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On the Benefits of Traffic "Reprofiling'' -- The Single Hop Case

Jiayi Song, Jiaming Qiu, Roch Guerin, Henry Sariowan

TL;DR

This work addresses minimizing the bandwidth needed to guarantee hard end-to-end delays for multiple token bucket regulated flows on a single link. It analyzes schedulers of varying complexity from dynamic EDF to static priority and FIFO, and introduces reprofiling to reduce burstiness at the cost of an upfront delay. The key contributions include a complete characterization of the minimum bandwidth for EDF, static priority, and FIFO, plus constructive reprofiling strategies that allow simpler schedulers to closely approach EDF performance across two-flow and multi-flow scenarios, including application-derived traffic. The results demonstrate that reprofiling offers a practical mechanism to lower resource usage while preserving latency targets, and they establish a foundation for extending these ideas to multi-hop networks. Overall, the paper provides both analytical frameworks and numerical evidence that reprofiling can significantly improve bandwidth efficiency without sacrificing stringent delay guarantees in single-hop networks, informing scalable deterministic networking practice and future multi-hop extensions.

Abstract

The need to guarantee hard delay bounds to traffic flows with deterministic traffic profiles, e.g., token buckets, arises in a number of network settings. Of interest are solutions that offer such guarantees while minimizing network bandwidth. The paper explores a basic building block towards realizing such solutions, namely, a single hop configuration. The main results are in the form of optimal solutions for meeting local deadlines under schedulers of varying complexity and therefore cost. The results demonstrate how judiciously modifying flows' traffic profiles, i.e., reprofiling them, can help simple schedulers reduce the bandwidth they require, often performing nearly as well as more complex ones.

On the Benefits of Traffic "Reprofiling'' -- The Single Hop Case

TL;DR

This work addresses minimizing the bandwidth needed to guarantee hard end-to-end delays for multiple token bucket regulated flows on a single link. It analyzes schedulers of varying complexity from dynamic EDF to static priority and FIFO, and introduces reprofiling to reduce burstiness at the cost of an upfront delay. The key contributions include a complete characterization of the minimum bandwidth for EDF, static priority, and FIFO, plus constructive reprofiling strategies that allow simpler schedulers to closely approach EDF performance across two-flow and multi-flow scenarios, including application-derived traffic. The results demonstrate that reprofiling offers a practical mechanism to lower resource usage while preserving latency targets, and they establish a foundation for extending these ideas to multi-hop networks. Overall, the paper provides both analytical frameworks and numerical evidence that reprofiling can significantly improve bandwidth efficiency without sacrificing stringent delay guarantees in single-hop networks, informing scalable deterministic networking practice and future multi-hop extensions.

Abstract

The need to guarantee hard delay bounds to traffic flows with deterministic traffic profiles, e.g., token buckets, arises in a number of network settings. Of interest are solutions that offer such guarantees while minimizing network bandwidth. The paper explores a basic building block towards realizing such solutions, namely, a single hop configuration. The main results are in the form of optimal solutions for meeting local deadlines under schedulers of varying complexity and therefore cost. The results demonstrate how judiciously modifying flows' traffic profiles, i.e., reprofiling them, can help simple schedulers reduce the bandwidth they require, often performing nearly as well as more complex ones.

Paper Structure

This paper contains 42 sections, 24 theorems, 87 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Model Formulation
  3. Dynamic Priorities
  4. Static Priorities
  5. Static Priorities without Reprofiling
  6. Static Priorities with Reprofiling
  7. Basic FIFO with Reprofiling
  8. Evaluation
  9. Basic Two-Flow Configurations
  10. The Impact of Scheduler Complexity
  11. The Benefits of Reprofiling
  12. Relative Performance -- Multiple Flows
  13. Synthetic Flow Profiles
  14. Application Derived Flow Profiles
  15. Summary Discussion
  16. ...and 27 more sections

Key Result

Proposition 1

Consider a link shared by $n$ token bucket controlled flows, where flow $i, 1 \leq i \leq n$, has a traffic profile $(r_i, b_i)$ and a deadline $d_i$, with $d_1 > d_2 > ... > d_n$ and $d_1<\infty$. Consider a service-curve assignment $\Gamma_{sc}$ that allocates flow $i$ a service curve of Then

Figures (4)

  • Figure 1: A typical one-hop configuration with $n$ flows.
  • Figure 2: Relative bandwidth increases for $(r_1, b_1) = (4,10)$ and $(r_2, b_2) = (10,18)$, as a function of $d_1$ and $d_2<d_1$. The figure is in the form of a heat-map. Darker colors (purple) correspond to smaller increases than lighter ones (yellow).
  • Figure 3: Relative bandwidth increases for $(r_1, b_1) = (4,10)$ and $(r_2, b_2) = (10,18)$ as a function of $d_1$ and $d_2<d_1$. The figure is in the form of a heat-map. Darker colors (purple) correspond to smaller increases than lighter ones (yellow).
  • Figure 4: CDF of flow rates for Web, Cache, & Hadoop applications from roy15 assuming correlated flow durations and sizes.

Theorems & Definitions (41)

  • Proposition 1
  • Proposition 2
  • Proposition 3
  • Proposition 4
  • Proposition 5
  • Corollary 6
  • Proposition 7
  • Proposition 8
  • Proposition 9
  • Proposition 10
  • ...and 31 more