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A fluid model for a relay node in an ad hoc network: the case of heavy-tailed input

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A fluid model for a relay node in an ad hoc network: the case of heavy-tailed input
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  Centrum voor Wiskunde en Informatica Probability, Networks and Algorithms Probability, Networks and Algorithms  A fluid model for a relay node in an ad-hoc network: the case of heavy-tailed inputR. Bekker, M.R.H. Mandjes R  EPORT  PNA-E0703 S EPTEMBER   2007  Centrum voor Wiskunde en Informatica (CWI) is the national research institute for Mathematics and Computer Science. It is sponsored by the Netherlands Organisation for Scientific Research (NWO). CWI is a founding member of ERCIM, the European Research Consortium for Informatics and Mathematics. CWI's research has a theme-oriented structure and is grouped into four clusters. Listed below are the names of the clusters and in parentheses their acronyms. Probability, Networks and Algorithms (PNA) Software Engineering (SEN) Modelling, Analysis and Simulation (MAS) Information Systems (INS) Copyright © 2007, Stichting Centrum voor Wiskunde en InformaticaP.O. Box 94079, 1090 GB Amsterdam (NL)Kruislaan 413, 1098 SJ Amsterdam (NL)Telephone +31 20 592 9333Telefax +31 20 592 4199 ISSN 1386-3711   A fluid model for a relay node in an ad-hoc network:the case of heavy-tailed input 2000 Mathematics Subject Classification:   60K25 Keywords and Phrases:    fluid queue - ad hoc network - regular variation Note  : Part of the researchwas conducted while the first author was affiliated to CWI. Hans van den Berg (TNO Telecom)is acknowledged for bringing this model under our attention. Abstract Relay nodes in an ad-hoc network can be modelled as fluid queues, in which theavailable service capacity is shared by the input and output. In this paper such arelay node is considered; jobs arrive according to a Poisson process and bring alonga random amount of work. The total transmission capacity is fairly shared, meaningthat, when  n  jobs arepresent, eachjob transmits trafficinto the queue atrate  1 / ( n +1) while the queue is drained at the same rate of   1 / ( n  + 1) . Where previous studiesmainly concentrated on the case of exponentially distributed job sizes, the presentpaper addresses regularly varying jobs. The focus lies on the tail asymptotics of thesojourn time  S  . Using sample-path arguments, it is proven that  P { S > x }  behavesroughly as the  residual  job size, i.e., if the job sizes are regularly varying of index − ν  , the tail of   S   is regularly varying of index  1 − ν  . In addition, we address the tailasymptotics of other performance metrics, such as the workload in the queue, theflow transfer time and the queueing delay.    A fluid model for a relay node in an ad-hoc network:the case of heavy-tailed input R. Bekker 1 and M. Mandjes 2 , 3 , 4  ∗ 1 Department of MathematicsVrije UniversiteitDe Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands 2 Korteweg-de Vries Institute for MathematicsPlantage Muidergracht 24, 1018 TV Amsterdam, the Netherlands 3 CWIAmsterdam, the Netherlands 4 E URANDOM Eindhoven, the Netherlands Abstract Relay nodes in an ad-hoc network can be modelled as fluid queues, in which theavailable service capacity is shared by the input and output. In this paper such arelay node is considered; jobs arrive according to a Poisson process and bring alonga random amount of work. The total transmission capacity is fairly shared, meaningthat, when  n  jobs arepresent, eachjob transmits trafficinto the queue atrate  1 / ( n +1) while the queue is drained at the same rate of   1 / ( n  + 1) . Where previous studiesmainly concentrated on the case of exponentially distributed job sizes, the presentpaper addresses regularly varying jobs. The focus lies on the tail asymptotics of thesojourn time  S  . Using sample-path arguments, it is proven that  P { S > x }  behavesroughly as the  residual  job size, i.e., if the job sizes are regularly varying of index − ν  , the tail of   S   is regularly varying of index  1 − ν  . In addition, we address the tailasymptotics of other performance metrics, such as the workload in the queue, theflow transfer time and the queueing delay. 1 Introduction Ad-hoc networks are self-configuring networks of mobile routers, connected by wirelesslinks. They enable infrastructure-free communication: no fixed equipment is needed, but instead each client acts as a hub. When information needs to be transmitted across ∗ Part ofthe researchwas conducted while the firstauthor was affiliatedto CWI. Hans van den Berg(TNOTelecom) is acknowledged for bringing this model under our attention. 1
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