DocumentCode :
704261
Title :
Online Spike Detection in Cloud Workloads
Author :
Mehta, Amardeep ; Durango, Jonas ; Tordsson, Johan ; Elmroth, Erik
Author_Institution :
Dept. of Comput. Sci., Umea Univ., Umea, Sweden
fYear :
2015
fDate :
9-13 March 2015
Firstpage :
446
Lastpage :
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 falsely report spikes.
Keywords :
adaptive signal processing; cloud computing; adaptive signal processing; cloud resources; cloud workloads; corrective management actions; load spikes; online spike detection; Adaptation models; Detectors; Dispersion; Noise measurement; Predictive models; Smoothing methods; White noise; Cloud workload; cusum test; spike detection; workload modeling; workload spike;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Engineering (IC2E), 2015 IEEE International Conference on
Conference_Location :
Tempe, AZ
Type :
conf
DOI :
10.1109/IC2E.2015.50
Filename :
7092959
Link To Document :
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