Generalized Processor Sharing with light-tailed and heavy-tailer input

Sem Borst, Michel Mandjes, Miranda van Uitert

Research output: Contribution to journalArticleAcademicpeer-review

53 Citations (Scopus)
35 Downloads (Pure)


We consider a queue fed by a mixture of light-tailed and heavy-tailed traffic. The two traffic flows are served in accordance with the generalized processor sharing (GPS) discipline. GPS-based scheduling algorithms, such as weighted fair queueing, have emerged as an important mechanism for achieving service differentiation in integrated networks. We derive the asymptotic workload behavior of the light-tailed traffic flow under the assumption that its GPS weight is larger than its traffic intensity. The GPS mechanism ensures that the workload is bounded above by that in an isolated system with the light-tailed flow served in isolation at a constant rate equal to its GPS weight. We show that the workload distribution is in fact asymptotically equivalent to that in the isolated system, multiplied with a certain pre-factor, which accounts for the interaction with the heavy-tailed flow. Specifically, the pre-factor represents the probability that the heavy-tailed flow is backlogged long enough for the light-tailed flow to reach overflow. The results provide crucial qualitative insight in the typical overflow scenario.
Original languageEnglish
Pages (from-to)821-834
Number of pages14
JournalIEEE/ACM transactions on networking
Issue number5
Publication statusPublished - 2003


  • Light-tailed traffic
  • Workload asymptotics
  • Regular variation
  • Weighted fair queueing
  • Heavy-tailed traffic
  • EWI-17784
  • IR-70871
  • Markov fluid
  • Generalized Processor Sharing (GPS)
  • Large deviations


Dive into the research topics of 'Generalized Processor Sharing with light-tailed and heavy-tailer input'. Together they form a unique fingerprint.

Cite this