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Distributed Computing Systems Workshops (ICDCSW), 2011 31st International Conference on

Date 20-24 June 2011

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Displaying Results 1 - 25 of 70
  • [Front cover]

    Page(s): C1
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  • [Title page i]

    Page(s): i
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  • [Title page iii]

    Page(s): iii
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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - x
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  • Message from the Workshops Co-Chairs

    Page(s): xi
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  • Message from the DCperf 2011 Workshop Organizers

    Page(s): xii
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  • DCperf 2011 Committee Members

    Page(s): xiii - xiv
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  • Message from the SAHNS 2011 Chair

    Page(s): xv - xvi
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  • SAHNS 2011 Committees

    Page(s): xvii - xviii
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  • Message from SIMPLEX'11 Workshop Program Committee Chairs

    Page(s): xix
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  • SIMPLEX'11 Committee Members

    Page(s): xx
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  • Message from the SN 2011 Chairs

    Page(s): xxi
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  • SN 2011 Organizing Committee and Reviewers

    Page(s): xxii - xxiii
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  • Message from ICDCS-SPCC 2011 Workshop Program Co-chairs

    Page(s): xxiv
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  • ICDCS-SPCC 2011 Committees

    Page(s): xxv
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  • Message from the WWASN 2011 Chairs

    Page(s): xxvi
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  • WWASN 2011 Organizing Committee

    Page(s): xxvii
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  • Maximizing Profit in Cloud Computing System via Resource Allocation

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (458 KB) |  | HTML iconHTML  

    With increasing demand for high performance computing and data storage, distributed computing systems have attracted a lot of attention. Resource allocation is one of the most important challenges in the distributed systems specially when the clients have some Service Level Agreements (SLAs) and the total profit in the system depends on how the system can meet these SLAs. In this paper, an SLA-based resource allocation problem for cloud computing is considered and a distributed solution to this problem is presented. The processing, data storage, and communication resources are considered as three dimensions in which optimizations are performed. Simulation results demonstrate that the proposed heuristic algorithm is robust (produces high quality solutions independent of the initial solution provided) and produces solutions very close to the "optimum" (best solution found by Monte Carlo simulation). View full abstract»

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  • Multi-dimensional Resource Integrated Scheduling in a Shared Data Center

    Page(s): 7 - 13
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1711 KB) |  | HTML iconHTML  

    Resource scheduling is crucial to data centers. However, most existing resource scheduling algorithms focus only on one-dimensional resource models, ignoring the fact that multiple resources (e.g. CPU, memory, storage, and network bandwidth) are consumed simultaneously. Competition for multiple resources has become increasingly severe as cloud computing allows uncoordinated and heterogeneous users to share a data center. In this paper, we map such a resource scheduling problem to a bounded multi-dimensional scheduling problem (B-MDKP), taking into account the requirement dependency among multi-dimensional resources. Due to the NP hardness of B-MDKP, we present Multi-dimensional Resource Integrated Scheduling (MRIS), a novel heuristic algorithm to obtain the approximate optimal solution. To demonstrate the advantage of our scheduling scheme, we have implemented MRIS in Haizea, a resource management architecture and made a detailed comparison with existing studies. Our evaluation results show that MRIS achieves high efficiency and high performance for a diverse set of workloads. View full abstract»

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  • Exploiting Resource Usage Patterns for Better Utilization Prediction

    Page(s): 14 - 19
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (417 KB) |  | HTML iconHTML  

    Understanding the resource utilization in computing clouds is critical for efficient resource planning and better operational performance. In this paper, we propose two ways, from microscopic and macroscopic perspectives, to predict the resource consumption for data centers by statistically characterizing resource usage patterns. The first approach focuses on the usage prediction for a specific node. Compared to the basic method of calibrating AR models for CPU usages separately, we find that using both CPU and memory usage data can improve the forecasting performance. The second approach is based on Principal Component Analysis (PCA) to identify resource usage patterns across different nodes. Using the identified patterns, we can reduce the number of parameters for predicting the resource usage on multiple nodes. In addition, using the principal components obtained from PCA, we propose an optimization framework to optimally consolidate VMs into a number of physical servers and in the meanwhile reduce the resource usage variability. The evaluation of the proposed approaches is based on traces collected from a production cloud environment. View full abstract»

