How to unleash frugal innovation through internet of things and artificial intelligence: Moderating role of entrepreneurial knowledge and future challenges

Qin, Weiwei

doi:10.1016/j.techfore.2024.123286

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…The term "frugal" implies simplicity, economy, and minimalism, where the goal is to meet computing needs with the least number of resources, both in terms of hardware and energy [1]. Frugal clusters are an innovative solution that intersects sustainability and digital transformation [2]. By leveraging energy-efficient hardware components like Single-Board Computers (SBCs) [3], these clusters reduce energy consumption, aligning with sustainability goals and minimizing environmental impact [4], thus aligning with broader sustainability goals.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Criteria Selection for Efficient Resource Allocation in Frugal Heterogeneous Hadoop Clusters

Qureshi

2024

Electronics

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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 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.

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Section: Introductionmentioning

confidence: 99%