Efficient On-Demand Operations in Large-Scale Infrastructures

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.

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
• Nahrstedt, Klara
• Abdelzaher, Tarek F.
• Milojicic, Dejan

• Distributed Systems

Statement dc:rights

• Copyright 2009 Steven Y. Ko

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/13386

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/13386