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Network research generally requires a simulation or emulation environment to test protocol implementations, to evaluate the performance of a scheme or a system, and to study complicated and highly varying network operations. For large network simulation, simulators will consume a large amount of time and memory, and its result is largely based on some modeling assumptions that may not hold in the real world. Emulators are difficult to scale for large network emulation because of the high cost of equipment if a one-to-one mapping scheme is employed. Otherwise, the target network has to be abstracted to a single router modeled with some performance metrics. In this paper, we present a distributed IP network emulator cluster EMPOWER, which not only can be used to emulate a large network with a limited number of commodity computers, but also can generate user-defined arbitrary network conditions and traffic dynamics at packet level for specific test scenarios. EMPOWER is highly scalable in that each emulator node could be configured to emulate multiple network nodes, and the increment of the number of emulator nodes does not affect emulation validity. Some significant research issues such as network mapping and mobile wireless network emulation are discussed and addressed. Preliminary emulation results show that EMPOWER is capable of assisting the study of both wireline and wireless network protocols and applications. [ABSTRACT FROM AUTHOR]

This paper presents a Markov chain model to analyze the performance of shared-resource array processors for multiple vector processing. Such a parallel processor contains multiple control units sharing a resource pool of processing elements and operating with multiple single-instruction multiple-data streams (MSIMD). In the steady state, the Markov model corresponds to a two-dimensional Markov chain, which can be expressed by a set of equilibrium equations. An iterative method is developed to solve the Markov chain after projecting the equilibrium equations onto a one-dimensional state space. The convergence rate of the iterative method can be greatly enhanced by choosing starting values corresponding to the approximated analytical results obtained earlier by the authors. [ABSTRACT FROM AUTHOR]