Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

AnyPro: Preference-Preserving Anycast Optimization based on Strategic AS-Path Prepending

Minyuan Zhou, Yuning Chen, Jiaqi Zheng, Yifei Xu, Pan Hu, Yongping Tang, Wendong Yin, Jie Lin, Qingyan Yu, Yuanchao Su, Guihai Chen, Wanchun Dou, Songwu Lu, Wan Du

Abstract

Operating large-scale anycast networks is challenging because client-to-site mappings often misalign with operator's expectation due to opaque inter-domain routing. We present AnyPro, the first system to unlock the full potential of AS-path prepending (ASPP), efficiently deriving globally optimal configurations to steer clients toward performance-optimal sites at scale. AnyPro first employs an efficient polling mechanism to identify all clients sensitive to ASPP. By analyzing the routing changes during the process, the system derives a set of ASPP constraints that guide client traffic toward the desired sites. We then formulate the anycast optimization problem as a constraint-based program and compute optimal ASPP configurations. Extensive evaluation on a global testbed with 20 PoPs demonstrates the effectiveness of AnyPro: it reduces the 90th percentile latency by 37.7% compared to baseline configurations without ASPP. Furthermore, we show that AnyPro can be integrated with PoP-level anycast optimization techniques to achieve additional performance gains.

AnyPro: Preference-Preserving Anycast Optimization based on Strategic AS-Path Prepending

Abstract

Operating large-scale anycast networks is challenging because client-to-site mappings often misalign with operator's expectation due to opaque inter-domain routing. We present AnyPro, the first system to unlock the full potential of AS-path prepending (ASPP), efficiently deriving globally optimal configurations to steer clients toward performance-optimal sites at scale. AnyPro first employs an efficient polling mechanism to identify all clients sensitive to ASPP. By analyzing the routing changes during the process, the system derives a set of ASPP constraints that guide client traffic toward the desired sites. We then formulate the anycast optimization problem as a constraint-based program and compute optimal ASPP configurations. Extensive evaluation on a global testbed with 20 PoPs demonstrates the effectiveness of AnyPro: it reduces the 90th percentile latency by 37.7% compared to baseline configurations without ASPP. Furthermore, we show that AnyPro can be integrated with PoP-level anycast optimization techniques to achieve additional performance gains.
Paper Structure (25 sections, 3 theorems, 4 equations, 12 figures, 2 tables, 2 algorithms)

This paper contains 25 sections, 3 theorems, 4 equations, 12 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. AnyPro
  4. Overview
  5. Proactive measurement system
  6. Practical challenges
  7. Max-min Polling
  8. Solving with constraints and contradiction resolution
  9. Deep Dive: Algorithm Design Rationale
  10. Evaluation
  11. Overall Performance (RQ1)
  12. Experiment Setup.
  13. Optimization Effectiveness (RQ2)
  14. Correlation between normalized objective and RTT
  15. Constraint identification
  16. ...and 10 more sections

Key Result

Lemma 1

For any two ingresses $p_{i,j}$ and $p_{m,n}$, max-min polling can effectively explore all potential routes for all clients by shrinking the length of the decision space from an interval $(s_{i,j},s_{m,n}\in [MIN, MAX])$ to two points $(s_{i,j},s_{m,n} \in \{MIN, MAX\})$, where $s_{i,j}$ and $s_{m,n

Figures (12)

  • Figure 1: AnyPro system overview.
  • Figure 2: Illustration of the real-world testbed we used for anycast measurements.
  • Figure 3: An illustrative example of max-min polling.
  • Figure 4: Workflow of the contradiction resolution.
  • Figure 5: Illustration of an ingress shift caused by the ASPP change of a third-party ingress .
  • ...and 7 more figures

Theorems & Definitions (7)

  • Lemma 1: Completeness of max-min polling for a pair of ingresses
  • proof
  • Theorem 2
  • proof
  • Theorem 3: Existence and uniqueness of preference-preserving constraints for a pair of ingresses
  • proof
  • proof