Real Time Application and CPU Utilisation Monitoring Tool

Chindalia, Utsav; S, Skanda C; Chandrashekar, Shruthi; Sharma, Oshin

doi:10.1109/iceca.2019.8822069

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…The execution times for all operations are measured using built-in time measurement functions, encompassing the entire spectrum of activities related to both client and server tasks in the training process. The tool used to record the training time is Psutil, which is a cross-platform python library that provides data on running processes and system usage on the workstation [31]. These measurements also include any delays associated with queuing and messaging between client and aggregation server processes.…”

Section: Methodsmentioning

confidence: 99%

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Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis

Soudan,

Abbas,

Kubba

et al. 2024

Preprint

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This paper presents a systematic examination and experimental comparison of the prominent Federated Learning (FL) frameworks FedML, Flower, Substra, and OpenFL. The frameworks are evaluated experimentally by implementing federated learning over a varying number of clients, emphasizing a thorough analysis of scalability and key performance metrics. The study assesses the impact of increasing client counts on total training time, loss and accuracy values, and CPU and RAM usage. Results indicate distinct performance characteristics among the frameworks, with Flower displaying an unusually high loss, FedML achieving a notably low accuracy range of 66–79%, and Substra demonstrating good resource efficiency, albeit with an exponential growth in total training time. Notably, OpenFL emerges as the most scalable platform, demonstrating consistent accuracy, loss, and training time across different client counts. OpenFL’s stable CPU and RAM underscore its reliability in real-world scenarios. This comprehensive analysis provides valuable insights into the relative performance of FL frameworks, offering good understanding of their capabilities and providing guidance for their effective deployment across diverse user bases.

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Section: Methodsmentioning

confidence: 99%

“…These metrics are essential for determining the framework's efficiency and suitability under varying resource constraints, providing a comprehensive understanding of its resource utilization patterns. This metric was recorded using the psutil python tool [31].…”

Section: Cpu and Ram Usage During Trainingmentioning

confidence: 99%

Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis

Soudan,

Abbas,

Kubba

et al. 2024

Preprint

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“…Operating system (OS) organizes the processes by allotting different tasks properly according to their arrival time [1]. To increase the CPU utilization, Operating system's primary role is to allow many processes concurrently [2,3].There are different types of CPU scheduling algorithm which perform differently according to the situation, mainly CPU-scheduling algorithms concentrate on increasing CPU-performance by reducing turnaround time, the number of the context switch and waiting time [4,5]. In this work, the proposed algorithm improves the capability of the round-robin-algorithm by introducing a new enhanced quantum time in place of the time quantum in round-robin.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Round Robin Scheduling Mechanism for Process Management

Faizan¹,

Marikal²,

Anil³

2020

IJCA

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Every process which runs in an operating system is processed through multiprogramming, by sharing the processor. Multiprogramming is a capability of the operating system to execute one or a lot of programs in a single processing machine. Multiprogramming's main objective is to optimize the use of CPUs, throughput and minimize average waiting time (AWT), the average turnaround time (ATT), average response time (ART) and the number of context switches (NOC). Multi-programming can be performed by several CPU scheduling algorithms. Most preferable scheduling algorithm among all is Round robin (RR). RR algorithm is mainly depended on time quantum which is stationary. Hence, the proposed work is new approach to the round-robin scheduling algorithm (RR algorithm) with an enhanced time quantum based algorithm. This enhancement of RR algorithm by using dynamic time quantum leads to minimize AWT, ATT, ART and NOC. This approach inherits the properties of Round robin, shortest job first (SJF) algorithm and first come first serve algorithm(FCFS). Therefore, the proposed algorithm is a hybrid round robin scheduling mechanism for process management (HYRR Mechanism). HYRR Mechanism is an innovative scheduling algorithm which reduces ATT, ART, AWT and NOC to the desired levels.

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GFBE: A Generalized and Fine-Grained Blockchain Evaluation Framework

Ma,

Liu,

et al. 2024

IEEE Trans. Comput.

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Real Time Application and CPU Utilisation Monitoring Tool

Cited by 3 publications

References 1 publication

Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis

Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis

A Hybrid Round Robin Scheduling Mechanism for Process Management

GFBE: A Generalized and Fine-Grained Blockchain Evaluation Framework

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