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Abstract. 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 Inter-Cloud 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.

Abstract. This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3 rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.

Abstract. Virtualization technology has become commonplace in modern data centers and cluster systems, often referred as “computing clouds”. In particular, the capability of virtual machine (VM) migration brings multiple benefits such as higher performance, improved manageability and fault tolerance. Moreover, live migration of VMs often allows workload movement with a short service downtime. However, service levels of running applications are likely to be negatively affected during a live VM migration. For this reason, a better understanding of its effects on system performance is highly desirable. In this paper, we present a performance evaluation on the effects of live migration of virtual machines on the performance of applications running inside Xen VMs. Results show that in most cases, migration overhead is acceptable but cannot be disregarded, especially in systems where service availability and responsiveness are governed by strict Service Level Agreements (SLAs). Despite that, there is a high potential for live migration applicability in data centers serving enterprise-class Internet applications. Our results are based on a workload composed of a real application, covering the domain of multi-tier Web 2.0 applications. 1

Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific and business domains. However, the ever increasing energy consumption of computing systems has started to limit further performance growth due to overwhelming electricity bills and carbon dioxide footprints. Therefore, the goal of the computer system design has been shifted to power and energy efficiency. To identify open challenges in the area and facilitate future advancements it is essential to synthesize and classify the research on power and energy-efficient design conducted to date. In this work we discuss causes and problems of high power / energy consumption, and present a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization and data center levels. We survey various key works in the area and map them to our taxonomy to guide future design and development efforts. This chapter is concluded with a discussion of advancements identified in energy-efficient computing and our vision on future

Dedicated computing clusters are typically sized based on an expected average workload over a period of years, rather than on peak workloads, which might exist for relatively short times of weeks or months. Recent work has proposed temporarily adding capacity to dedicated clusters during peak periods, by purchasing additional resources from Infrastructure as a Service (IaaS) providers such as Amazon’s EC2. In this paper, we consider the economics of purchasing such resources by taking advantage of new opportunities offered for renting virtual infrastructure such as the spot pricing model introduced by Amazon. Furthermore, we define different provisioning policies and investigate the use of spot instances compared to normal instances in terms of cost savings and total breach time of tasks in the queue.

Distributed system emulators provide a paramount platform for testing of network protocols and distributed applications in clusters and networks of workstations. However, to allow testers to benefit from these systems, it is necessary an efficient and automatic mapping of hundreds, or even thousands, of virtual nodes to physical hosts—and the mapping of the virtual links between guests to physical paths in the physical environment. In this paper we present a heuristic to map both virtual machines to hosts and virtual links between virtual machines to paths in the real system. We define the problem we are addressing, present the solution for it and evaluate it in different usage scenarios. 1.

Abstract—Advances in Cloud computing opens up many new possibilities for Internet applications developers. Previously, a main concern of Internet applications developers was deployment and hosting of applications, because it required acquisition of a server with a fixed capacity able to handle the expected application peak demand and the installation and maintenance of the whole software infrastructure of the platform supporting the application. Furthermore, server was underutilized because peak traffic happens only at specific times. With the advent of the Cloud, deployment and hosting became cheaper and easier with the use of pay-peruse flexible elastic infrastructure services offered by Cloud providers. Because several Cloud providers are available, each one offering different pricing models and located in different geographic regions, a new concern of application developers is selecting providers and data center locations for applications. However, there is a lack of tools that enable developers to evaluate requirements of large-scale Cloud applications in terms of geographic distribution of both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudAnalyst. It was developed to simulate large-scale Cloud applications with the purpose of studying the behavior of such applications under various deployment configurations. CloudAnalyst helps developers with insights in how to distribute applications among Cloud infrastructures and value added services such as optimization of applications performance and providers incoming with the use of Service Brokers.

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under

Virtual machines (VMs) have become capable enough to emulate full-featured physical machines in all aspects. Therefore, they have become the foundation not only for flexible data center infrastructure but also for commercial Infrastructure-as-a-Service (IaaS) solutions. However, current providers of virtual infrastructure offer simple mechanisms through which users can ask for immediate allocation of VMs. More sophisticated economic and allocation mechanisms are required so that users can plan ahead and IaaS providers can improve their revenue. This paper introduces OpenPEX, a system that allows users to provision resources ahead of time through advance reservations. OpenPEX also incorporates a bilateral negotiation protocol that allows users and providers to come to an agreement by exchanging offers and counter-offers. These functions are made available to users through a web portal and a RESTbased Web service interface. 1