Fluid models of congestion collapse in overloaded switched networks
We consider a switched network (i.e. a queueing network in which there are constraints on which queues may be served simultaneously), in a state of overload. We analyse the behaviour of two scheduling algorithms for multihop switched networks: a generalized version of max-weight, and the α -fair pol...
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          | Published in | Queueing systems Vol. 69; no. 2; pp. 121 - 143 | 
|---|---|
| Main Authors | , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Boston
          Springer US
    
        01.10.2011
     Springer Nature B.V  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 0257-0130 1572-9443  | 
| DOI | 10.1007/s11134-011-9250-1 | 
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| Abstract | We consider a switched network (i.e. a queueing network in which there are constraints on which queues may be served simultaneously), in a state of overload. We analyse the behaviour of two scheduling algorithms for multihop switched networks: a generalized version of max-weight, and the
α
-fair policy. We show that queue sizes grow linearly with time, under either algorithm, and we characterize the growth rates. We use this characterization to demonstrate examples of congestion collapse, i.e. cases in which throughput drops as the switched network becomes more overloaded. We further show that the loss of throughput can be made arbitrarily small by the max-weight algorithm with weight function
f
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q
)=
q
α
as
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| AbstractList | We consider a switched network (i.e. a queueing network in which there are constraints on which queues may be served simultaneously), in a state of overload. We analyse the behaviour of two scheduling algorithms for multihop switched networks: a generalized version of max-weight, and the alpha -fair policy. We show that queue sizes grow linearly with time, under either algorithm, and we characterize the growth rates. We use this characterization to demonstrate examples of congestion collapse, i.e. cases in which throughput drops as the switched network becomes more overloaded. We further show that the loss of throughput can be made arbitrarily small by the max-weight algorithm with weight function f(q)=q super( ) alpha as alpha arrow right 0. We consider a switched network (i.e. a queueing network in which there are constraints on which queues may be served simultaneously), in a state of overload. We analyse the behaviour of two scheduling algorithms for multihop switched networks: a generalized version of max-weight, and the α -fair policy. We show that queue sizes grow linearly with time, under either algorithm, and we characterize the growth rates. We use this characterization to demonstrate examples of congestion collapse, i.e. cases in which throughput drops as the switched network becomes more overloaded. We further show that the loss of throughput can be made arbitrarily small by the max-weight algorithm with weight function f ( q )= q α as α →0. We consider a switched network (i.e. a queueing network in which there are constraints on which queues may be served simultaneously), in a state of overload. We analyse the behaviour of two scheduling algorithms for multihop switched networks: a generalized version of max-weight, and the α-fair policy. We show that queue sizes grow linearly with time, under either algorithm, and we characterize the growth rates. We use this characterization to demonstrate examples of congestion collapse, i.e. cases in which throughput drops as the switched network becomes more overloaded. We further show that the loss of throughput can be made arbitrarily small by the max-weight algorithm with weight function f(q)=q ^sup α^ as α[arrow right]0.[PUBLICATION ABSTRACT]  | 
    
| Author | Wischik, Damon Shah, Devavrat  | 
    
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| CitedBy_id | crossref_primary_10_1287_opre_2016_1565 crossref_primary_10_1287_15_SSY187 crossref_primary_10_1287_moor_2020_1106 crossref_primary_10_1109_TCNS_2014_2367361 crossref_primary_10_1007_s11134_015_9468_4 crossref_primary_10_1109_TNET_2014_2302445 crossref_primary_10_1007_s11134_011_9258_6 crossref_primary_10_1007_s11134_014_9421_y crossref_primary_10_1145_2318857_2254762 crossref_primary_10_1007_s11134_015_9447_9 crossref_primary_10_1109_TCOMM_2014_2359875 crossref_primary_10_1214_13_AAP970 crossref_primary_10_1155_2013_157636 crossref_primary_10_1145_2637364_2591987 crossref_primary_10_1287_ijoc_2015_0642 crossref_primary_10_1145_3366698  | 
    
| Cites_doi | 10.1214/105051604000000224 10.1016/j.peva.2007.06.024 10.1109/90.879343 10.1109/TIT.2006.874401 10.1109/9.182479 10.1023/A:1019138827659 10.1287/msom.1040.0052 10.1287/opre.1040.0170 10.1214/11-AAP759 10.1214/08-AAP522 10.1214/08-AAP541  | 
    
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| DOI | 10.1007/s11134-011-9250-1 | 
    
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| Keywords | 60K30 Switch Overload 90B36 Fluid model Max-weight 68M20 60K25 Bandwidth sharing  | 
    
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| SubjectTerms | Algorithms Bandwidths Business and Management Collapse Computational fluid dynamics Computer Communication Networks Congestion Control Internet Linear programming Mathematical analysis Networks Operations Research/Decision Theory Optimization Packet switched networks Probability Theory and Stochastic Processes Queues Scheduling Studies Supply Chain Management Switching theory Systems Theory Traffic congestion Weight function Wireless networks  | 
    
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