Mean value analysis of mixed, multiple class BCMP networks with load dependent service stations☆
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Cited by (33)
Facilitating load-dependent queueing analysis through factorization
2021, Performance EvaluationCitation Excerpt :Unfortunately, despite a long history of work on this subject [12], when LLD stations are considered in multiclass closed networks, which are used to abstract systems with finite levels of parallelism, they become expensive to analyze. Indeed, commonly employed evaluation techniques for models with fixed-rate stations, such as the convolution algorithm (CA) [13–15] and mean value analysis (MVA) [16] or its approximate versions [17], are much more expensive to apply when extended to the LLD setting. These methods can also incur numerical instabilities [18], as we further discuss in Section 8.
Power control in saturated fork-join queueing systems
2017, Performance EvaluationQD-AMVA: Evaluating systems with queue-dependent service requirements
2015, Performance EvaluationCitation Excerpt :To support the raising interest for characterizing load dependence in queueing networks, this paper introduces QD-AMVA, a novel AMVA algorithm for queue-dependent models, i.e., models of systems where the service requirements depend on the number and mix of requests in execution at the resource where the request executes. Although AMVA methods for load-dependent models exist [1], these methods have severe limitations in the multiclass setting, including excessive computational requirements, numerical instabilities, and being restricted to the simplest types of queue-dependence [16,17]. This has led to multiclass state-dependent networks being seldom used in real-world studies.
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2006, International Journal of Production EconomicsAn approximate MVA algorithm for exponential, class-dependent multiple servers
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This work was supported in part by NSF Grant MCS-80006308 and Nato Research Grant 280.81.