Tree embedding based mapping system for low-latency mobile applications in multi-access networks

TL;DR

The paper tackles the challenge of low-latency mobility management for highly dynamic, multi-access mobile devices in emerging networks. It extends the Locator/ID separation concept with a tree-embedding LLP overlay and an end-host mobility module to enable fast session setup and direct mobility handling without router changes. Key contributions include the Hierarchical Cluster Selection (HCS) LLP tree construction, cluster-center heuristics, and shortcut-based tree augmentation that bound latency inflation while maintaining manageable forwarding state. Evaluations on real topology data show the LLP tree outperforms centralized and LISP-based approaches in CSR latency inflation, memory footprint, and update disruption, enabling efficient multi-access mobility with incremental deployment potential. The work offers practical implications for V2X, 6G, and latency-sensitive AR/VR/online gaming applications by delivering near-optimal paths and scalable location updates across diverse access networks.

Abstract

Low-latency applications like AR/VR and online gaming need fast, stable connections. New technologies such as V2X, LEO satellites, and 6G bring unique challenges in mobility management. Traditional solutions based on centralized or distributed anchors often fall short in supporting rapid mobility due to inefficient routing, low versatility, and insufficient multi-access support. In this paper, we design a new end-to-end system for tracking multi-connected mobile devices at scale and optimizing performance for latency-sensitive, highly dynamic applications. Our system, based on the locator/ID separation principle, extends to multi-access networks without requiring specialized routers or caching. Using a novel tree embedding-based overlay, we enable fast session setup while allowing endpoints to directly handle mobility between them. Evaluation with real network data shows our solution cuts connection latency to 7.42% inflation over the shortest path, compared to LISP's 359\% due to cache misses. It also significantly reduces location update overhead and disruption time during mobility.

Figures (11)

• Figure 1: CSR Forwarding
• Figure 2: Location Update Handling in LLP Tree
• Figure 3: Tree Building Example
• Figure 4: LLP Tree Shortcut Example
• Figure 5: Latency Inflation of CSR Processing

Theorems & Definitions (8)

• Definition 1
• Claim 1
• proof
• Claim 2
• proof
• Claim 3
• Claim 4
• proof