Title page for ETD etd-07042011-201553


Type of Document Dissertation
Author Yun, Zhifeng
Author's Email Address zyun@cct.lsu.edu
URN etd-07042011-201553
Title Integrating Multiple Clusters for Compute-intensive Applications
Degree Doctor of Philosophy (Ph.D.)
Department Electrical & Computer Engineering
Advisory Committee
Advisor Name Title
Ramanujam, Jagannathan Committee Chair
Katz, Daniel S Committee Co-Chair
Allen, Gabrielle D Committee Member
Chen, Jianhua Committee Member
Vaidyanathan, Ramachandran Committee Member
Ditusa, John F Dean's Representative
Keywords
  • scheduling
  • distributed systems
  • execution model
  • load balancing
  • parallel systems
Date of Defense 2011-05-09
Availability unrestricted
Abstract
Multicluster grids provide one promising solution to satisfying the growing computational demands of compute-intensive applications. However, it is challenging to seamlessly integrate all participating clusters in different domains into a single virtual computational platform. In order to fully utilize the capabilities of multicluster grids, computer scientists need to deal with the issue of joining together participating autonomic systems practically and efficiently to execute grid-enabled applications.

Driven by several compute-intensive applications, this theses develops a multicluster grid management toolkit called Pelecanus to bridge the gap between user's needs and the system's heterogeneity. Application scientists will be able to conduct very large-scale execution across multiclusters with transparent QoS assurance. A novel model called DA-TC (Dynamic Assignment with Task Containers) is developed and is integrated into Pelecanus. This model uses the concept of a task container that allows one to decouple resource allocation from resource binding. It employs static load balancing for task container distribution and dynamic load balancing for task assignment. The slowest resources become useful rather than

be bottlenecks in this manner. A cluster abstraction is implemented, which not only provides various cluster information for the DA-TC execution model, but also can be used as a standalone toolkit to monitor and evaluate the clusters' functionality and performance.

The performance of the proposed DA-TC model is evaluated

both theoretically and experimentally. Results demonstrate the

importance of reducing queuing time in decreasing the total

turnaround time for an application. Experiments were conducted to understand the performance of various aspects of the DA-TC

model. Experiments showed that our model could significantly reduce turnaround time and increase resource utilization for our targeted application scenarios. Four applications are implemented as case studies to determine the applicability of the DA-TC model. In each case the turnaround time is greatly reduced, which demonstrates that the DA-TC model is efficient

for assisting application scientists in conducting their research.

In addition, virtual resources were integrated into the DA-TC model for application execution. Experiments show that the execution model proposed in this thesis can work seamlessly with multiple hybrid grid/cloud resources to achieve reduced turnaround time.

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