Abstract

Coded multicasting has been shown to be a promising approach to significantly improve the performance of content delivery networks with multiple caches downstream of a common multicast link. However, the schemes that have been shown to achieve order-optimal performance require content items to be partitioned into several packets that grows exponentially with the number of caches, leading to codes of exponential complexity that jeopardize their promising performance benefits. In this paper, we address this crucial performance-complexity tradeoff in a heterogeneous caching network setting, where edge caches with possibly different storage capacity collect multiple content requests that may follow distinct demand distributions. We extend the asymptotic (in the number of packets per file) analysis of shared link caching networks to heterogeneous network settings, and present novel coded multicast schemes, based on local graph coloring, that exhibit polynomial-time complexity in all the system parameters, while preserving the asymptotically proven multiplicative caching gain even for finite file packetization. We further demonstrate that the packetization order (the number of packets each file is split into) can be traded-off with the number of requests collected by each cache, while preserving the same multiplicative caching gain. Simulation results confirm the superiority of the proposed schemes and illustrate the interesting request aggregation vs. packetization order tradeoff within several practical settings. Our results provide a compelling step towards the practical achievability of the promising multiplicative caching gain in next generation access networks.

Highlights

  • We address the important problem of finite-length coded multicasting under random cache placement, focusing on a more general heterogeneous shared link caching network, in which caches with possibly different sizes collect possibly multiple requests according to possibly different demand distributions

  • We show how Random Aggregate Popularity (RAP)-Hierarchical Greedy Local Coloring (HgLC), with a slight increase in the polynomial complexity order, further improves the caching gain of Randomized Aggregate Popularity-Greedy Local Coloring (RAP-GLC), remarkably approaching the multiplicative gain that existing schemes can only guarantee in the asymptotic file-length regime

  • In two aspects: (1) conventional coloring is replaced by local coloring to leverage possible gains in the multiple-request scenario, as described in Sections 3.2.1 and 3.3, and (2) RAP-GLC adaptively chooses between naive or coded multicasting according to a threshold parameter, instead of sticking to one of them

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Summary

Introduction

Recent information-theoretic studies [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49] have characterized the fundamental limiting performance of several caching networks of practical relevance, in which throughput scales linearly with cache size, showing great promise to accommodate the exponential traffic growth experienced in today’s communication networks [50]. In the case that users cannot communicate between each other, but share a multicast link from the content source, the authors in [8,9] showed that the use of coded multicasting ( referred to as index coding [51]) allows achieving the same order-optimal worst-case throughput as in the D2D caching network In this case, in order to create enough coding opportunities during the delivery phase, requested files are required to be partitioned into a number of packets that grows exponentially with the number of users, leading to coding schemes of exponential complexity [8,9,21].

Network Model and Problem Formulation
Random Fractional Cache Placement
Random Multiple Requests
Performance Metric
Graph-Coloring-Based Coded Multicast Delivery
Conflict Graph Construction
Code Construction
Graph Coloring and Chromatic Number
Local Graph Coloring and Local Chromatic Number
Benefits of Local Coloring
Proposed Algorithms and Performance Analysis
RAP-GLC Algorithm Description
RAP-GLC Performance Analysis
RAP-HgLC Algorithm Description
RAP-HgLC Performance Analysis
Tradeoff between Number of Requests and Code Length
Simulations and Discussions
Conclusions
Full Text
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