Size-Based Flow-Scheduling Using Spike-Detection

  • Dinil Mon Divakaran
  • Eitan Altman
  • Pascale Vicat-Blanc Primet
Conference paper

DOI: 10.1007/978-3-642-21713-5_24

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6751)
Cite this paper as:
Divakaran D.M., Altman E., Vicat-Blanc Primet P. (2011) Size-Based Flow-Scheduling Using Spike-Detection. In: Al-Begain K., Balsamo S., Fiems D., Marin A. (eds) Analytical and Stochastic Modeling Techniques and Applications. ASMTA 2011. Lecture Notes in Computer Science, vol 6751. Springer, Berlin, Heidelberg

Abstract

Size-based scheduling is a promising solution to improve the response time of small flows (mice) that have to share bandwidth with large flows (elephants). To do this, one important task is to track the size of the ongoing flows at the router. However, most of the proposed size-based schedulers either employ the trivial way of tracking the size information of all flows, or require changes at end-hosts. Hence, either they are not scalable or they increase complexity. This paper proposes a new way of performing size-based scheduling in a practical and scalable fashion, by identifying and ‘de-prioritizing’ elephants only at times of high load. We exploit TCP’s behaviour by using a mechanism that detects a window of packets — called spikes — when the buffer length exceeds a certain threshold. This spike-detection is used to identify elephant flows and thereafter de-prioritize them. Two-level processor-sharing (TLPS) scheduling is employed to schedule flows in two queues, one with the high-priority flows, and the other with the de-prioritized flows. We perform studies, using both analyses and simulations, to highlight the properties of the spike-detection mechanism. We show that, the proposed mechanism not only improves the response time of mice flows in a scalable way, but also gives better response times to other flows by treating them preferentially as long as they do not overload the high-priority queue.

Keywords

Queueing Scheduling Flows Networks Performance modelling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dinil Mon Divakaran
    • 1
  • Eitan Altman
    • 2
  • Pascale Vicat-Blanc Primet
    • 3
  1. 1.IIT MandiIndia
  2. 2.INRIAFrance
  3. 3.LYaTissFrance

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