2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)

16-19 May 2016

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  • [Front cover]

    Publication Year: 2016, Page(s): C4
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  • [Title page i]

    Publication Year: 2016, Page(s): i
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  • [Title page iii]

    Publication Year: 2016, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2016, Page(s): iv
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  • Table of contents

    Publication Year: 2016, Page(s):v - xiii
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  • Message from the CCGrid 2016 General Chairs

    Publication Year: 2016, Page(s):xiv - xv
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  • Message from the CCGrid 2016 Program Chairs

    Publication Year: 2016, Page(s): xvi
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  • Organizing Committee

    Publication Year: 2016, Page(s):xvii - xix
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  • Technical Program Committee

    Publication Year: 2016, Page(s):xx - xxiv
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  • List of Reviewers

    Publication Year: 2016, Page(s):xxv - xxix
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  • Keynotes

    Publication Year: 2016, Page(s):xxx - xxxiii
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (212 KB)

    These keynote speeches discuss the following: Time & Energy Efficient Computing; Improving Cloud Utilization via SLO Differentiation; and InfoSymbiotic Systems/DDDAS -Large-Scale Dynamic Data and Large-Scale Big Computing for Smart Systems. View full abstract»

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  • Automatic Communication Optimization of Parallel Applications in Public Clouds

    Publication Year: 2016, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (256 KB) | HTML iconHTML

    One of the most important aspects that influences the performance of parallel applications is the speed of communication between their tasks. To optimize communication, tasks that exchange lots of data should be mapped to processing units that have a high network performance. This technique is called communication-aware task mapping and requires detailed information about the underlying network to... View full abstract»

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  • Tyrex: Size-Based Resource Allocation in MapReduce Frameworks

    Publication Year: 2016, Page(s):11 - 20
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (290 KB) | HTML iconHTML

    Many large-scale data analytics infrastructures are employed for a wide variety of jobs, ranging from short interactive queries to large data analysis jobs that may take hours or even days to complete. As a consequence, data-processing frameworks like MapReduce may have workloads consisting of jobs with heavy-tailed processing requirements. With such workloads, short jobs may experience slowdowns ... View full abstract»

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  • Demand-Aware Power Management for Power-Constrained HPC Systems

    Publication Year: 2016, Page(s):21 - 31
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (775 KB) | HTML iconHTML

    As limited power budget is becoming one of the most crucialchallenges in developing supercomputer systems, hardware overprovisioning which installs larger number of nodes beyond the limitations of the power constraint determinedby Thermal Design Power is an attractive way to design extreme-scale supercomputers. In this design, power consumption of each node should be controlled by power-knobs equi... View full abstract»

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  • Landrush: Rethinking In-Situ Analysis for GPGPU Workflows

    Publication Year: 2016, Page(s):32 - 41
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (764 KB) | HTML iconHTML

    In-situ analysis on the output data of scientific simulations has been made necessary by ever-growing output data volumes and increasing costs of data movement as supercomputing is moving towards exascale. With hardware accelerators like GPUs becoming increasingly common in high end machines, new opportunities arise to co-locate scientific simulations and online analysis performed on the scientifi... View full abstract»

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  • Service Level and Performance Aware Dynamic Resource Allocation in Overbooked Data Centers

    Publication Year: 2016, Page(s):42 - 51
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (567 KB) | HTML iconHTML

    Many cloud computing providers use overbooking to increase their low utilization ratios. This however increases the risk of performance degradation due to interference among co-located VMs. To address this problem we present a service level and performance aware controller that: (1) provides performance isolation for high QoS VMs, and (2) reduces the VM interference between low QoS VMs by dynamica... View full abstract»

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  • DieHard: Reliable Scheduling to Survive Correlated Failures in Cloud Data Centers

    Publication Year: 2016, Page(s):52 - 59
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (273 KB) | HTML iconHTML

    In large scale data centers, a single fault can lead to correlated failures of several physical machines and the tasks running on them, simultaneously. Such correlated failures can severely damage the reliability of a service or a job. This paper models the impact of stochastic and correlated failures on job reliability in a data center. We focus on correlated failures caused by power outages or f... View full abstract»

