Bridging the High Performance Computing Gap: the OurGrid Experience

Brasileiro, Francisco; Araiijo, E.; Voorsluys, William; Oliveira, Marcelo Costa; Figueiredo, Flávio

doi:10.1109/ccgrid.2007.28

Cited by 30 publications

(21 citation statements)

References 10 publications

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“…Among others, BOINC [4] is probably the most common, and uses a classical client/server architecture. Other types of architecture are also proposed, some of them inspired by peer-to-peer systems [8].…”

Section: Desktop Grid and Schedulingmentioning

confidence: 99%

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A Fair Decentralized Scheduler for Bag-of-Tasks Applications on Desktop Grids

Celaya¹,

Marchal²

2010

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

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Desktop Grids have become very popular nowadays, with projects that include hundred of thousands computers. Desktop grid scheduling faces two challenges. First, the platform is volatile, since users may reclaim their computer at any time, which makes centralized schedulers inappropriate. Second, desktop grids are likely to be shared among several users, thus we must be particularly careful to ensure a fair sharing of the resources.In this paper, we propose a decentralized scheduler for bag-of-tasks applications on desktop grids, which ensures a fair and efficient use of the resources. It aims to provide a similar share of the platform to every application by minimizing their maximum stretch, using completely decentralized algorithms and protocols. After presenting our algorithms, we evaluate them through extensive simulations. We compare our solution to already existing centralized ones under similar conditions, and show that its performance is close to the best centralized algorithms.

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Section: Desktop Grid and Schedulingmentioning

confidence: 99%

“…This motivates the use of a robust distributed architecture to manage resources, and the adaptation of peer-to-peer systems to computing grids is natural [8,9].…”

Section: Introductionmentioning

confidence: 99%

A Fair Decentralized Scheduler for Bag-of-Tasks Applications on Desktop Grids

Celaya¹,

Marchal²

2010

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

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“…Although F. Nadeem, etc. in [11] used "similarity templates" to derive the predictions and C. Glasner, etc in [12,13] clustered the users, hosts and jobs to detect similarities and derived predictions, they also have some specific restrictions, such as the former specifies restriction on scientific workflow applications, there are however many applications fall into this category of bag-of-task (BoT) despite their simplicity [21,22]. Hence, the classification remains a problem that should attract attention and we hope to further explore in future work.…”

Section: The Architecture Of the Proposed Model: Mscpmmentioning

confidence: 99%

A multi-strategy collaborative prediction model for the runtime of online tasks in computing cluster/grid

2010

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An efficient function of a complicated or dynamic high performance computing environment requires the scheduler to dispatch the submitted tasks according to the identification of the idling resources. A derivative problem is to provide accurate forecasts of the tasks runtimes. This is usually needed to assist scheduling policies and fine tune scheduling decisions, and also used for future planning of resource allocation when conducting advance reservation. However, the characteristics of the existing prediction strategies determine that the sole strategy is not appropriate for all kinds of heterogeneous tasks. Aiming at this problem, a multi-strategy collaborative prediction model (MSCPM) for the runtime of online tasks is proposed, and a novel concept named Prediction Accuracy Assurance (PAA) as a criterion is introduced to quantitatively evaluate the precision of the prediction runtime provided by a specific prediction strategy.MSCPM uses the existing strategies of prediction runtime to generate multiple collaborative prediction schemes and takes the prediction result of the scheme which provides the optimal PAA. We evaluate the performance of the proposed model which recently integrates four simple yet M. Tao ( ) · S. Dong widely used time series prediction strategies based on the gathered traces of three different tasks. The analysis results show that MSCPM can aggregate the superiority of the various existing prediction strategies and the evaluation criterion can pick out the near-optimal one within the prediction results provided by the integrated strategies. MSCPM provides an enhanced accuracy assurance for the prediction runtime of the online tasks in the computing environments.

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“…The success of the volunteer paradigm is witnessed by a wide set of existing platforms-among others, we mention: BOINC [8], HTCondor [33], OurGrid [10], Seattle [12], SETI@home [9] and ScienceCloud [23]. Most of them rely on distributed hash tables to save and locate resources in the network.…”

Section: Introductionmentioning

confidence: 99%

A computational field framework for collaborative task execution in volunteer clouds

Sebastio

Amoretti

Lafuente

2014

Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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The increasing diffusion of cloud technologies offers new opportunities for distributed and collaborative computing. Volunteer clouds are a prominent example, where participants join and leave the platform and collaborate by sharing computational resources. The high complexity, dynamism and unpredictability of such scenarios call for decentralized self-* approaches. We present in this paper a framework for the design and evaluation of self-adaptive collaborative task execution strategies in volunteer clouds. As a byproduct, we propose a novel strategy based on the Ant Colony Optimization paradigm, that we validate through simulation-based statistical analysis over Google workload data.

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Bridging the High Performance Computing Gap: the OurGrid Experience

Cited by 30 publications

References 10 publications

A Fair Decentralized Scheduler for Bag-of-Tasks Applications on Desktop Grids

A Fair Decentralized Scheduler for Bag-of-Tasks Applications on Desktop Grids

A multi-strategy collaborative prediction model for the runtime of online tasks in computing cluster/grid

A computational field framework for collaborative task execution in volunteer clouds

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