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  1. Transactions on Autonomous and Adaptive Systems (TAAS)
  2. ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 7
  3. Issue 4(Special Section: Extended Version of SASO 2011 Best Paper), December 2012
  4. The XtreemOS Resource Selection Service
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ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 11
ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 10
ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 9
ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 8
ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 7
Issue 4(Special Section: Extended Version of SASO 2011 Best Paper), December 2012
Bio-Inspired P2P Systems: The Case of Multidimensional Overlay
Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems
The XtreemOS Resource Selection Service
A Lightweight Method for Automated Design of Convergence in Network Protocols
Axiomatization of Socio-Economic Principles for Self-Organizing Institutions: Concepts, Experiments and Challenges
Issue 3, September 2012
Issue 2, July 2012
Issue 1(Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms), April 2012
ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 6
ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 5
ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 4
ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 3
ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 2
ACM Transactions on Autonomous and Adaptive Systems (TAAS) : Volume 1

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The XtreemOS Resource Selection Service

Content Provider ACM Digital Library
Author Stratan, Corina Costa, Paolo Napper, Jeff Pierre, Guillaume Sacha, Jan
Copyright Year 2013
Abstract Many large-scale utility computing infrastructures comprise heterogeneous hardware and software resources. This raises the need for scalable resource selection services that identify resources that match application requirements. Such a service must provide an efficient lookup in spite of changing resource attributes such as disk size, changing application requirements such as installed software libraries, and changing system composition as resources join or leave. We present a fully decentralized, self-managing Resource Selection Service (RSS) algorithm by which resources autonomously select themselves when their attributes match a query. An application specifies what it expects from a resource by means of a conjunction of (attribute,value-range) pairs, which are matched against the attribute values of resources. The set of search attributes can also be updated online to reflect new requirements. We show that our solution scales in the number of resources and in the number of attributes, while being relatively insensitive to churn and other membership changes like node failures. Our RSS continuously self-adapts its routing structure in response to variations in the distribution of node attributes and queries. We show that this autonomous optimization maintains performance and availability in a long-lived service even when the set of application requirements used to select resources changes.
Starting Page 1
Ending Page 32
Page Count 32
File Format PDF
ISSN 15564665
e-ISSN 15564703
DOI 10.1145/2382570.2382573
Volume Number 7
Issue Number 4
Journal ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-12-01
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword RSS XtreemOS Resource selection Self-adaptation
Content Type Text
Resource Type Article
Subject Control and Systems Engineering Software
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