Statistical Mechanics-Inspired Modeling of Heterogeneous Packet Transmission in Communication Networks

Sarkar, Soumik; Mukherjee, Kushal; Ray, Asok; Srivastav, Abhishek; Wettergren, Thomas A.

doi:10.1109/tsmcb.2012.2186611

Cited by 8 publications

(15 citation statements)

References 18 publications

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“…Many researchers [5][6][7][8][9][10][11][12][13][14] have used simulation to investigate congestion spread in network topologies. These researchers often find congestion can be modeled as a percolation process on a graph, spreading slowly under increasing load until a critical point.…”

Section: ----------------------------------------------------------57mentioning

confidence: 99%

“…Researchers used either deterministic or probabilistic topologies. The most popular deterministic topology was a square lattice, either open [8,13] or folded into a toroid [5-7, 9, 11]. Rykalova et al [12] also used a ring.…”

Section: Similarities and Variations Among Previous Studiesmentioning

confidence: 99%

“…The most popular approach was to allow every node to be a packet source and sink, as well as a router [9][10][11][12]14]. Sarkar et al [13] restricted sources and sinks to the network edge, while Mukherjee and Manna [8] placed sources at the top edge of a lattice and sinks at the bottom edge. Other researchers [5][6][7] assigned nodes to be a source/sink or router with a biased coin flip.…”

Section: Similarities and Variations Among Previous Studiesmentioning

confidence: 99%

“…All surveyed studies generated loads by having sources inject individual packets, where each packet is destined for a randomly selected sink. The most popular strategy [5][6][7][8][9][12][13] was for each source to generate a packet per time step (p/ts) with a specified probability. A few studies [10][11]14] generated a fixed number of p/ts and randomly assigned the packets to sources.…”

Section: Similarities and Variations Among Previous Studiesmentioning

confidence: 99%

“…One study [10] used bounded last-in, first-out (LIFO) queues. One study [13] used bounded FIFO queues, where the oldest packet was dropped when a packet arrived at a full queue. Most studies [5-8, 12, 13] selected next hop based on shortest-path first (SPF) in hops.…”

Section: Similarities and Variations Among Previous Studiesmentioning

confidence: 99%

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The Influence of Realism on Congestion in Network Simulations

Dabrowski¹,

Mills²

2016

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Abstract. Many researchers have used simulation to investigate the spread of congestion in networks. These researchers often find that congestion can be modeled as a percolation process, spreading slowly under increasing load until a critical point. After the critical point, congestion spreads quickly throughout the entire network. The researchers also identify various measureable signals that arise around the critical point. These findings appear quite promising as a theoretical basis for monitoring regimes that network operators could deploy to warn of impending congestion collapse. Yet questions surround the extant research because the findings arise from models that are quite abstract. Such models bear little resemblance to networks deployed based on modern technology.We explore these questions by examining the influence of realism on the spread of congestion in network simulations. We begin with an abstract network simulation, taken from the literature, and add elements of realism in various combinations, culminating with a high-fidelity simulation, also taken from the literature. By comparing patterns of congestion among combinations, we make four main contributions. First, we illustrate that congestion spread in abstract network models differs significantly from spread in realistic models. Second, we show that models investigating network congestion must include specific elements of realism before acceptable engineering findings can be established. Third, we identify the influence of specific elements of realism on congestion in network simulations. Finally, we demonstrate an effective means to compare congestion patterns among network simulations comprising diverse configurations. We hope our contributions lead to better understanding of the influence of realism on congestion in network simulations, and to improved dialog throughout the diverse community of researchers who rely on network simulations.

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