3D analytical modelling and iterative solution for high performance computing clusters
Article
Kirsal, Y., Kirsal Ever, Y., Mapp, G. and Raza, M. 2022. 3D analytical modelling and iterative solution for high performance computing clusters. IEEE Transactions on Cloud Computing. 10 (4), pp. 2238-2251. https://doi.org/10.1109/TCC.2021.3055119
Type | Article |
---|---|
Title | 3D analytical modelling and iterative solution for high performance computing clusters |
Authors | Kirsal, Y., Kirsal Ever, Y., Mapp, G. and Raza, M. |
Abstract | Mobile Cloud Computing enables the migration of services to the edge of the Internet. Therefore, high-performance computing clusters are widely deployed to improve computational capabilities of such environments. However, they are prone to failures and need analytical models to predict their behaviour in order to deliver desired quality-of-service and quality-of-experience to mobile users. This paper proposes a 3D analytical model and a problem-solving approach for sustainability evaluation of high-performance computing clusters. The proposed solution uses an iterative approach to obtain performance measurements to overcome the state space explosion problem. The availability modelling and evaluation of master and computing nodes are performed using a multi-repairman approach. The optimum number of repairmen is also obtained to get realistic results and reduce the overall cost. The proposed model is validated using discrete event simulation. The analytical approach is much faster and in good agreement with the simulations. The analysis focuses on mean queue length, throughput, and mean response time outputs. The maximum differences between analytical and simulation results in the considered scenarios of up to a billion states are less than1.149%,3.82%, and3.76%respectively. These differences are well within the5%of confidence interval of the simulation and the proposed model. |
Keywords | Analytical models; Quality of service; Computational modeling; Object oriented modeling; Cloud computing; Numerical models; Explosions |
Publisher | IEEE |
Journal | IEEE Transactions on Cloud Computing |
ISSN | |
Electronic | 2168-7161 |
Publication dates | |
Online | 28 Jan 2021 |
06 Dec 2022 | |
Publication process dates | |
Deposited | 08 Mar 2021 |
Accepted | 22 Jan 2021 |
Submitted | 27 Nov 2019 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TCC.2021.3055119 |
Web of Science identifier | WOS:000894810300001 |
Language | English |
https://repository.mdx.ac.uk/item/89474
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