Title page for ETD etd-06152006-165329


Type of Document Master's Thesis
Author Bergstrom, Craig Casey
URN etd-06152006-165329
Title The Distributed Open Network Emulator: Applying Relativistic Time
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Varadarajan, Srinidhi Committee Chair
Back, Godmar V. Committee Member
Ribbens, Calvin J. Committee Member
Keywords
  • Network Emulation
  • Simulation
  • Virtual Time
  • Distributed Simulation
  • Simulation Time
  • Network Simulation
Date of Defense 2006-05-15
Availability unrestricted
Abstract
The increasing scale and complexity of network applications and protocols motivates the need for tools to aid in the understanding of network dynamics at similarly large scales. While current network simulation tools achieve large scale modeling, they do so by ignoring much of the intra-program state that plays an important role in the overall system's behavior. This work presents The Distributed Open Network Emulator, a scalable distributed network model that incorporates application program state to achieve high fidelity modeling.

The Distributed Open Network Emulator, or DONE for short, is a parallel and distributed network simulation-emulation hybrid that achieves both scalability and the capability to run existing application code with minimal modification. These goals are accomplished through the use of a protocol stack extracted from the Linux kernel, a new programming model based on C, and a scaled real-time method for distributed synchronization.

One of the primary challenges in the development of DONE was in reconciling the opposing requirements of emulation and simulation. Emulated code directly executes in real-time which progresses autonomously. In contrast, simulation models are forced ahead by the execution of events, an explicitly controlled mechanism. Relativistic time is used to integrate these two paradigms into a single model while providing efficient distributed synchronization.

To demonstrate that the model provides the desired traits, a series of experiments are described. They show that DONE can provide super-linear speedup on small clusters, nearly linear speedup on moderate sized clusters, and accurate results when tuned appropriately.

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