A Straggler Identification Model for Large-Scale Distributed Computing Systems Using Machine Learning

Said, Samar A.; Habashy, Shahira M.; Salem, Sameh A.; Saad, E. L.-Sayed. M.

doi:10.1007/978-3-031-20601-6_10

Cited by 1 publication

(1 citation statement)

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“…Some works [ 31 ] are related to the detection of errors in the process of static software analysis. Said et al [ 32 ] presented a straggler identification model for distributed environments using machine learning. This model uses several parameters extracted by the execution of various types and large-scale jobs.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Extraction and Discovery about Web System Based on the Benchmark Application of Online Stock Trading System

Borowiec

Piszko

Rak

2023

Sensors

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Predicting workload characteristics could help web systems achieve elastic scaling and reliability by optimizing servers’ configuration and ensuring Quality of Service, such as increasing or decreasing used resources. However, a successful analysis using a simulation model and recognition and prediction of the behavior of the client presents a challenging task. Furthermore, the network traffic characteristic is a subject of frequent changes in modern web systems and the huge content of system logs makes it a difficult area for data mining research. In this work, we investigate prepared trace contents that are obtained from the benchmark of the web system. The article proposes traffic classification on the web system that is used to find the behavior of client classes. We present a case study involving workload analysis of an online stock trading application that is run in the cloud, and that processes requests from the designed generator. The results show that the proposed analysis could help us better understand the requests scenario and select the values of system and application parameters. Our work is useful for practitioners and researchers of log analysis to enhance service reliability.

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Section: Related Workmentioning

confidence: 99%