Article
Version 2
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Towards Improving YARN performance for Frugal Heterogeneous SBC-based Edge Clusters
Version 1
: Received: 30 March 2024 / Approved: 1 April 2024 / Online: 2 April 2024 (09:14:06 CEST)
Version 2 : Received: 3 May 2024 / Approved: 3 May 2024 / Online: 3 May 2024 (10:40:13 CEST)
Version 2 : Received: 3 May 2024 / Approved: 3 May 2024 / Online: 3 May 2024 (10:40:13 CEST)
How to cite: Qureshi, B. Towards Improving YARN performance for Frugal Heterogeneous SBC-based Edge Clusters. Preprints 2024, 2024040154. https://doi.org/10.20944/preprints202404.0154.v2 Qureshi, B. Towards Improving YARN performance for Frugal Heterogeneous SBC-based Edge Clusters. Preprints 2024, 2024040154. https://doi.org/10.20944/preprints202404.0154.v2
Abstract
Efficient resource allocation is crucial in clusters with frugal Single-Board Computers (SBCs) possessing limited computational resources. These clusters are increasingly being deployed in edge computing environments in resource-constrained settings where energy efficiency and cost-effectiveness are paramount. A major challenge in Hadoop YARN scheduling is load-balancing, as frugal nodes within the cluster can become overwhelmed, resulting in degraded performance and frequent occurrences of out-of-memory errors, ultimately leading to job failures. In this study, we introduce an Adaptive Multi-criteria Selection for Efficient Resource Allocation (AMS-ERA) in Frugal Heterogeneous Hadoop Clusters. Our criterion considers CPU, memory and disk requirements for jobs and aligns the requirements with available resources in the cluster for optimal resource allocation. To validate our approach, we deploy a heterogeneous SBC-based cluster consisting of 11 SBC nodes and conduct several experiments to evaluate the performance using Hadoop wordcount and terasort benchmark for various workload settings. The results are compared to the Hadoop-Fair, FOG and IDaPS scheduling strategies. Our results demonstrate a significant improvement in performance with the proposed AMS-ERA, reducing execution time by 27.2%, 17.4% and 7.6% respectively using terasort and wordcount benchmarks.
Keywords
frugal hadoop clusters; dynamic analytical hierarchy process; locality aware data placement; single board computers
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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