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Detecting Discontinuities in Large-Scale Systems
BibTeX
@MISC{Malik_detectingdiscontinuities,
author = {Haroon Malik and Ian J. Davis and Michael W. Godfrey and Douglas Neuse and Serge Mankovskii},
title = {Detecting Discontinuities in Large-Scale Systems},
year = {}
}
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Abstract
Abstract—Cloud providers and data centers rely heavily on forecasts to accurately predict future workload. This information helps them in appropriate virtualization and cost-effective provisioning of the infrastructure. The accuracy of a forecast greatly depends upon the merit of performance data fed to the underlying algorithms. One of the fundamental problems faced by analysts in preparing data for use in forecasting is the timely identification of data discontinuities. A discontinuity is an abrupt change in a time-series pattern of a performance counter that persists but does not recur. Analysts need to identify discontinuities in performance data so that they can a) remove the discontinuities from the data before building a forecast model and b) retrain an existing forecast model on the performance data from the point in time where a discontinuity occurred. There exist several approaches and tools to help analysts identify anomalies in performance data. However, there exists no automated approach to assist data center operators in detecting discontinuities in the first place. In this paper, we present and evaluate our proposed approach to help data center analysts and cloud providers automatically detect discontinuities. A case study on the performance data obtained from a large cloud provider and performance tests conducted using an open source benchmark system show that our proposed approach provides an accuracy of 92 % and does not require any domain knowledge to operate.
Keyphrases
performance data large-scale system forecast model abstract cloud provider cost-effective provisioning large cloud provider time-series pattern data center operator case study data center analyst first place appropriate virtualization abrupt change automated approach open source benchmark system detect discontinuity data discontinuity domain knowledge performance counter exist several approach data center performance test fundamental problem cloud provider predict future workload timely identification
