Usage of task and data parallelism for finding the lower boundary vectors in a stochastic-flow network

Forghani-elahabad, Majid; Francesquini, Emílio

doi:10.1016/j.ress.2023.109417

Cited by 11 publications

(1 citation statement)

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“…It is the probability of transmitting at least a given demand for d units of flow/data/commodity from node one (source) to node n (destination). Numerous exact and approximative algorithms have been proposed in the literature to compute this indicator [8,10,15,20,[22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Using a Node–Child Matrix to Address the Quickest Path Problem in Multistate Flow Networks under Transmission Cost Constraints

Forghani-elahabad,

Alsalami

2023

Mathematics

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The quickest path problem in multistate flow networks, which is also known as the quickest path reliability problem (QPRP), aims at calculating the probability of successfully sending a minimum of d flow units/data/commodity from a source node to a destination node via one minimal path (MP) within a specified time frame of T units. Several exact and approximative algorithms have been proposed in the literature to address this problem. Most of the exact algorithms in the literature need prior knowledge of all of the network’s minimal paths (MPs), which is considered a weak point. In addition to the time, the budget is always limited in real-world systems, making it an essential consideration in the analysis of systems’ performance. Hence, this study considers the QPRP under cost constraints and provides an efficient approach based on a node–child matrix to address the problem without knowing the MPs. We show the correctness of the algorithm, compute the complexity results, illustrate it through a benchmark example, and describe our extensive experimental results on one thousand randomly generated test problems and well-established benchmarks to showcase its practical superiority over the available algorithms in the literature.

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