Online Spike Detection in Cloud Workloads
(2015) 2nd IEEE Workshop on Cloud Analytics p.446-451- Abstract
- We investigate methods for detection of rapid workload increases (load spikes) for cloud workloads. Such rapid and unexpected workload spikes are a main cause for poor performance or even crashing applications as the allocated cloud resources become insufficient. To detect the spikes early is fundamental to perform corrective management actions, like allocating additional resources, before the spikes become large enough to cause problems. For this, we propose a number of methods for early spike detection, based on established techniques from adaptive signal processing. A comparative evaluation shows, for example, to what extent the different methods manage to detect the spikes, how early the detection is made, and how frequently they... (More)
- We investigate methods for detection of rapid workload increases (load spikes) for cloud workloads. Such rapid and unexpected workload spikes are a main cause for poor performance or even crashing applications as the allocated cloud resources become insufficient. To detect the spikes early is fundamental to perform corrective management actions, like allocating additional resources, before the spikes become large enough to cause problems. For this, we propose a number of methods for early spike detection, based on established techniques from adaptive signal processing. A comparative evaluation shows, for example, to what extent the different methods manage to detect the spikes, how early the detection is made, and how frequently they falsely report spikes. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5151925
- author
- Mehta, Amardeep ; Dürango, Jonas LU ; Tordsson, Johan and Elmroth, Erik
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- cloud, cloud workload, workload spike, spike detection
- host publication
- [Host publication title missing]
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2nd IEEE Workshop on Cloud Analytics
- conference location
- Tempe, AZ, United States
- conference dates
- 2015-03-12
- external identifiers
-
- scopus:84944312023
- wos:000380449000072
- DOI
- 10.1109/IC2E.2015.50
- project
- EIT_VR CLOUD Cloud Control
- language
- English
- LU publication?
- yes
- id
- 89c1b8a4-7197-40d9-97a7-0fb09987bb3f (old id 5151925)
- date added to LUP
- 2016-04-04 10:51:09
- date last changed
- 2022-05-17 03:33:36
@inproceedings{89c1b8a4-7197-40d9-97a7-0fb09987bb3f,
abstract = {{We investigate methods for detection of rapid workload increases (load spikes) for cloud workloads. Such rapid and unexpected workload spikes are a main cause for poor performance or even crashing applications as the allocated cloud resources become insufficient. To detect the spikes early is fundamental to perform corrective management actions, like allocating additional resources, before the spikes become large enough to cause problems. For this, we propose a number of methods for early spike detection, based on established techniques from adaptive signal processing. A comparative evaluation shows, for example, to what extent the different methods manage to detect the spikes, how early the detection is made, and how frequently they falsely report spikes.}},
author = {{Mehta, Amardeep and Dürango, Jonas and Tordsson, Johan and Elmroth, Erik}},
booktitle = {{[Host publication title missing]}},
keywords = {{cloud; cloud workload; workload spike; spike detection}},
language = {{eng}},
pages = {{446--451}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Online Spike Detection in Cloud Workloads}},
url = {{https://lup.lub.lu.se/search/files/5636144/5154398.pdf}},
doi = {{10.1109/IC2E.2015.50}},
year = {{2015}},
}