761 research outputs found

    Cloud Ready Desktop Virtualization Solution

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    Cloud computing is a relatively new set of technologies that can allow businesses to easily scale their computing resources and improve responsiveness to customer needs. This has held true for application, server, and desktop virtualization. Desktop virtualization in particular provides a means to solve many of the traditional challenges associated with deploying and maintaining large business workstation environments, including centralized data management, rapid deployment of workstations, and centralized updating. VMware, a longtime leader in the virtualization sector, offers a desktop virtualization platform that is widely considered to be the best in class. This paper explores the process of building a desktop virtualization solution using VMware View, a cloud-ready desktop virtualization solution from VMware

    Multi-dimensional optimization for cloud based multi-tier applications

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    Emerging trends toward cloud computing and virtualization have been opening new avenues to meet enormous demands of space, resource utilization, and energy efficiency in modern data centers. By being allowed to host many multi-tier applications in consolidated environments, cloud infrastructure providers enable resources to be shared among these applications at a very fine granularity. Meanwhile, resource virtualization has recently gained considerable attention in the design of computer systems and become a key ingredient for cloud computing. It provides significant improvement of aggregated power efficiency and high resource utilization by enabling resource consolidation. It also allows infrastructure providers to manage their resources in an agile way under highly dynamic conditions. However, these trends also raise significant challenges to researchers and practitioners to successfully achieve agile resource management in consolidated environments. First, they must deal with very different responsiveness of different applications, while handling dynamic changes in resource demands as applications' workloads change over time. Second, when provisioning resources, they must consider management costs such as power consumption and adaptation overheads (i.e., overheads incurred by dynamically reconfiguring resources). Dynamic provisioning of virtual resources entails the inherent performance-power tradeoff. Moreover, indiscriminate adaptations can result in significant overheads on power consumption and end-to-end performance. Hence, to achieve agile resource management, it is important to thoroughly investigate various performance characteristics of deployed applications, precisely integrate costs caused by adaptations, and then balance benefits and costs. Fundamentally, the research question is how to dynamically provision available resources for all deployed applications to maximize overall utility under time-varying workloads, while considering such management costs. Given the scope of the problem space, this dissertation aims to develop an optimization system that not only meets performance requirements of deployed applications, but also addresses tradeoffs between performance, power consumption, and adaptation overheads. To this end, this dissertation makes two distinct contributions. First, I show that adaptations applied to cloud infrastructures can cause significant overheads on not only end-to-end response time, but also server power consumption. Moreover, I show that such costs can vary in intensity and time scale against workload, adaptation types, and performance characteristics of hosted applications. Second, I address multi-dimensional optimization between server power consumption, performance benefit, and transient costs incurred by various adaptations. Additionally, I incorporate the overhead of the optimization procedure itself into the problem formulation. Typically, system optimization approaches entail intensive computations and potentially have a long delay to deal with a huge search space in cloud computing infrastructures. Therefore, this type of cost cannot be ignored when adaptation plans are designed. In this multi-dimensional optimization work, scalable optimization algorithm and hierarchical adaptation architecture are developed to handle many applications, hosting servers, and various adaptations to support various time-scale adaptation decisions.Ph.D.Committee Chair: Pu, Calton; Committee Member: Liu, Ling; Committee Member: Liu, Xue; Committee Member: Schlichting, Richard; Committee Member: Schwan, Karsten; Committee Member: Yalamanchili, Sudhaka

    Two levels autonomic resource management in virtualized IaaS

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    International audienceVirtualized cloud infrastructures are very popular as they allow resource mutualization and therefore cost reduction. For cloud providers, minimizing the number of used resources is one of the main services that such environments must ensure. Cloud customers are also concerned with the minimization of used resources in the cloud since they want to reduce their invoice. Thus, resource management in the cloud should be considered by the cloud provider at the virtualization level and by the cloud customers at the application level. Many research works investigate resource management strategies in these two levels. Most of them study virtual machine consolidation (according to the virtualized infrastructure utilization rate) at the virtualized level and dynamic application sizing (according to its workload) at the application level. However, these strategies are studied separately. In this article, we show that virtual machine consolidation and dynamic application sizing are complementary. We show the efficiency of the combination of these two strategies, in reducing resource usage and keeping an application’s Quality of Service. Our demonstration is done by comparing the evaluation of three resource management strategies (implemented at the virtualization level only, at the application level only, or complementary at both levels) in a private cloud infrastructure, hosting typical JEE web applications (evaluated with the RUBiS benchmark)

    Challenges for the comprehensive management of cloud services in a PaaS framework

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    The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its lifecycle management, a one stop shop for Cloud services and a PaaS level resource management featuring elasticity. 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud-aware immigrant technologies

    Elastic Highly Available Cloud Computing

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    High availability and elasticity are two the cloud computing services technical features. Elasticity is a key feature of cloud computing where provisioning of resources is closely tied to the runtime demand. High availability assure that cloud applications are resilient to failures. Existing cloud solutions focus on providing both features at the level of the virtual resource through virtual machines by managing their restart, addition, and removal as needed. These existing solutions map applications to a specific design, which is not suitable for many applications especially virtualized telecommunication applications that are required to meet carrier grade standards. Carrier grade applications typically rely on the underlying platform to manage their availability by monitoring heartbeats, executing recoveries, and attempting repairs to bring the system back to normal. Migrating such applications to the cloud can be particularly challenging, especially if the elasticity policies target the application only, without considering the underlying platform contributing to its high availability (HA). In this thesis, a Network Function Virtualization (NFV) framework is introduced; the challenges and requirements of its use in mobile networks are discussed. In particular, an architecture for NFV framework entities in the virtual environment is proposed. In order to reduce signaling traffic congestion and achieve better performance, a criterion to bundle multiple functions of virtualized evolved packet-core in a single physical device or a group of adjacent devices is proposed. The analysis shows that the proposed grouping can reduce the network control traffic by 70 percent. Moreover, a comprehensive framework for the elasticity of highly available applications that considers the elastic deployment of the platform and the HA placement of the application’s components is proposed. The approach is applied to an internet protocol multimedia subsystem (IMS) application and demonstrate how, within a matter of seconds, the IMS application can be scaled up while maintaining its HA status
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