Proactive Fault Tolerance Using Preemptive Migration

Engelmann, Christian; Vallée, Geoffroy; Naughton, Thomas J.; Scott, Stephen L.

doi:10.1109/pdp.2009.31

Cited by 74 publications

(41 citation statements)

References 10 publications

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“…Proactive fault tolerance [6] avoids experiencing failures through preventative measures, such as by migrating application parts away from compute nodes that are "about to fail". It relies on a feedback-loop control (Figure 1) with continuous health monitoring, data analysis, and application reallocation.…”

Section: System Monitoringmentioning

confidence: 99%

“…We deployed the framework on the same 64-node cluster (in a 32-node degraded fashion due to faulty hardware) that was used for our earlier investigations (see Section 2.1 and [9,6]). For this test, we sampled 18 metrics on 32 nodes over a 4 hour period with constantly varying classes and a sample interval for all metrics of 30 seconds.…”

Section: Monitoring Data Accumulationmentioning

confidence: 99%

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Aggregation of Real-Time System Monitoring Data for Analyzing Large-Scale Parallel and Distributed Computing Environments

Bohm

Engelmann

Scott

2010

2010 IEEE 12th International Conference on High Performance Computing and Communications (HPCC)

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We present a monitoring system for large-scale parallel and distributed computing environments that allows to trade-off accuracy in a tunable fashion to gain scalability without compromising fidelity. The approach relies on classifying each gathered monitoring metric based on individual needs and on aggregating messages containing classes of individual monitoring metrics using a tree-based overlay network. The MRNet-based prototype is able to significantly reduce the amount of gathered and stored monitoring data, e.g., by a factor of ≈56 in comparison to the Ganglia distributed monitoring system. A simple scaling study reveals, however, that further efforts are needed in reducing the amount of data to monitor future-generation extreme-scale systems with up to 1,000,000 nodes. The implemented solution did not had a measurable performance impact as the 32-node test system did not produce enough monitoring data to interfere with running applications.

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Section: System Monitoringmentioning

confidence: 99%

Section: Monitoring Data Accumulationmentioning

confidence: 99%

Aggregation of Real-Time System Monitoring Data for Analyzing Large-Scale Parallel and Distributed Computing Environments

Bohm

Engelmann

Scott

2010

2010 IEEE 12th International Conference on High Performance Computing and Communications (HPCC)

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“…A number of advanced resilience technologies have been developed and/or are currently in development, including checkpoint/restart-specific file and storage systems, incremental/differential checkpointing, message logging for uncoordinated checkpointing, fault tolerant message passing interface (FT-MPI), containment domains, algorithm-based fault tolerance (ABFT), rejuvenation, reliability-aware scheduling, proactive migration, and redundancy [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]. However, there are currently no tools, methods, and metrics to compare them fairly, especially at extreme scale, and to identify the cost/benefit trade-off.…”

Section: Introductionmentioning

confidence: 99%

Toward a Performance/Resilience Tool for Hardware/Software Co-design of High-Performance Computing Systems

Engelmann

Naughton

2013

2013 42nd International Conference on Parallel Processing

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Abstract-xSim is a simulation-based performance investigation toolkit that permits running high-performance computing (HPC) applications in a controlled environment with millions of concurrent execution threads, while observing application performance in a simulated extreme-scale system for hardware/software co-design. The presented work details newly developed features for xSim that permit the injection of MPI process failures, the propagation/detection/notification of such failures within the simulation, and their handling using application-level checkpoint/restart. These new capabilities enable the observation of application behavior and performance under failure within a simulated future-generation HPC system using the most common fault handling technique.

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“…It is not desirable to have to restart a job from the beginning if it has been executing for hours or days or months [6]. A key challenge in maintaining the seamless (or near seamless) execution of such jobs in the event of failures is addressed under research in fault tolerance [7,8,9,10].Many jobs rely on fault tolerant approaches that are implemented in the middleware supporting the job (for example [6,11,12,13]). The conventional fault tolerant mechanism supported by the middleware is checkpointing [14,15,16,17], which involves the periodic recording of intermediate states of execution of a job to which execution can be returned if a fault occurs.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Automating fault tolerance in high-performance computational biological jobs using multi-agent approaches

Varghese

McKee

Alexandrov

2014

Computers in Biology and Medicine

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Automating fault tolerance in high-performance computational biological jobs using multi-agent approaches Varghese, B., McKee, G., & Alexandrov, V. (2014). Automating fault tolerance in high-performance computational biological jobs using multi-agent approaches. General rights Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.Take down policy The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact openaccess@qub.ac.uk. Background: Large-scale biological jobs on high-performance computing systems require manual intervention if one or more computing cores on which they execute fail. This places not only a cost on the maintenance of the job, but also a cost on the time taken for reinstating the job and the risk of losing data and execution accomplished by the job before it failed. Approaches which can proactively detect computing core failures and take action to relocate the computing core's job onto reliable cores can make a significant step towards automating fault tolerance.Method: This paper describes an experimental investigation into the use of multi-agent approaches for fault tolerance. Two approaches are studied, the first at the job level and the second at the core level. The approaches are investigated for single core failure scenarios that can occur in the execution of parallel reduction algorithms on computer clusters. A third approach is proposed that incorporates multi-agent technology both at the job and core level. Experiments are pursued in the context of genome searching, a popular computational biology application.Result: The key conclusion is that the approaches proposed are feasible for automating fault tolerance in high-performance computing systems with minimal human intervention. In a typical experiment in which the fault tolerance is studied, centralised and decentralised checkpointing approaches on an average add 90% to the actual time for executing the job. On the other hand, in the same experiment the multi-agent approaches add only 10% to the overall execution time.high-performance computing | fault tolerance | biological jobs | multi-agents | seamless execution | checkpoint Introduction T he scale of resources and computations required for executing large-scale biological jobs are significantly increasing [1,2]. With this increase the resultant number of failures while running these jobs will also increase and the time between failures will decrease [3,4,5]. It is not desirable to have to restart a job from the beginning if it has been executin...

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Proactive Fault Tolerance Using Preemptive Migration

Cited by 74 publications

References 10 publications

Aggregation of Real-Time System Monitoring Data for Analyzing Large-Scale Parallel and Distributed Computing Environments

Aggregation of Real-Time System Monitoring Data for Analyzing Large-Scale Parallel and Distributed Computing Environments

Toward a Performance/Resilience Tool for Hardware/Software Co-design of High-Performance Computing Systems

Automating fault tolerance in high-performance computational biological jobs using multi-agent approaches

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