Adding Virtualization Capabilities to the Grid’5000 Testbed

Balouek, Daniel; Amarie, Alexandra Carpen; Charrier, Ghislain; Desprez, Frédéric; Jeannot, Emmanuel; Jeanvoine, Emmanuel; Lèbre, Adrien; Margery, David; Niclausse, Nicolas; Nussbaum, Lucas; Richard, Olivier; Pérez, Christian; Quesnel, Flavien; Rohr, Cyril; Sarzyniec, Luc

doi:10.1007/978-3-319-04519-1_1

Cited by 218 publications

(200 citation statements)

References 5 publications

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“…Experiments were conducted on Dell PowerEdge R720 (2 CPUs, 6 cores) and AMD Opteron 6164 HE 1.7GHz (2 CPUs, 12 cores) from Taurus and Stremi clusters from Grid'5000 [24].…”

Section: A Methodologymentioning

confidence: 99%

Efficient Heuristics for Placing Large-Scale Distributed Applications on Multiple Clouds

Pérez

Desprez

2016

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

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Abstract-With the fast growth of the demand for Cloud computing services, the Cloud has become a very popular platform to develop distributed applications. Features that in the past were available only to big corporations, like fast scalability, availability, and reliability, are now accessible to any customer, including individuals and small companies, thanks to Cloud computing. In order to place an application, a designer must choose among VM types, from private and public cloud providers, those that are capable of hosting her application or its parts using as criteria application requirements, VM prices, and VM resources. This procedure becomes more complicated when the objective is to place large component based applications on multiple clouds. In this case, the number of possible configurations explodes making necessary the automation of the placement. In this context, scalability has a central role since the placement problem is a generalization of the NP-Hard multi-dimensional bin packing problem.In this paper we propose efficient greedy heuristics based on first fit decreasing and best fit algorithms, which are capable of computing near optimal solutions for very large applications, with the objective of minimizing costs and meeting application performance requirements. Through a meticulous evaluation, we show that the greedy heuristics took a few seconds to calculate near optimal solutions to placements that would require hours or even days when calculated using state of the art solutions, namely exact algorithms or meta-heuristics.

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“…Experiments were conducted on Dell PowerEdge R720 (2 CPUs, 6 cores) and AMD Opteron 6164 HE 1.7GHz (2 CPUs, 12 cores) from Taurus and Stremi clusters from Grid'5000 [24].…”

Section: A Methodologymentioning

confidence: 99%

Efficient Heuristics for Placing Large-Scale Distributed Applications on Multiple Clouds

Pérez

Desprez

2016

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

Self Cite

View full text Add to dashboard Cite

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“…Moreover, we emphasize that the language is resource provider dependent in order to handle infrastructure specificities. For instance, on Grid'5000 [14], the language has been extended to specify the name of physical clusters where resources should be acquired, as depicted in Figure 1(b). In this description, the paravance cluster (located in Rennes) will provide resources for the network services and the econome cluster (located in Nantes) will provide resources for the compute nodes.…”

Section: A Enos Description Language For Flexible Topologiesmentioning

confidence: 99%

“…In other words for each testbed, an extended version of the Enos DSL and a provider should be available. Currently, Enos supports two kinds of infrastructure; the first one gets bare-metal resources from the Grid'5000 testbed [14]; the second one uses a VM based on Vagrant [15]. We emphasize that additional drivers for any other system can be easily implemented in less than 500 lines of Python code.…”

Section: B Enos Workflowmentioning

confidence: 99%

Toward a Holistic Framework for Conducting Scientific Evaluations of OpenStack

Cherrueau

Pertin²,

Simonet³

et al. 2017

2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

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Abstract-By massively adopting OpenStack for operating small to large private and public clouds, the industry has made it one of the largest running software project, overgrowing the Linux kernel. However, with success comes increased complexity; facing technical and scientific challenges, developers are in great difficulty when testing the impact of individual changes on the performance of such a large codebase, which will likely slow down the evolution of OpenStack. Thus, we claim it is now time for the scientific community to join the effort and get involved in the development of OpenStack, like it has been once done for Linux.In this spirit, we developed Enos, an integrated framework that relies on container technologies for deploying and evaluating OpenStack on any testbed. Enos allows researchers to easily express different configurations, enabling fine-grained investigations of OpenStack services. Enos collects performance metrics at runtime and stores them for post-mortem analysis and sharing. The relevance of the Enos approach to reproducible research is illustrated by evaluating different OpenStack scenarios on the Grid'5000 testbed.

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“…Then, the first instance, named Disk1 (lines [14][15], which is attached to bob does not specify any specific list of files and thus inherits of the generic list of its type. Conversely the second instance, named Disk2 and attached to alice (lines [17][18][19][20], is given a specific list of file, described in a different format, that of Windows TM . The method used to parse file lists derives from the value of the content_type attribute.…”

Section: A Description Of Storage Componentsmentioning

confidence: 99%

Adding Storage Simulation Capacities to the SimGrid Toolkit: Concepts, Models, and API

Lèbre¹,

Legrand

Suter

et al. 2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Abstract-For each kind of distributed computing infrastructures, i.e., clusters, grids, clouds, data centers, or supercomputers, storage is a essential component to cope with the tremendous increase in scientific data production and the ever-growing need for data analysis and preservation. Understanding the performance of a storage subsystem or dimensioning it properly is an important concern for which simulation can help by allowing for fast, fully repeatable, and configurable experiments for arbitrary hypothetical scenarios. However, most simulation frameworks tailored for the study of distributed systems offer no or little abstractions or models of storage resources.In this paper, we detail the extension of SimGrid, a versatile toolkit for the simulation of large-scale distributed computing systems, with storage simulation capacities. We first define the required abstractions and propose a new API to handle storage components and their contents in SimGrid-based simulators. Then we characterize the performance of the fundamental storage component that are disks and derive models of these resources. Finally we list several concrete use cases of storage simulations in clusters, grids, clouds, and data centers for which the proposed extension would be beneficial.

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Adding Virtualization Capabilities to the Grid’5000 Testbed

Cited by 218 publications

References 5 publications

Efficient Heuristics for Placing Large-Scale Distributed Applications on Multiple Clouds

Efficient Heuristics for Placing Large-Scale Distributed Applications on Multiple Clouds

Toward a Holistic Framework for Conducting Scientific Evaluations of OpenStack

Adding Storage Simulation Capacities to the SimGrid Toolkit: Concepts, Models, and API

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