Learning to Diagnose Stragglers in Distributed Computing

Li, Cong; Shen, Huanxing; Huang, Tai

doi:10.1109/mtags.2016.04

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…When a task in execution becomes slower than other tasks in the same job, this task is called a "straggler task" which prolong the entire job and the cluster throughput will be affected. There are numerous causes that make a task take a long time in execution and turned into a straggler [29], [30]. These causes like hardware heterogeneity, overawed machines, network congestion, bad code and contention of resources between tasks running on the straggler machine.…”

Section: B Straggler Problemmentioning

confidence: 99%

An Optimized Straggler Mitigation Framework for Large-Scale Distributed Computing Systems

et al. 2022

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Nowadays, Big Data becomes a research focus in industrial, banking, social network, and other fields. In addition, the explosive increase of data and information require efficient processing solutions. Therefore, Spark is considered as a promising candidate of Large-Scale Distributed Computing Systems for big data processing. One primary challenge is the straggler problem that occurred due to the presence of heterogeneity where a machine takes an extra-long time to finish execution of a task, which decreases the system throughput. To mitigate straggler tasks, Spark adopts speculative execution mechanism, in which the scheduler launches additional backup to avoid slow task processing and achieve acceleration. In this paper, a new Optimized Straggler Mitigation Framework is proposed. The proposed framework uses a dynamic criterion to determine the closest straggler tasks. This criterion is based on multiple coefficients to achieve a reliable straggler decision. Also, it integrates the historical data analysis and online adaptation for intelligent straggler judgment. This guarantees the effectiveness of speculative tasks by improving cluster performance. Experimental results on various benchmarks and applications show that the proposed framework achieves 23.5% to 30.7% execution time reductions, and 25.4 to 46.3% increase of the cluster throughputs compared with spark engine.

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Section: B Straggler Problemmentioning

confidence: 99%

An Optimized Straggler Mitigation Framework for Large-Scale Distributed Computing Systems

et al. 2022

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“…The successful execution of a task is important to avoid straggler occurrence during execution of jobs [24][25][26][27][28] [67] [68]. During job execution, stragglers can occur due to unhandled requests or ineffective task interference and task incompatibility management.…”

Section: Task Executionmentioning

confidence: 99%

“…The proposed techniques improve the performance of computing systems by reducing task stragglers occurrence. Cong et al [27] proposed a Machine Learning based Straggler Detection (MLSD) technique using unsupervised clustering method. The proposed technique effectively manages the resources while executing the jobs and diagnosing the stragglers at runtime.…”

Section: Straggler Detection Straggler Mitigationmentioning

confidence: 99%

Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centres

2020

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Cloud computing systems are splitting compute and data intensive jobs into smaller tasks to execute them in a parallel manner using clusters to improve execution time. However, such systems at increasing scale are exposed to stragglers, whereby abnormally slow running tasks executing within a job substantially affect job performance completion. Such stragglers are a direct threat towards attaining fast execution of data intensive jobs within cloud computing. Researchers have proposed an assortment of different mechanisms, frameworks, and management techniques to detect and mitigate stragglers both proactively and reactively. In this paper, we present a comprehensive review of straggler management techniques within large-scale cloud datacenters. We provide a detailed taxonomy of straggler causes, as well as proposed management and mitigation techniques based on straggler characteristics and properties. From this systematic review, we outline several outstanding challenges and potential directions of possible future work for straggler research.

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“…This allows reconstruction of the full computation result with a decoding step even if not all the workers have returned computation results. Therefore, CC schemes can reduce latency and increase reliability in scenarios in which the worker nodes are, e.g., subject to random failures or straggling [1].…”

Section: Introductionmentioning

confidence: 99%

A Learning-Based Approach to Approximate Coded Computation

Agrawal¹,

Qiu²,

Frey³

et al. 2022

Preprint

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Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are representable as matrix polynomials. In this paper, we propose AICC, an AI-aided learning approach that is inspired by LCC but also uses deep neural networks (DNNs). It is appropriate for coded computation of more general functions. Numerical simulations demonstrate the suitability of the proposed approach for the coded computation of different matrix functions that are often utilized in digital signal processing.

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Learning to Diagnose Stragglers in Distributed Computing

Cited by 10 publications

References 11 publications

An Optimized Straggler Mitigation Framework for Large-Scale Distributed Computing Systems

An Optimized Straggler Mitigation Framework for Large-Scale Distributed Computing Systems

Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centres

A Learning-Based Approach to Approximate Coded Computation

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