Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Subject: search.filters.subject.performance evaluation

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    Performance Evaluation in Multi-Rate, Multi-Hop Communication Networks with Adaptive Routing
    (1998) Liu, Mingyan D.; Baras, John S.; Misra, Archan; ISR; CSHCN
    Accurate performance evaluation has always been an important issue in network design and analysis. Discrete event simulation has been known to be accurate but very time consuming. A particular performance metric of interest is the end-to-end blocking probability in a circuit-switched loss network. Various analytical approaches and approximation schemes have been suggested and among them, the fixed-point method, or reduced load method, has been receiving much attention. However, most of these schemes either consider only single traffic rate situations or multi-rate traffic under fixed routing. We develop an approximation scheme to estimate end-to-end blocking probability in a multi-rate multi-hop network with an adaptive routing scheme. The approximation results are compared with that of discrete event simulation. An example of application is also provided in which the proposed scheme is linked to the optimization tool CONSOL-OPTCAD to get network design trade-offs. This paper was presented at the "ATIRP ARL Federal Laboratory 2nd Annual Conference," February 5-6, 1998, University of Maryland, College Park campus.
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    Distributed Parallelism Considered Harmful
    (1992) Makowski, Armand M.; Nelson, R.; ISR
    We consider a model of a distributed parallel processing system that shows that parallel versus sequential processing is beneficial only under conditions of light load. Our results are valid under general assumptions on the number of processors, task service times and the information used to schedule jobs. Our model of a parallel processing system consists of a set of homogeneous processors each with private memory in which tasks queue before being served. Jobs arriving to the system consist of a random number of tasks which can be executed independently each other and we consider a job to be completed only after all of its component tasks have finished execution. a central dispatcher schedules the tasks on the processors at job arrival instants using information on the number of tasks currently scheduled on each processor. We model this system as a distributed fork/join queueing system and derive the structure of the individually optimal scheduling policy. Our results show that the individually optimal is a mixture of policies corresponding to sequential job execution (all tasks are scheduled on a single processor) and parallel scheduling (tasks are distributed among several processors in a manner that tends to equalize queue lengths). We show that, under conditions that include the case of moderate to heavy loads, the individually optimal scheduler schedules tasks according to the sequential policy which runs counter to the intuition that parallel processing is desirable. Because we do not include certain overheads associated with executing jobs in parallel in our model, our results are biased towards parallel rather than sequential processing. since we believe that systems are not typically underutilized, our results strongly suggest that it can be harmful to have parallel execution in distributed processing systems. Response time properties of the individually optimal scheduler are derived and compared to other scheduling policies.
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    Optimal Scheduling for a Distributed Parallel Processing Model
    (1992) Makowski, Armand M.; Nelson, R.; ISR
    We consider a model of a parallel processing system consisting of K distributed homogeneous processors each with private memory in which tasks queue before being served. Jobs arriving to the system consist of a set of tasks which can be executed independently of each other and we consider a job to be completed only after all of its component tasks have finished execution. A central dispatcher schedules the tasks on the processors at job arrival instants using information on the number of tasks currently scheduled on each processor. We model this system as a distributed fork/join queueing system and derive the structure of the individually optimal scheduling policy. Our results show that the individually optimal policy is a mixture of policies corresponding to sequential job execution (all tasks are scheduled on a single processor) and parallel scheduling (tasks are distributed among several processors in a manner that tends to equalize queue lengths). We show that, under conditions that include the case of moderate to heavy loads, the individually optimal scheduler schedules tasks according to the sequential policy which runs counter to the intuition that parallel processing is desirable. Because we do not include certain overheads associated with executing jobs in parallel in our model, our results are biased towards parallel rather than sequential processing. Thus our results strongly suggest that for actual distributed memory systems the benefits of parallel processing can be achieved only in conditions of light load. Response time properties of the individually optimal scheduler are derived and compared by simulation to other scheduling policies.