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Online Spike Detection in Cloud Workloads

Mehta, Amardeep; Dürango, Jonas LU ; Tordsson, Johan and Elmroth, Erik (2015) 2nd IEEE Workshop on Cloud Analytics In [Host publication title missing] 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:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
cloud, cloud workload, workload spike, spike detection
in
[Host publication title missing]
pages
6 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
2nd IEEE Workshop on Cloud Analytics
external identifiers
  • scopus:84944312023
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
2015-03-09 09:32:24
date last changed
2017-04-09 04:38:03
@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]},
  keyword      = {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          = {http://dx.doi.org/10.1109/IC2E.2015.50},
  year         = {2015},
}