Yves J. Bationo, Naser Ezzati-Jivan and Michel Dagenais
Paper (2018)
Open Access document in PolyPublie |
|
Open Access to the full text of this document Accepted Version Terms of Use: All rights reserved Download (201kB) |
Abstract
With the increase of cloud infrastructure complexity, the origin of service deterioration is difficult to detect because issues may occur at the different layer of the system. We propose a multi-layer tracing approach to gather all the relevant information needed for a full workflow analysis. The idea is to collect trace events from all the cloud nodes to follow users' requests from the cloud interface to their execution on the hardware. Our approach involves tracing OpenStack's interfaces, the virtualization layer, and the host kernel space to perform analysis and show abnormal tasks and the main causes of latency or failures in the system. Experimental results about virtual machines live migration confirm that we are able to analyse services efficiency by locating platforms' weakest links.
Uncontrolled Keywords
Cloud; OpenStack; QEMU; Tracing, LTTNg
Subjects: |
2700 Information technology > 2706 Software engineering 2700 Information technology > 2715 Optimization |
---|---|
Department: | Department of Computer Engineering and Software Engineering |
Funders: | CRSNG/NSERC |
Grant number: | CRDPJ468687-14 |
PolyPublie URL: | https://publications.polymtl.ca/4201/ |
Conference Title: | IEEE International Conference on Consumer Electronics (ICCE 2018) |
Conference Location: | Las Vegas, NV, USA |
Conference Date(s): | 2018-01-12 - 2018-01-14 |
Publisher: | IEEE |
DOI: | 10.1109/icce.2018.8326353 |
Official URL: | https://doi.org/10.1109/icce.2018.8326353 |
Date Deposited: | 17 Feb 2020 12:32 |
Last Modified: | 28 Sep 2024 09:56 |
Cite in APA 7: | Bationo, Y. J., Ezzati-Jivan, N., & Dagenais, M. (2018, January). Efficient cloud tracing: From very high level to very low level [Paper]. IEEE International Conference on Consumer Electronics (ICCE 2018), Las Vegas, NV, USA (6 pages). https://doi.org/10.1109/icce.2018.8326353 |
---|---|
Statistics
Total downloads
Downloads per month in the last year
Origin of downloads
Dimensions