An Approximation Algorithm for Optimal Clique Cover Delivery in Coded Caching

Abstract

Coded caching can significantly reduce the communication bandwidth requirement for satisfying users' demands by utilizing the multicasting gain among multiple users. Most existing works assume that the users follow the prescriptions for content placement made by the system. However, users may prefer to decide what files to cache. To address this issue, we consider a network consisting of a file server connected through a shared link to K users, each equipped with a cache which has been already filled arbitrarily. Given an arbitrary content placement, the goal is to find a delivery strategy for the server that minimizes the load of the shared link. In this paper, we focus on a specific class of coded multicasting delivery schemes known as the "clique cover delivery scheme". We first formulate the optimal clique cover delivery problem as a combinatorial optimization problem. Using a connection with the weighted set cover problem, we propose an approximation algorithm and show that it provides an approximation ratio of (1 + log K), while the approximation ratio for the existing coded delivery schemes is linear in K. Numerical simulations show that our proposed algorithm provides a considerable bandwidth reduction over the existing coded delivery schemes for almost all content placement schemes.

Learning to Code: Coded Caching via Deep Reinforcement Learning

“…Hence, this algorithm achieves a normalized delivery delay of 1 [3]: Maddah-Ali and Niesen's delivery algorithm generalized to arbitrary cache placement realizations. • Size-aware coded multicast (SACM) [7]: The state-of-theart approximation algorithm that is guaranteed to achieve a normalized delivery delay, which is within a factor 1 + log K of the optimum. scenario with K = 4 users, each capable of caching M = 1 file.…”

Cryptography in Hierarchical Coded Caching: System Model and Cost Analysis

“…Coded caching is finding its application in modern technologies and services, such as content delivery [10][11][12], mobile computing [13][14][15], and information-enteric networks [16]. Different aspects of coded caching have recently been studied among which we can refer to as [17], centralized [18,19] and decentralized [18,19] coded caching, placement [20] and delivery [21,22] schemes, as well as added pre-fetching phase [23], multi-casting [22,[24][25][26], scheduling [27], error correction [28], clustering [29], heterogeneity [12,25,30], the impact of file size [31,32], dealing with non-uniform user demands [33] and peak-time traffic reduction [1]. Moreover, security in coded caching has been considered as a concern [20,[34][35][36] and cryptography has been among the best-studied security mechanisms for use in coded caching [37,38].…”

Cluster‐Based Cooperative Cache Deployment and Coded Delivery Strategy in C‐V2X Networks

“…Ramakrishnan et al in [28] proposed to divide a file into subfiles and code the subfiles and then transmit them to users, which can effectively reduce the traffic on the shared link. Asghari et al studied the optimal coded delivery scheme for arbitrary file deployment [14,15] and formulated the problem as a set cover problem, which however ignored the redundant data among subfiles in the packet. Such data packets may cause repeated transmission of the same subfiles and still waste a lot of network bandwidth.…”

“…To reduce the network load and computing complexity, Asghari et al [14,15] proposed an efficient method to avoid obtaining the set of feasible data packets, namely, data packets with smaller bits and more subfiles are selected from the subfile set as much as possible. However, for a multiobjective optimization problem, this method cannot be used directly.…”

“…Moreover, a smaller request delay and a higher hit ratio can be obtained by optimizing the cache deployment strategy, which can also reduce the network load of the backhaul link. To reduce the network bandwidth resources consumed by user requests, utilizing a coded delivery method based on subfile coding [14,15] can eliminate redundancy among multiple requested files and also reduce redundancy among subfiles. Due to ignoring the impact of cache deployment strategy on response delay and bandwidth [16], we combine the cache strategy and the transmission strategy to discuss their impacts on C-V2X networks in this article.…”