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Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on

Date 13-16 May 2013

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Displaying Results 1 - 25 of 118
  • [Front cover]

    Publication Year: 2013, Page(s): C4
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  • [Title page i]

    Publication Year: 2013, Page(s): i
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  • [Title page iii]

    Publication Year: 2013, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2013, Page(s): iv
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  • Table of contents

    Publication Year: 2013, Page(s):v - xiii
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  • Message from the CCGrid 2013 General Chair

    Publication Year: 2013, Page(s):xiv - xv
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  • Message from the CCGrid 2013 Program Chair

    Publication Year: 2013, Page(s):xvi - xvii
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  • CCGrid 2013 Organization

    Publication Year: 2013, Page(s):xviii - xix
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  • Steering Committee

    Publication Year: 2013, Page(s): xx
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  • Committees

    Publication Year: 2013, Page(s):xxi - xxv
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  • External reviewers

    Publication Year: 2013, Page(s):xxvi - xxvii
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  • Programming Models for High-Performance Computing

    Publication Year: 2013, Page(s): 1
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (116 KB) | HTML iconHTML

    The first version of the MPI standard was released in November 1993. At the time, many of the authors of this standard, myself included, viewed MPI as a temporary solution, to be used until it is replaced by a good programming language for distributed memory systems. Almost twenty years later, MPI is the main programming model for High-Performance Computing, and practically all HPC applications us... View full abstract»

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  • Exploiting per user information for supercomputing workload prediction requires care

    Publication Year: 2013, Page(s):2 - 9
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (211 KB) | HTML iconHTML

    Efficient management of supercomputing facilities requires estimates of future workload based on past user behaviour. For supercomputers with large numbers of users, aggregate user behaviour is commonly assumed to be best in prediction of future workloads, however for systems with smaller numbers of users the question arises as to whether it is still suitable or if benefits can be derived from mon... View full abstract»

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  • Building an Elastic Cloud out of Small Datacenters

    Publication Year: 2013, Page(s):10 - 17
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (385 KB) | HTML iconHTML

    Cloud providers may operate large-scale data centers in a few locations. We argue that deploying many small-scale data centers at network edge can significantly improve user experience in terms of latency. Small-scale data centers, however, may not be able to provide elastic services. In this paper, we investigate distributed small-scale data centers with load reallocation where jobs that cannot b... View full abstract»

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  • Simulating Application Workflows and Services Deployed on the European Grid Infrastructure

    Publication Year: 2013, Page(s):18 - 25
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (227 KB) | HTML iconHTML

    This paper presents an end-to-end SimGrid-based simulation of a Monte-Carlo computation deployed on the European Grid Infrastructure. We describe a simulation framework allowing to replay executions from real traces. Middleware services, namely the file catalog, storage elements, the DIRAC pilot-job system, and the MOTEUR workflow engine are simulated by SimGrid processes. The deployment of pilot ... View full abstract»

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  • Optimizing Large Data Transfers over 100Gbps Wide Area Networks

    Publication Year: 2013, Page(s):26 - 33
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (981 KB) | HTML iconHTML

    The Advanced Networking Initiative (ANI) project from the Energy Services Network provides a 100 Gbps test bed, which offers the opportunity for evaluating applications and middleware used by scientific experiments. This test bed is a prototype of a 100 Gbps wide-area network backbone, which links several Department of Energy (DOE) national laboratories, universities and other research institution... View full abstract»

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  • Automated, Elastic Resource Provisioning for NoSQL Clusters Using TIRAMOLA

    Publication Year: 2013, Page(s):34 - 41
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (505 KB) | HTML iconHTML

    This work presents TIRAMOLA, a cloud-enabled, open-source framework to perform automatic resizing of NoSQL clusters according to user-defined policies. Decisions on adding or removing worker VMs from a cluster are modeled as a Markov Decision Process and taken in real-time. The system automatically decides on the most advantageous cluster size according to user-defined policies, it then proceeds o... View full abstract»

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  • Autoflex: Service Agnostic Auto-scaling Framework for IaaS Deployment Models

