2,304 research outputs found

    An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers

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    Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.Comment: Submitted for publication consideration for the Journal of Network and Computer Applications (JNCA). Total page: 28. Number of figures: 15 figure

    A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

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    The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

    Optimal VM placement in data centres with architectural and resource constraints

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    Recent advance in virtualisation technology enables service provisioning in a flexible way by consolidating several virtual machines (VMs) into a single physical machine (PM). The inter-VM communications are inevitable when a group of VMs in a data centre provide services in a collaborative manner. With the increasing demands of such intra-data-centre traffics, it becomes essential to study the VM-to-PM placement such that the aggregated communication cost within a data centre is minimised. Such optimisation problem is proved NP-hard and formulated as an integer programming with quadratic constraints in this paper. Different from existing work, our formulation takes into consideration of data-centre architecture, inter-VM traffic pattern, and resource capacity of PMs. Furthermore, a heuristic algorithm is proposed and its high efficiency is extensively validated

    Algorithms for advance bandwidth reservation in media production networks

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    Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

    Strategies for Increased Energy Awareness in Cloud Federations

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    This chapter first identifies three scenarios that current energy aware cloud solutions cannot handle as isolated IaaS, but their federative efforts offer opportunities to be explored. These scenarios are centered around: (i) multi-datacenter cloud operator, (ii) commercial cloud federations, (iii) academic cloud federations. Based on these scenarios, we identify energy-aware scheduling policies to be applied in the management solutions of cloud federations. Among others, these policies should consider the behavior of independent administrative domains, the frequently contradicting goals of the participating clouds and federation wide energy consumption
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