NDLI: Adaptive Scheduling on Power-Aware Managed Data-Centers Using Machine Learning

Content Provider	IEEE Xplore Digital Library
Author	Berral, J.L. Gavalda, R. Torres, J.
Copyright Year	2011
Abstract	Energy-related costs have become one of the major economic factors in IT data-centers, and companies and the research community are currently working on new efficient power-aware resource management strategies, also known as "Green IT". Here we propose a framework for autonomic scheduling of tasks and web-services on cloud environments, optimizing the profit taking into account revenue for task execution minus penalties for service-level agreement violations, minus power consumption cost. The principal contribution is the combination of consolidation and virtualization technologies, mathematical optimization methods, and machine learning techniques. The data-center infrastructure, tasks to execute, and desired profit are casted as a mathematical programming model, which can then be solved in different ways to find good task scheduling. We use an exact solver based on mixed linear programming as a proof of concept but, since it is an NP-complete problem, we show that approximate solvers provide valid alternatives for finding approximately optimal schedules. The machine learning is used to estimate the initially unknown parameters of the mathematical model. In particular, we need to predict a priori resource usage (such as CPU consumption) by different tasks under current workloads, and estimate task service-level-agreement (such as response time) given workload features, host characteristics, and contention among tasks in the same host. Experiments show that machine learning algorithms can predict system behavior with acceptable accuracy, and that their combination with the exact or approximate schedulers manages to allocate tasks to hosts striking a balance between revenue for executed tasks, quality of service, and power consumption.
Starting Page	66
Ending Page	73
File Size	202819
Page Count	8
File Format	PDF
ISBN	9781457719042
ISSN	15505510
e-ISBN	9780769545721
DOI	10.1109/Grid.2011.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-21
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time factors Power demand Mathematical model Load modeling Machine learning Predictive models Web services SLA Data centers Energy Heuristics Machine Learning Scheduling
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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