2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

1-4 May 2018

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  • [Title page i]

    Publication Year: 2018, Page(s): 1
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  • [Title page iii]

    Publication Year: 2018, Page(s): 3
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  • [Copyright notice]

    Publication Year: 2018, Page(s): 4
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  • Table of contents

    Publication Year: 2018, Page(s):5 - 14
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  • Message from the General Chair

    Publication Year: 2018, Page(s):15 - 16
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  • Message from the Program Chair

    Publication Year: 2018, Page(s):17 - 18
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  • CCGrid 2018 Committees

    Publication Year: 2018, Page(s):19 - 24
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  • Steering Committee

    Publication Year: 2018, Page(s):25 - 30
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  • External Reviewers

    Publication Year: 2018, Page(s):31 - 32
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  • SCALE Committee

    Publication Year: 2018, Page(s): 33
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  • AHPAMA Committee

    Publication Year: 2018, Page(s): 34
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  • DBDM Committee

    Publication Year: 2018, Page(s): 35
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  • Keynotes

    Publication Year: 2018, Page(s):36 - 42
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (277 KB)

    Provides an abstract for each of the keynote presentations and may include a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Sponsors

    Publication Year: 2018, Page(s): 43
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  • A Network-Aware Scheduler in Data-Parallel Clusters for High Performance

    Publication Year: 2018, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (229 KB) | HTML iconHTML

    In spite of many shuffle-heavy jobs in current commercial data-parallel clusters, few previous studies have considered the network traffic in the shuffle phase, which contains a large amount of data transfers and may adversely affect the cluster performance. In this paper, we propose a network-aware scheduler (NAS) that handles two main challenges associated with the shuffle phase for high perform... View full abstract»

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  • Bandwidth Scheduling with Flexible Multi-paths in High-Performance Networks

    Publication Year: 2018, Page(s):11 - 20
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (364 KB) | HTML iconHTML

    Modern data-intensive applications require the transfer of big data over high-performance networks (HPNs) through bandwidth reservation for various purposes such as data storage and analysis. The key performance metrics for bandwidth scheduling include the utilization of network resources and the satisfaction of user requests. In this paper, for a given batch of Deadline-Constrained Bandwidth Rese... View full abstract»

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  • CAVA: Exploring Memory Locality for Big Data Analytics in Virtualized Clusters

    Publication Year: 2018, Page(s):21 - 30
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (418 KB) | HTML iconHTML

    Running big data analytics frameworks in the cloud is becoming increasingly important, but their resource managers in the current form are not designed to consider virtualized environments. In this work, we investigate various levels of data locality in a virtualized environment, ranging from rack locality to memory locality. Exploiting extra fine-grained levels of data locality in a virtualized e... View full abstract»

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  • Multi-Level Load Balancing with an Integrated Runtime Approach

    Publication Year: 2018, Page(s):31 - 40
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (861 KB) | HTML iconHTML

    The recent trend of increasing numbers of cores per chip has resulted in vast amounts of on-node parallelism. These high core counts result in hardware variability that introduces imbalance. Applications are also becoming more complex, re-sulting in dynamic load imbalance. Load imbalance of any kind can result in loss of performance and system utilization. We address the challenge of handling both... View full abstract»

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  • RPPC: A Holistic Runtime System for Maximizing Performance Under Power Capping

    Publication Year: 2018, Page(s):41 - 50
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (305 KB) | HTML iconHTML

    Maximizing performance in power-constrained computing environments is highly important in cloud and datacenter computing. To achieve the best possible performance of parallel applications under power capping, it is crucial to execute them with the optimal concurrency level and cross-component power allocation between CPUs and memory. Despite extensive prior works, it still remains unexplored to in... View full abstract»

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  • Enhancing Efficiency of Hybrid Transactional Memory Via Dynamic Data Partitioning Schemes

    Publication Year: 2018, Page(s):51 - 61
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (307 KB) | HTML iconHTML

    Transactional Memory (TM) is an emerging paradigm that promises to significantly ease the development of parallel programs. Hybrid TM (HyTM) is probably the most promising implementation of the TM abstraction, which seeks to combine the high efficiency of hardware implementations (HTM) with the robustness and flexibility of software-based ones (STM). Unfortunately, though, existing Hybrid TM syste... View full abstract»

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  • Uncertainty-Aware Elastic Virtual Machine Scheduling for Stream Processing Systems

    Publication Year: 2018, Page(s):62 - 71
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1192 KB) | HTML iconHTML

    Stream processing systems deployed on the cloud need to be elastic to effectively accommodate workload variations over time. Performance models can predict maximum sustainable throughput (MST) as a function of the number of VMs allocated. We present a scheduling framework that incorporates three statistical techniques to improve Quality of Service (QoS) of cloud stream processing systems: (i) unce... View full abstract»

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  • Adaptive Energy-Aware Scheduling of Dynamic Event Analytics Across Edge and Cloud Resources

    Publication Year: 2018, Page(s):72 - 82
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (922 KB) | HTML iconHTML

    The growing deployment of sensors as part of Internet of Things (IoT) is generating thousands of event streams. Complex Event Processing (CEP) queries offer a useful paradigm for rapid decision-making over such data sources. While often centralized in the Cloud, the deployment of capable edge devices on the field motivates the need for cooperative event analytics that span Edge and Cloud computing... View full abstract»

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  • First Hop Mobile Offloading of DAG Computations

    Publication Year: 2018, Page(s):83 - 92
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (425 KB) | HTML iconHTML

    In recent years, Mobile Cloud Computing (MCC) has been proposed to increase battery lifetime of mobile devices. However, offloading on Cloud infrastructures may be infeasible for latency critical applications, due to the geographical distribution of Cloud data centers that increases offloading time. In this paper, we investigate the use of Mobile Edge Cloud Offloading (MECO), namely offloading to ... View full abstract»

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  • bQueue: A Coarse-Grained Bucket QoS Scheduler

    Publication Year: 2018, Page(s):93 - 102
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (319 KB) | HTML iconHTML

    We consider the problem of providing QoS guarantees in a clustered storage system whose data is distributed over multiple server nodes. Storage objects are encapsulated in a single logical bucket and QoS is provided at the level of buckets. The service that a single bucket receives is the aggregate of the service it receives at the nodes holding its constituent objects. The service depends on indi... View full abstract»

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  • SuperCell: Adaptive Software-Defined Storage for Cloud Storage Workloads

    Publication Year: 2018, Page(s):103 - 112
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (443 KB) | HTML iconHTML

    The explosive growth of data due to the increasing adoption of cloud technologies in the enterprise has created a strong demand for more flexible, cost-effective, and scalable storage solutions. Many storage systems, however, are not well matched to the workloads they service due to the difficulty of configuring the storage system optimally a priori with only approximate knowledge of the workload ... View full abstract»

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