Designing parallel computing using raspberry pi clusters for IoT servers on apache Hadoop

Nugroho, Sudarmanto Budi; Widiyanto, Andi

doi:10.1088/1742-6596/1517/1/012070

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…Testing on virtual meet HLI was conducted to see the blockchain's ability to process virtual meet more efficiently. The number of requests to test web server performance in this study is 100, 100, 200, and 500 [14]. The test scenarios discussed in this study are Transaction, Response Time, Transaction Rate, Throughput.…”

Section: Resultsmentioning

confidence: 99%

Influence of Blockchain Implementation for Virtual Meetings at Home Learning Indonesia

Widiyanto

Pujiarto²,

Prabowo³

et al. 2023

Int. J. Emerg. Technol. Learn.

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Online learning is currently one of the popular learning concepts in schools. The results of online learning are still not as expected. This is due to the lack of reliability of teachers in mastering learning platforms and the lack of virtual meetings between teachers and students. Home Learning Indonesia (HLI) is a Learning Management System with virtual meetings and blockchain technology is an alternative to solving this problem. This research utilizes blockchain technology to minimize the shortcomings of existing virtual meeting platforms. In this study, testing HLI virtual meetings and several other platforms. The results of the HLI Virtual meetings test when the requests given are getting higher, and the throughput is decreasing following the number of requests given. These results can show that the use of blockchain in virtual meetings can increase network stability in its use.

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Section: Resultsmentioning

confidence: 99%

Influence of Blockchain Implementation for Virtual Meetings at Home Learning Indonesia

Widiyanto

Pujiarto²,

Prabowo³

et al. 2023

Int. J. Emerg. Technol. Learn.

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“…They note that clusters formed by Raspberry Pi have proved to be a viable and economical solution for carrying out tasks involving the use of big data. Nugroho et al in [20] also design a parallel computing framework using Raspberry Pi clusters for IoT services and applications.…”

Section: Relevant Research Workmentioning

confidence: 99%

Optimizing Hadoop Scheduling in Single-Board-Computer-Based Heterogeneous Clusters

Qureshi

2024

Computation

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Single-board computers (SBCs) are emerging as an efficient and economical solution for fog and edge computing, providing localized big data processing with lower energy consumption. Newer and faster SBCs deliver improved performance while still maintaining a compact form factor and cost-effectiveness. In recent times, researchers have addressed scheduling issues in Hadoop-based SBC clusters. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the scheduling mechanism to address these challenges. In this paper, we leverage the use of node labels introduced in Hadoop 3+ and define a Frugality Index that categorizes and labels SBC nodes based on their physical capabilities, such as CPU, memory, disk space, etc. Next, an adaptive configuration policy modifies the native fair scheduling policy by dynamically adjusting resource allocation in response to workload and cluster conditions. Furthermore, the proposed frugal configuration policy considers prioritizing the reduced tasks based on the Frugality Index to maximize parallelism. To evaluate our proposal, we construct a 13-node SBC cluster and conduct empirical evaluation using the Hadoop CPU and IO intensive microbenchmarks. The results demonstrate significant performance improvements compared to native Hadoop FIFO and capacity schedulers, with execution times 56% and 22% faster than the best_cap and best_fifo scenarios. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations.

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“…The D-Link (DES-1008A) features eight fast ethernet LAN ports that enable high-speed data transfer between nodes. The parallelisation processes follow the procedures used in prior literature [18]- [20]. Its plug-and-play feature also eases the setting of the networking configuration for the parallel system.…”

Section: Hardwarementioning

confidence: 99%

Evaluation of CFD Computing Performance on Multi-Core Processors for Flow Simulations

Ismail

Mohammed

Basuno

et al. 2022

ARASET

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Previous parallel computing implementations for Computational Fluid Dynamics (CFD) focused extensively on Complex Instruction Set Computer (CISC). Parallel programming was incorporated into the previous generation of the Raspberry Pi Reduced Instruction Set Computer (RISC). However, it yielded poor computing performance due to the processing power limits of the time. This research focuses on utilising two Raspberry Pi 3 B+ with increased processing capability compared to its previous generation to tackle fluid flow problems using numerical analysis and CFD. Parallel computing elements such as Secure Shell (SSH) and the Message Passing Interface (MPI) protocol were implemented for Advanced RISC Machine (ARM) processors. The parallel network was then validated by a processor call attempt and core execution test. Parallelisation of the processors enables the study of fluid flow and computational fluid dynamics (CFD) problems, such as validation of the NACA 0012 airfoil and an additional case of the Laplace equation for computing the temperature distribution via the parallel system. The experimental NACA 0012 data was validated using the parallel system, which can simulate the airfoil's physics. Each core was enabled and tested to determine the system's performance in parallelising the execution of various programming algorithms such as pi calculation. A comparison of the execution time for the NACA 0012 validation case yielded a parallelisation efficiency above 50%. The case studies confirmed the Raspberry Pi 3 B+'s successful parallelisation independent of external software and machines, making it a self-sustaining compact demonstration cluster of parallel computers for CFD.

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Designing parallel computing using raspberry pi clusters for IoT servers on apache Hadoop

Cited by 7 publications

References 3 publications

Influence of Blockchain Implementation for Virtual Meetings at Home Learning Indonesia

Influence of Blockchain Implementation for Virtual Meetings at Home Learning Indonesia

Optimizing Hadoop Scheduling in Single-Board-Computer-Based Heterogeneous Clusters

Evaluation of CFD Computing Performance on Multi-Core Processors for Flow Simulations

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