realize Cloud federations, a number of technical and managerial difficulties need to be solved. We present ongoing work addressing three related key management topics, namely, specification, scheduling, and monitoring of services. Service providers need to be able to influence how their resources are placed in Cloud federations, as federations may cross national borders or include companies in direct competition with the service provider. Based on related work in the RESERVOIR project, we propose a way to define service structure and placement restrictions using hierarchical directed acyclic graphs. We define a model for scheduling in Cloud federations that abides by the specified placement constraints and minimizes the risk of violating Service-Level Agreements. We present a heuristic that helps the model determine which virtual machines (VMs) are suitable candidates for migration. To aid the scheduler, and to provide unified data to service providers, we also propose a monitoring data distribution architecture that introduces cross-site compatibility by means of semantic metadata annotations. Distributed resource allocation is a very important and complex problem in emerging horizontal dynamic cloud federation (HDCF) platforms, where different cloud providers (CPs) collaborate dynamically to gain economies of scale and enlargements of their virtual machine (VM) infrastructure capabilities in order to meet consumer requirements. HDCF platforms differ from the existing vertical supply chain federation (VSCF) models in terms of establishing federation and dynamic pricing. There is a need to develop algorithms that can capture this complexity and easily solve distributed VM resource allocation problem in a HDCF platform. In this paper, we propose a cooperative game-theoretic solution that is mutually beneficial to the CPs. It is shown that in non-cooperative environment, the optimal aggregated benefit received by the CPs is not guaranteed. We study two utility maximizing cooperative resource allocation games in a HDCF environment. We use price-based resource allocation strategy and present both centralized and distributed algorithms to find optimal solutions to these games. Various simulations were carried out to verify the proposed algorithms. The simulation results demonstrate that the algorithms are effective, showing robust performance for resource allocation and requiring minimal computation time. Balava is a new system for managing computations that span multiple clouds and involve data with confidentiality constraints. This paper describes the design, implementation and initial performance evaluation of Balava building-blocks. We detail the run-time developed to interconnect private and public clouds, and present a storage overlay built on top of this run-time. To support low-overhead execution of Balava computations, we are investigating alternative approaches to virtualization. We present a new hyper visor that supports light-weight virtual environments, while also preserving application binary interfaces. In order to answer the question whether or not to utilize the cloud for processing, this paper aims at identifying characteristics of potential cloud beneficiaries and advisable actions to actually gain financial benefits. A game-theoretic model of an Infrastructure-as-a-Service (IaaS) cloud market, covering dynamics of pricing and usage, is suggested. Incorporating the possibility of hybrid clouds (clouds plus own infrastructure) into this model turns out essential for cloud computing being significantly in favor of not only the provider but the client as well. Parameters like load profiles and economy of scale have a huge effect on likely future pricing as well as on a cost-optimal split-up of client demand between a client's own data center and a public cloud service. As cloud computing becomes more predominant, the problem of scalability has become critical for cloud computing providers. The cloud paradigm is attractive because it offers a dramatic reduction in capital and operation expenses for consumers. We present fundamental challenges for scalable and dependable service platforms and architectures that enable flexible and dynamic provisioning of cloud services. Our findings are incorporated in a toolkit targeting the cloud service and infrastructure providers. The innovations behind the toolkit are aimed at optimizing the whole service life cycle, including service construction, deployment, and operation, on a basis of aspects such as trust, risk, eco-efficiency and cost. Notably, adaptive self-preservation is crucial to meet predicted and unforeseen changes in resource requirements. By addressing the whole service life cycle, taking into account several cloud architectures, and by taking a holistic approach to sustainable service provisioning, the toolkit aims to provide a foundation for a reliable, sustainable, and trustful cloud computing industry. Current large distributed systems allow users to share and trade resources. In cloud computing, users purchase different types of resources from one or more resource providers using a fixed pricing scheme. Federated clouds, a topic of recent interest, allows different cloud providers to share resources for increased scalability and reliability. However, users and providers of cloud resources are rational and maximize their own interest when consuming and contributing shared resources. In this paper, we present a dyanmic pricing scheme suitable for rational users requests containing multiple resource types. Using simulations, we compare the efficiency of our proposed strategy-proof dynamic scheme with fixed pricing, and show that user welfare and the percentage of successful requests is increased by using dynamic pricing. As a key component in a modern datacenter, the cloud operating system is responsible for managing the physical and virtual infrastructure, orchestrating and commanding service provisioning and deployment, and providing federation capabilities for accessing and deploying virtual resources in remote cloud infrastructures. Cloud Federation is a recent paradigm that helps Infrastructure as a Service (IaaS) providers to overcome resource limitation during spikes in demand for Virtual Machines (VMs) by outsourcing requests to other federation members. IaaS providers also have the option of terminating spot VMs, i.e, cheaper VMs that can be canceled to free resources for more profitable VM requests. By both approaches, providers can expect to reject less profitable requests. For IaaS providers, pricing and profit are two important factors, in addition to maintaining a high Quality of Service (QoS) and utilization of their resources to remain in the business. For this, a clear understanding of the usage pattern, types of requests, and infrastructure costs are necessary while making decisions to terminate spot VMs, outsourcing or contributing to the federation. In this paper, we propose policies that help in the decision-making process to increase resources utilization and profit. Simulation results indicate that the proposed policies enhance the profit, utilization, and QoS (smaller number of rejected VM requests) in a Cloud federation environment.