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Efficient On-Demand Operations in Large-Scale Infrastructures

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Title: Efficient On-Demand Operations in Large-Scale Infrastructures
Author(s): Ko, Steven Y.
Doctoral Committee Chair(s): Gupta, Indranil
Doctoral Committee Member(s): Nahrstedt, Klara; Abdelzaher, Tarek F.; Milojicic, Dejan
Department / Program: Computer Science
Discipline: Computer Science
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: Ph.D.
Genre: Dissertation
Subject(s): Distributed Systems
Abstract: In large-scale distributed infrastructures such as clouds, Grids, peer-to-peer systems, and wide-area testbeds, users and administrators typically desire to perform on-demand operations that deal with the most up-to-date state of the infrastructure. However, the scale and dynamism present in the operating environment make it challenging to support on-demand operations efficiently, i.e., in a bandwidth- and response-efficient manner. This dissertation discusses several on-demand operations, challenges associated with them, and system designs that meet these challenges. Specifically, we design and implement techniques for 1) on-demand group monitoring that allows users and administrators of an infrastructure to query and aggregate the up-to-date state of the machines (e.g., CPU utilization) in one or multiple groups, 2) on-demand storage for intermediate data generated by dataflow programming paradigms running in clouds, 3) on-demand Grid scheduling that makes worker-centric scheduling decisions based on the current availability of compute nodes, and 4) on-demand key/value pair lookup that is overlay-independent and perturbation-resistant. We evaluate these on-demand operations using large-scale simulations with traces gathered from real systems, as well as via deployments over real testbeds such as Emulab and PlanetLab.
Issue Date: 2009-08-04
Genre: Dissertation / Thesis
Type: Text
Language: English
URI: http://hdl.handle.net/2142/13386
Rights Information: Copyright 2009 Steven Y. Ko
Date Available in IDEALS: 2009-08-05
 

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