Avoiding Slow Running Nodes in Distributed Systems

Deshmukh, Shyam; Aghav, Jagannath; Rao, K. Ram Mohan; Rao, B. Thirumala

doi:10.1007/978-981-10-3226-4_41

Cited by 3 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is can be achieved in practice using multitask learning (MTL) as demonstrated by Yadwadkar et al [9]. Another approach mentioned in Deshmukh et al [12] tried to avoid straggler occurrence through data parallelism techniques like MPI-libraries.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Deshmukh

Rao

Shabaz

2021

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

Modern big data applications tend to prefer a cluster computing approach as they are linked to the distributed computing framework that serves users jobs as per demand. It performs rapid processing of tasks by subdividing them into tasks that execute in parallel. Because of the complex environment, hardware and software issues, tasks might run slowly leading to delayed job completion, and such phenomena are also known as stragglers. The performance improvement of distributed computing framework is a bottleneck by straggling nodes due to various factors like shared resources, heavy system load, or hardware issues leading to the prolonged job execution time. Many state-of-the-art approaches use independent models per node and workload. With increased nodes and workloads, the number of models would increase, and even with large numbers of nodes. Not every node would be able to capture the stragglers as there might not be sufficient training data available of straggler patterns, yielding suboptimal straggler prediction. To alleviate such problems, we propose a novel collaborative learning-based approach for straggler prediction, the alternate direction method of multipliers (ADMM), which is resource-efficient and learns how to efficiently deal with mitigating stragglers without moving data to a centralized location. The proposed framework shares information among the various models, allowing us to use larger training data and bring training time down by avoiding data transfer. We rigorously evaluate the proposed method on various datasets with high accuracy results.

show abstract

Section: Introductionmentioning

confidence: 99%

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Deshmukh

Rao

Shabaz

2021

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

show abstract

Hawkeye: Adaptive Straggler Identification on Heterogeneous Spark Cluster With Reinforcement Learning

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

It is a common sense that people harbors the belief that stragglers exert huge influence upon the performance conducted by the analysis systems of big data for the reason of poor performance made by some computing nodes, data skew and so on. Accordingly, stragglers have been billed as an indispensable bottleneck in Map-Reduce framework processing. However, existing studies on stragglers identification are targeting coarse-grained detection, schedule level optimization and off-line log based cause analysis. Accuracy identifying the stragglers in time for each job, however, is an extremely tough because (1) Auite a number of root causes for stragglers in data analytics frameworks;(2) The number of key parameters affecting stragglers identification; and (3) The different clusters configurations, and their impact on the stragglers detection, vary among different job types and sizes. Either existing solutions adopt a ''tweak-and-pray'' manual tuning approach, which is complex, time-consuming and error-prone, or only most of them fix theirs eyes upon coarse-grained straggler detection. In this paper, we systematically conduct the exploration on the fundamental problem of automatic, adaptive straggler identification on big data analytics platform. Under the inspiration of the recent triumphs over implementing Reinforcement Learning (RL) techniques for solving complex online optimal problems, we conducted investigation that Reinforcement learning are reasonably employed to adaptively opt the optimal parameters to identify stragglers free of the intervention of human beings. Specifically, we propose Hawkeye, a general adaptive speculative execution system which identifies stragglers by reinforcement are learning to launch speculative tasks on heterogeneous cluster at runtime. In accordance with the experimental conclusions, Hawkeye manages to cut down the job completion time over the distinct type applications. An instance is that it reveals as many as nearly 37% decrease average job completion time based on an improvement of 23% on the preciseness of the present resolutions to the heterogeneous cluster.

show abstract

Cheetah: A Dynamic Performance Optimization Approach on Heterogeneous Big Data Analytics Cluster

Zhang

Han

et al. 2019

2019 5th International Conference on Big Data Computing and Communications (BIGCOM)

View full text Add to dashboard Cite

Avoiding Slow Running Nodes in Distributed Systems

Cited by 3 publications

References 4 publications

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Hawkeye: Adaptive Straggler Identification on Heterogeneous Spark Cluster With Reinforcement Learning

Cheetah: A Dynamic Performance Optimization Approach on Heterogeneous Big Data Analytics Cluster

Contact Info

Product

Resources

About