NDLI: Black-box approach to capacity identification for multi-tier applications hosted on virtualized platforms

Content Provider	IEEE Xplore Digital Library
Author	Iqbal, W. Dailey, M.N. Carrera, D.
Copyright Year	2011
Description	Author affiliation: Computer Science and Information Management, Asian Institute of Technology, Thailand (Iqbal, W.; Dailey, M.N.) \|\| Technical University of Catalonia, Barcelona Supercomputing Center, Spain (Carrera, D.)
Abstract	In cloud-based Web application hosting environments, virtualization offers the potential to exploit dynamic resource provisioning and scaling to maintain service level agreements while minimizing resource utilization for a given workload. However, optimal proactive resource provisioning and scaling for a specific Web application require, at the least, a profile of the application's current workload and a model of the application's capacity under various resource configurations. Here we focus on multi-tier Web applications. The capacity of a multi-tier Web application varies substantially as the pattern of requests in the workload changes. In this paper, we propose and evaluate a black-box method for capacity prediction that first identifies workload patterns for a multi-tier Web application from access logs using unsupervised machine learning and then, based on those patterns, builds a model capable of predicting the application's capacity for any specific workload pattern. In an experimental evaluation, we compare a baseline method that predicts capacity without a model of the application-specific workload patterns to several regression models using the proposed workload identification method. All of the models based on workload pattern identification outperform the baseline method. The best model, a Gaussian process regression model, gives only 6.42% error. Cloud providers utilizing our method can proactively perform dynamic allocation of resources to multi-tier Web applications, meeting service level agreements at minimal cost.
Starting Page	111
Ending Page	117
File Size	920881
Page Count	7
File Format	PDF
ISBN	9781457716355
e-ISBN	9781457716379
e-ISBN	9781457716362
DOI	10.1109/CSC.2011.6138506
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Machine learning Benchmark testing Predictive models Time factors Resource management Monitoring
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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