University of Illinois Urbana-Champaign

Relaxed Model Characterization of Degree-Guided Task Assignment

Qian, Junle

This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.

Permalink

https://hdl.handle.net/2142/46517

Description

Title
Relaxed Model Characterization of Degree-Guided Task Assignment
Author(s)
Qian, Junle
Contributor(s)
Lu, Yi
Issue Date
2012-12
Keyword(s)
  • cluster computing
  • cluster computing algorithms
  • cluster computing performance
  • task assignment
Abstract
Cluster computing systems are popular in IT industries for data-intensive applications and services. In such systems, the task file is divided into data chunks, which are replicated and distributed to a number of servers. Servers have to remotely acquire necessary file chunks that are not present locally before they proceed to relevant computation. Due to the fact that network connections have inevitable latencies, servers complete tasks with local data faster than those with remote data. Therefore, task assignment with data locality constraints plays an essential role in cluster computing systems. In this research, we have proposed a degree-guided assignment algorithm that significantly outperforms the random server algorithm at light load. However, the performance of this algorithm drops sharply when the system reaches the queuing threshold. We have begun to look for enhancements that solve the problem of performance plunge at high loads. Specifically, my work in this thesis concentrates on characterizing the performance of the degree-guided algorithm in a relaxed model, where leftover tasks are considered as fresh ones in a time interval. This will allow us to characterize and analyze the degree-guided algorithm from an approaching perspective.
Type of Resource
text
Language
en
Permalink
http://hdl.handle.net/2142/46517

Owning Collections

Senior Theses - Electrical and Computer Engineering PRIMARY
The best of ECE undergraduate research