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  • SHMEMPMI -- Shared Memory Based PMI for Improved Performance and Scalability

    Publication Year: 2016, Page(s):60 - 69
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (829 KB) | HTML iconHTML

    Dense systems with large number of cores per node are becoming increasingly popular. Existing designs of the Process Management Interface (PMI) show poor scalability in terms of performance and memory consumption on such systems with large number of processes concurrently accessing the PMI interface. Our analysis shows the local socket-based communication scheme used by PMI to be a major bottlenec... View full abstract»

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  • DiBA: Distributed Power Budget Allocation for Large-Scale Computing Clusters

    Publication Year: 2016, Page(s):70 - 79
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (344 KB) | HTML iconHTML

    Power management has become a central issue inlarge-scale computing clusters where a considerable amount ofenergy is consumed and a large operational cost is incurredannually. Traditional power management techniques have a centralizeddesign that creates challenges for scalability of computingclusters. In this work, we develop a framework for distributedpower budget allocation that maximizes the ut... View full abstract»

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  • KOALA-F: A Resource Manager for Scheduling Frameworks in Clusters

    Publication Year: 2016, Page(s):80 - 89
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (592 KB) | HTML iconHTML

    Due to the diversity in the applications that run in clusters, many different application frameworks have been developed, such as MapReduce for data-intensive batch jobs and Spark for interactive data analytics. A framework is first deployed in a cluster, and then starts executing a large set of jobs that are submitted over time. When multiple such frameworks with time-varying resource demands are... View full abstract»

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  • Elastic Partition Placement for Non-stationary Graph Algorithms

    Publication Year: 2016, Page(s):90 - 93
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (196 KB) | HTML iconHTML

    Distributed graph platforms like Pregel have usedvertex-centric programming models to process the growing cor-pus of graph datasets using commodity clusters. However, theirregular structure of graphs causes load imbalances acrossmachines, and this is exacerbated for non-stationary graphalgorithms where not all parts of the graph are active at thesame time. As a result, such graph platforms do not ... View full abstract»

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  • In-Memory Caching Orchestration for Hadoop

    Publication Year: 2016, Page(s):94 - 97
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (149 KB) | HTML iconHTML

    In this paper, we investigate techniques to effectively orchestrate HDFS in-memory caching for Hadoop. We first evaluate a degree of benefit which each of various MapReduce applications can get from in-memory caching, i.e. cache affinity. We then propose an adaptive cache local scheduling algorithm that adaptively adjusts the waiting time of a MapReduce job in a queue for a cache local node. We se... View full abstract»

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  • Increasing the Performance of Data Centers by Combining Remote GPU Virtualization with Slurm

    Publication Year: 2016, Page(s):98 - 101
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (171 KB) | HTML iconHTML

    The use of Graphics Processing Units (GPUs) presents several side effects, such as increased acquisition costs as well as larger space requirements. Furthermore, GPUs require a non-negligible amount of energy even while idle. Additionally, GPU utilization is usually low for most applications. Using the virtual GPUs provided by the remote GPU virtualization mechanism may address the concerns associ... View full abstract»

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  • Scheduling In-Situ Analytics in Next-Generation Applications

    Publication Year: 2016, Page(s):102 - 105
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (153 KB) | HTML iconHTML

    Next-generation applications increasingly rely on in situ analytics to guide computation, reduce the amount of I/O performed, and perform other important tasks. Scheduling where and when to run analytics is challenging, however. This paper quantifies the costs and benefits of different approaches to scheduling applications and analytics on nodes in large-scale applications, including space sharing... View full abstract»

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  • CVSS: A Cost-Efficient and QoS-Aware Video Streaming Using Cloud Services

    Publication Year: 2016, Page(s):106 - 115
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1326 KB) | HTML iconHTML

    Video streams, either in form of on-demand streaming or live streaming, usually have to be converted (i.e., transcoded) based on the characteristics of clients' devices (e.g., spatial resolution, network bandwidth, and supported formats). Transcoding is a computationally expensive and time-consuming operation, therefore, streaming service providers currently store numerous transcoded versions of t... View full abstract»

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