Type of Document Master's Thesis Author Kodukula, Surya Ravikiran Author's Email Address email@example.com URN etd-02072002-141811 Title An Adaptive Time Window Algorithm for Large Scale Network Emulation Degree Master of Science Department Computer Science Advisory Committee
Advisor Name Title Varadarajan, Srinidhi Committee Chair Arthur, James D. Committee Member Nance, Richard E. Committee Member Keywords
- Parallel Simulation
- Time Window
- Optimistic Algorithm
Date of Defense 2002-01-25 Availability unrestricted AbstractWith the continuing growth of the Internet and network protocols, there is a need for Protocol Development Environments. Simulation environments like ns and OPNET require protocol code to be rewritten in a discrete event model. Direct Code Execution Environments (DCEE) solve the Verification and Validation problems by supporting the execution of unmodified protocol code in a controlled environment. Open Network Emulator (ONE) is a system supporting Direct Code Execution in a parallel environment - allowing unmodified protocol code to run on top of a parallel simulation layer, capable of simulating complex network topologies. Traditional approaches to the problem of Parallel Discrete Event Simulation (PDES) broadly fall into two categories. Conservative approaches allow processing of events only after it has been asserted that the event handling would not result in a causality error. Optimistic approaches allow for causality errors and support means of restoring state – i.e., rollback. All standard approaches to the problem of PDES are either flawed by their assumption of existing event patterns in the system or cannot be applied to ONE due to their restricted analysis on simplified models like queues and Petri-nets.
The Adaptive Time Window algorithm is a bounded optimistic parallel simulation algorithm with the capability to change the degree of optimism with changes in the degree of causality in the network. The optimism at any instant is bounded by the amount of virtual time called the time window. The algorithm assumes efficient rollback capabilities supported by the ‘Weaves’ framework. The algorithm is reactive and responds to changes in the degree of causality in the system by adjusting the length of its time window. With sufficient history gathered the algorithm adjusts to the increasing causality in the system with a small time window (conservative approach) and increases to a higher value (optimistic approach) during idle periods.
The problem of splitting the entire simulation run into time windows of arbitrary length, whereby the total number of rollbacks in the system is minimal, is NP-complete. The Adaptive Time Window algorithm is compared against offline greedy approaches to the NP-complete problem called Oracle Computations. The total number of rollbacks in the system and the total execution time for the Adaptive Time Window algorithm were comparable to the ones for Oracle Computations.
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