Efficient on -Demand Operations in Large-Scale Infrastructures

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2009

Author dc:creator

• Ko, Steven Y.

Contributors dc:contributor

• Gupta, Indranil

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3392096

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81860