NDLI: A Predictive Method for Identifying Optimum Cloud Availability Zones

Content Provider	IEEE Xplore Digital Library
Author	Unuvar, M. Doganata, Y. Steinder, M. Tantawi, A. Tosi, S.
Copyright Year	2014
Description	Author affiliation: Univ. of Modena & Reggio Emilia, Modena, Italy (Tosi, S.) \|\| IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA (Unuvar, M.; Doganata, Y.; Steinder, M.; Tantawi, A.)
Abstract	Cloud service providers enable enterprises with the ability to place their business applications into availability zones across multiple locations worldwide. While this capability helps achieve higher availability with smaller failure rates, business applications deployed across these independent zones may experience different Quality of Service (QoS) due to heterogeneous physical infrastructures. Since the perceived QoS against specific requirements are not usually advertised by cloud providers, selecting an availability zone that would best satisfy the user requirements is a challenge. In this paper, we introduce a predictive approach to identify the cloud availability zone that maximizes satisfaction of an incoming request against a set of requirements. The predictive models are built from historical usage data for each availability zone and are updated as the nature of the zones and requests change. Simulation results show that our method successfully predicts the unpublished zone behavior from historical data and identifies the availability zone that maximizes user satisfaction against specific requirements.
Starting Page	72
Ending Page	79
File Size	828030
Page Count	8
File Format	PDF
ISSN	21596190
e-ISBN	9781479950638
DOI	10.1109/CLOUD.2014.20
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Availability Training Quality of service Cloud Predictive models Multiple data centers Vectors Data models Performance analysis Predictive analytics Availability zones
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

Cloud Analytics for Wireless Metric Prediction - Framework and Performance

CLUE: System trace analytics for cloud service performance diagnosis

Prediction-Based Admission Control for IaaS Clouds with Multiple Service Classes

Configurable and Extensible Multi-flows for Providing Analytics as a Service on the Cloud

Identifying Employees for Re-skilling Using an Analytics-Based Approach

Modeling Availability in Clouds for Mobile Computing

ASAP: A Self-Adaptive Prediction System for Instant Cloud Resource Demand Provisioning

SMART: An Application Framework for Real Time Big Data Analysis on Heterogeneous Cloud Environments

Fast Modeling of Analytics Workloads for Big Data Services

A Predictive Method for Identifying Optimum Cloud Availability Zones

Similar Documents

Cloud Analytics for Wireless Metric Prediction - Framework and Performance

CLUE: System trace analytics for cloud service performance diagnosis

Prediction-Based Admission Control for IaaS Clouds with Multiple Service Classes

Configurable and Extensible Multi-flows for Providing Analytics as a Service on the Cloud

Identifying Employees for Re-skilling Using an Analytics-Based Approach

Modeling Availability in Clouds for Mobile Computing

ASAP: A Self-Adaptive Prediction System for Instant Cloud Resource Demand Provisioning

SMART: An Application Framework for Real Time Big Data Analysis on Heterogeneous Cloud Environments

Fast Modeling of Analytics Workloads for Big Data Services

A Predictive Method for Identifying Optimum Cloud Availability Zones