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  • Improving Fairness of Quantized Congestion Notification for Data Center Ethernet Networks

    Page(s): 20 - 25
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1795 KB) |  | HTML iconHTML  

    In large-scale data centers, two types of network are implemented: local area networks (LANs) and storage area networks (SANs). To achieve simple network management, integration of these two networks by Ethernet technology is of great interest. A SAN requires a significantly low frame loss rate. To integrate LANs and SANs, a multi-hop Ethernet configuration is generally used, and congestion may occur in traffic hot spots. Therefore, layer-2 congestion control that prevents frame loss in multi-hop Ethernet, Quantized Congestion Notification (QCN), is now discussed in IEEE 802.1Qau. In this paper, we evaluate QCN's throughput performance and reveal a technical problem with fairness among active flows. We also propose an enhancement of QCN's rate increase principle and demonstrate that it improves fairness. View full abstract»

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  • A Probabilistic Approach to Address TCP Incast in Data Center Networks

    Page(s): 26 - 33
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (684 KB) |  | HTML iconHTML  

    Data centers typically host tens of thousands of servers that communicate with each other using high speed network interconnects. While these servers help in servicing millions of clients, their overall performance largely depends on the efficiency of the center's communication fabric. Cost and compatibility reasons however, persuade many data centers to consider Ethernet for their baseline communication fabric. Until recently, Ethernet speeds inside data centers averaged around 100Mbps but the evolution of IEEE 802.3 standards has led to the development of 1 Gbps and 10 Gbps Ethernet. This sudden jump in Ethernet speeds requires proportional scaling of TCP/IP processing for network intensive applications to really benefit from the increased bandwidth. While IP is expected to scale well in this context, TCP is known to have problems supporting very high data rates at very low latencies. One such problem, termed the `Incast', results in gross under-utilization of link capacity in certain many-to-one TCP communication patterns. This paper presents a practical solution to TCP's incast problem. Our proposed technique relies on a probabilistic approach that augments TCP's standard congestion recovery mechanism. Simulation results demonstrate that this technique is effective in avoiding TCP throughput collapse in data center networks. View full abstract»

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  • A Model of Storage I/O Performance Interference in Virtualized Systems

    Page(s): 34 - 39
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (790 KB) |  | HTML iconHTML  

    In this paper, we propose simple performance models to predict the impact of consolidation on the storage I/O performance of virtualized applications. We use a measurement-based approach based on tools such as blktrace and tshark for storage workload characterization in a commercial virtualized solution, namely VMware ESX server. Our approach allows a distinct characterization of read/write performance attributes on a per request level and provides valuable information for parameterization of storage I/O performance models. In particular, based on measures of quantities such as the mean queue-length seen upon arrival by requests, we define simple linear prediction models for the throughput, response times, and mix of read/write requests in consolidation based only on information collected in isolation experiments for the individual virtual machines. View full abstract»

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  • The Edge of Smartness

    Page(s): 40 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (276 KB) |  | HTML iconHTML  

    This paper argues the need for "smart edge" devices to enhance the performance, functionality, and security of data center networks. Three examples, drawn primarily from the prior networking literature, are used to illustrate this point. The first example is the TCP in-cast problem, wherein highly concurrent TCP flows traverse a limited-buffer LAN switch, degrading system throughput. The second example is redundant traffic elimination (RTE), to economize on data movement, particularly over bandwidth-constrained Internet paths. The third example is CPU speed scaling, in which metrics such as energy consumption or economic cost trump throughput or response time metrics. The paper concludes with speculative ideas about other functionality that could also reside at the "smart edge". View full abstract»

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