The Challenge of Volunteer Computing with Lengthy Climate Model Simulations

Christensen, C.; Aina, T.; Stainforth, David A.

doi:10.1109/e-science.2005.76

Cited by 49 publications

(47 citation statements)

References 6 publications

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“…In Table 1, we show the gains in throughput and the costs of using cliques in the computation of a task. We assume the following data (taken from our previous experiments and from [3]): 20 checkpoints of 20MB for task; 40-day computation (so we have a checkpoint on every two days on average); and failure-prone nodes fail 2.66 times during those 40 days (this number matches Figure 4). It should be noticed that the frequency at which we publish checkpoints can be made totally independent from the actual frequency at which nodes produce local checkpoints.…”

Section: A Simple Estimation Of Trade-offsmentioning

confidence: 99%

“…If a worker fails to do so, it can be overrun by another worker. This can be combined with a Trickle mechanism [3] to give the appropriate credits to the right worker, when it reaches certain points in the computation. In this way, nodes can claim their credits as they go, they do not allow other nodes to steal their tasks, while at the same time, they can also claim ownership on a stalled task.…”

Section: Definition Of the Cliquesmentioning

confidence: 99%

“…In this section we roughly quantify these trade-offs using figures from climateprediction [3]. Machine attrition should be around 2/3, which means that around 2/3 of the machines drop computation before Figure 8.…”

Section: A Simple Estimation Of Trade-offsmentioning

confidence: 99%

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Using Cliques Of Nodes To Store Desktop Grid Checkpoints

et al. 2008

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Checkpoints that store intermediate results of computation have a fundamental impact on the computing throughput of Desktop Grid systems, like BOINC. Currently, BOINC workers store their checkpoints locally. A major limitation of this approach is that whenever a worker leaves unfinished computation, no other worker can proceed from the last stable checkpoint. This forces tasks to be restarted from scratch when the original machine is no longer available.To overcome this limitation, we propose to share checkpoints between nodes. To organize this mechanism, we arrange nodes to form complete graphs (cliques), where nodes share all the checkpoints they compute. Cliques function as survivable units, where checkpoints and tasks are not lost as long as one of the nodes of the clique remains alive. To simplify construction and maintenance of the cliques, we take advantage of the central supervisor of BOINC. To evaluate our solution, we combine simulation with some real data to answer the most fundamental question: what do we need to pay for increased throughput?

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Section: A Simple Estimation Of Trade-offsmentioning

confidence: 99%

Section: Definition Of the Cliquesmentioning

confidence: 99%

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Using Cliques Of Nodes To Store Desktop Grid Checkpoints

et al. 2008

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“…However, such an attitude would increase the number of messages and consequently stress the supervisor network, possibly disturbing the whole system performance. A more realistic approach is to use the so-called trickle messages [25] to send checkpoint digests to the supervisor. A trickle message is sent by a worker to the supervisor and provides some status information about the worker.…”

Section: Comparing Replicated Checkpoint Hashesmentioning

confidence: 99%

Sabotage-tolerance and trust management in desktop grid computing

Domingues

Sousa

Silva

2007

Future Generation Computer Systems

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The success of grid computing in open environments like the Internet is highly dependent on the adoption of mechanisms to detect failures and malicious sabotage attempts. It is also required to maintain a trust management system that permits one to distinguish the trustable from the non-trustable participants in a global computation. Without these mechanisms, users with data-critical applications will never rely on desktop grids, and will rather prefer to support higher costs to run their computations in closed and secure computing systems.This paper discusses the topics of sabotage-tolerance and trust management. After reviewing the state-of-the-art, we present two novel techniques: a mechanism for sabotage detection and a protocol for distributed trust management. The proposed techniques are targeted at the paradigm of volunteer-based computing commonly used on desktop grids.

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“…The BBC CCE uses the CPDN distributed computing infrastructure described in Christensen et al (2005). The two principal differences between the slab and BBC CCE experiments are that the BBC CCE uses the model with fully dynamical ocean, HadCM3L, described earlier, and that it is run under transient forcing-historical forcings for the period 1920-2000 are Figure 1.…”

Section: Bbc Ccementioning

confidence: 99%

The climate prediction .net BBC climate change experiment: design of the coupled model ensemble

Frame¹,

Aina

Christensen

et al. 2008

Phil. Trans. R. Soc. A.

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Perturbed physics experiments are among the most comprehensive ways to address uncertainty in climate change forecasts. In these experiments, parameters and parametrizations in atmosphere-ocean general circulation models are perturbed across ranges of uncertainty, and results are compared with observations. In this paper, we describe the largest perturbed physics climate experiment conducted to date, the British Broadcasting Corporation (BBC) climate change experiment, in which the physics of the atmosphere and ocean are changed, and run in conjunction with a forcing ensemble designed to represent uncertainty in past and future forcings, under the A1B Special Report on Emissions Scenarios (SRES) climate change scenario.

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The Challenge of Volunteer Computing with Lengthy Climate Model Simulations

Cited by 49 publications

References 6 publications

Using Cliques Of Nodes To Store Desktop Grid Checkpoints

Using Cliques Of Nodes To Store Desktop Grid Checkpoints

Sabotage-tolerance and trust management in desktop grid computing

The climate prediction .net BBC climate change experiment: design of the coupled model ensemble

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