    Publication Year: 2013, Page(s):42 - 49
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (474 KB) | HTML iconHTML

    Elasticity is a key property to reduce the costs associated with running services in cloud systems that employ an infrastructure-as-a-service (IaaS) deployment model. However, to be able to exploit this property, users of IaaS systems need to be able to anticipate the short-term future demand of their own services, so that only the required infrastructure is requested at any instant in time. This ... View full abstract»

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  • Towards QoS-Oriented SLA Guarantees for Online Cloud Services

    Publication Year: 2013, Page(s):50 - 57
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (525 KB) | HTML iconHTML

    Cloud Computing provides a convenient means of remote on-demand and pay-per-use access to computing resources. However, its ad hoc management of quality-of-service and SLA poses significant challenges to the performance, dependability and costs of online cloud services. The paper precisely addresses this issue and makes a threefold contribution. First, it introduces a new cloud model, the SLAaaS (... View full abstract»

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  • Optimal Cloud Resource Auto-Scaling for Web Applications

    Publication Year: 2013, Page(s):58 - 65
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (255 KB) | HTML iconHTML

    In the on-demand cloud environment, web application providers have the potential to scale virtual resources up or down to achieve cost-effective outcomes. True elasticity and cost-effectiveness in the pay-per-use cloud business model, however, have not yet been achieved. To address this challenge, we propose a novel cloud resource auto-scaling scheme at the virtual machine (VM) level for web appli... View full abstract»

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  • Cost-Efficient Virtual Machine Provisioning for Multi-tier Web Applications and Video Transcoding

    Publication Year: 2013, Page(s):66 - 69
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (132 KB) | HTML iconHTML

    Infrastructure as a Service (IaaS) clouds provide virtual machines (VMs) under the pay-per-use business model. The dynamic on-demand provisioning of VMs allows IaaS users to ensure scalability of their web applications and web-based services from really low to really high loads. However, VM provisioning must be done carefully because over-provisioning results in an increased operational cost, whil... View full abstract»

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  • Sesame: A User-Transparent Optimizing Framework for Many-Core Processors

    Publication Year: 2013, Page(s):70 - 73
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (248 KB) | HTML iconHTML

    With the integration of more computational cores and deeper memory hierarchies on modern processors, the performance gap between naively parallel zed code and optimized code becomes much larger than ever before. Very often, bridging the gap involves architecture-specific optimizations. These optimizations are difficult to implement by application programmers, who typically focus on the basic funct... View full abstract»

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  • Reproducing Network Conditions for Tests of Large-Scale Distributed Systems

    Publication Year: 2013, Page(s):74 - 77
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (672 KB) | HTML iconHTML

    Testing distributed systems is difficult, error-prone and time-consuming. Especially scalability tests are hard to perform because of the large number of connected devices. The heterogeneity of these devices (PCs, smart phones, tablets) and their various network access technologies (DSL, cable, WiFi, 3G/4G) even worsen the situation. We argue that the reproduction of network conditions is necessar... View full abstract»

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  • Fast Wide Area Live Migration with a Low Overhead through Page Cache Teleportation

    Publication Year: 2013, Page(s):78 - 82
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (199 KB) | HTML iconHTML

    Live migration of virtual machines over a wide area network has many use cases such as cross-data center load balancing, low carbon virtual private clouds, and disaster recovery of IT systems. An efficient wide area live migration method is required because cross-data center connections have a narrow bandwidth. Page cache occupies a large portion of the memory of a Virtual Machine (VM) when it exe... View full abstract»

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  • Towards an Optimized Big Data Processing System

    Publication Year: 2013, Page(s):83 - 86
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (151 KB) | HTML iconHTML

    Scalable by design to very large computing systems such as grids and clouds, MapReduce is currently a major big data processing paradigm. Nevertheless, existing performance models for MapReduce only comply with specific workloads that process a small fraction of the entire data set, thus failing to assess the capabilities of the MapReduce paradigm under heavy workloads that process exponentially i... View full abstract»

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