Standard
Predicting Cloud Performance Using Real-time VM-level Metrics. / Tian, Jihua
; Elhabbash, Abdessalam; Elkhatib, Yehia.
Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022. IEEE, 2023. p. 1165-1172.
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Tian, J
, Elhabbash, A & Elkhatib, Y 2023,
Predicting Cloud Performance Using Real-time VM-level Metrics. in
Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022. IEEE, pp. 1165-1172, 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022, Chengdu, China,
18/12/22.
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00184APA
Vancouver
Tian J
, Elhabbash A, Elkhatib Y.
Predicting Cloud Performance Using Real-time VM-level Metrics. In Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022. IEEE. 2023. p. 1165-1172 Epub 2022 Dec 18. doi: 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00184
Author
Bibtex
@inproceedings{69c2e754aebe49b3af5e240354cad91e,
title = "Predicting Cloud Performance Using Real-time VM-level Metrics",
abstract = "The vast range of cloud service offerings can easily overwhelm users and cause them to select ones that are unsuitable for their needs. As such, the literature has a number of proposals to predict application performance based on a history of executing a certain application or benchmark. However, this requires significant cost to pre-run the application on different service levels before identifying the most suitable one. We propose a machine learning model that enables a cloud user to select the optimal cloud service based on real-time execution without the need to do an exhaustive search. We develop and test this model using a popular benchmark suite on Microsoft Azure, a leading cloud provider. The key insight of this work is that fluctuations in rather than the absolute amount of utilization levels of CPU and memory can be strongly indicative of how well an application is executing.",
keywords = "Cloud computing, Machine Learning, Service Level Objectives",
author = "Jihua Tian and Abdessalam Elhabbash and Yehia Elkhatib",
year = "2023",
month = mar,
day = "28",
doi = "10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00184",
language = "English",
pages = "1165--1172",
booktitle = "Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022",
publisher = "IEEE",
note = "24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022 ; Conference date: 18-12-2022 Through 20-12-2022",
}
RIS
TY - GEN
T1 - Predicting Cloud Performance Using Real-time VM-level Metrics
AU - Tian, Jihua
AU - Elhabbash, Abdessalam
AU - Elkhatib, Yehia
PY - 2023/3/28
Y1 - 2023/3/28
N2 - The vast range of cloud service offerings can easily overwhelm users and cause them to select ones that are unsuitable for their needs. As such, the literature has a number of proposals to predict application performance based on a history of executing a certain application or benchmark. However, this requires significant cost to pre-run the application on different service levels before identifying the most suitable one. We propose a machine learning model that enables a cloud user to select the optimal cloud service based on real-time execution without the need to do an exhaustive search. We develop and test this model using a popular benchmark suite on Microsoft Azure, a leading cloud provider. The key insight of this work is that fluctuations in rather than the absolute amount of utilization levels of CPU and memory can be strongly indicative of how well an application is executing.
AB - The vast range of cloud service offerings can easily overwhelm users and cause them to select ones that are unsuitable for their needs. As such, the literature has a number of proposals to predict application performance based on a history of executing a certain application or benchmark. However, this requires significant cost to pre-run the application on different service levels before identifying the most suitable one. We propose a machine learning model that enables a cloud user to select the optimal cloud service based on real-time execution without the need to do an exhaustive search. We develop and test this model using a popular benchmark suite on Microsoft Azure, a leading cloud provider. The key insight of this work is that fluctuations in rather than the absolute amount of utilization levels of CPU and memory can be strongly indicative of how well an application is executing.
KW - Cloud computing
KW - Machine Learning
KW - Service Level Objectives
U2 - 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00184
DO - 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00184
M3 - Conference contribution/Paper
AN - SCOPUS:85152229806
SP - 1165
EP - 1172
BT - Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
PB - IEEE
T2 - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
Y2 - 18 December 2022 through 20 December 2022
ER -
