NDLI: Fast and Accurate Workload Characterization Using Locality Sensitive Hashing

Content Provider	IEEE Xplore Digital Library
Author	Islam, M.S. Gibson, M. Muzahid, A.
Copyright Year	2015
Description	Author affiliation: Comput. Sci., Univ. of Texas at San Antonio, San Antonio, TX, USA (Islam, M.S.; Gibson, M.; Muzahid, A.)
Abstract	Embedded applications are increasingly offloading their computations to a cloud data center. Determining an incoming application's sensitivity toward various shared resources is a major challenge. To this end, previous research attempts to characterize an incoming application's sensitivity toward interference on various resources (Source of Interference or SoI, for short) of a cloud system. Due to time constraints, the application's sensitivity is profiled in detail for only a small number of SoI, and the sensitivities for the remaining SoI are approximated by capitalizing on knowledge about some of the applications (i.e. training set) currently running in the system. A key drawback of previous approaches is that they have attempted to minimize the total error of the estimated sensitivities, however, various SoI do not behave the same as each other. For example, a 10% error in the estimate of SoI A may dramatically effect the QoS of an application whereas a 10% error in the estimate of SoI B may have a marginal effect. In this paper, we present a new method for workload characterization that considers these important issues. First, we compute an acceptable error for each SoI based on its effect on QoS, and our goal is to characterize an application so as to maximize the number of SoI that satisfy this acceptable error. Then we present a new technique for workload characterization based on Locality Sensitive Hashing (LSH). Our approach performs better than a state-of-the-art technique in terms of error rate (1.33 times better).
Starting Page	1192
Ending Page	1201
File Size	253886
Page Count	10
File Format	PDF
e-ISBN	9781479989379
DOI	10.1109/HPCC-CSS-ICESS.2015.249
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Sensitivity data center locality sensitive hashing Application characterization Quality of service Interference Servers Kernel
Content Type	Text
Resource Type	Article

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