Modeling the Delivery of Coded Packets in D2D Mobile Caching Networks

Ahmed, Ameer; Shan, Hangguan; Huang, Aiping

doi:10.1109/access.2019.2894513

Cited by 10 publications

(13 citation statements)

References 53 publications

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“…networks [3], cellular and radio access networks [4], [5], vehicular and wireless sensor networks [6], [7], and general wireless networks [8]- [12], as well as unreliable complex information technology infrastructures, such as data caching infrastructures [13]- [15]. One main hurdle that prevents the widespread adoption of RLNC in communication networks and information technology systems is the computationally highly demanding matrix multiplication and matrix inversion required for RLNC decoding [16]- [19].…”

Section: Random Linear Network Coding (Rlnc) Has the Potential To Grementioning

confidence: 99%

“…We round the w(i) obtained from Eqn. (13) to the nearest integer and always set at least one coefficient, i.e., set w(i) at least to one.…”

Section: E Sparsity Level As a Function Of Number Of Expansion Packementioning

confidence: 99%

“…, n + r, we set µ = r and adjust w(i) according to Eqn. (13). When an additional expansion packet is first added in, then the corresponding coding coefficient λ is set to one.…”

Section: A Example Policy: Dynamic Sparsity With Expansion Packets Rmentioning

confidence: 99%

“…For a given value of the number of expansion packets µ, the number of non-zero coding coefficients w(i) is evaluated according to Eqn. (13).…”

Section: B Example Policy: Dynamic Sparsity With Expansion Packets Smentioning

confidence: 99%

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DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

et al. 2020

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Fulcrum coding combines a high-field outer Random Linear Network Coding (RLNC) that generates outer coding expansion packets with a small-field inner RLNC that combines the source packets and the outer coding expansion packets. This two-layer Fulcrum coding allows flexible decoding in receivers with heterogeneous computational capabilities. Fulcrum coding has so far only been studied for conventional dense RLNC, which randomly selects all coding coefficients, and only for a statically fixed number of outer expansion packets. However, the probability that the coding coefficient row of a newly received packet is linearly independent of prior received coding coefficient rows (a prerequisite for successful decoding) is highly dynamic. We propose to exploit the dynamics of this probability to reduce the computational complexity of Fulcrum coding. In particular, we vary the density of non-zero coding coefficients, i.e., equivalently, the sparsity of coding coefficients, and the number of outer expansion packets to keep the complexity low while maintaining a reasonably high decoding probability. We introduce the general principles of dynamic sparsity and expansion packets (DSEP) for Fulcrum coding as well as two specific example DSEP policies. Our evaluations indicate that DSEP Fulcrum can increase the encoding throughput tenfold and increase the decoding throughput 1.4 to 4.3 fold while achieving decoding probabilities that are typically less than 1% lower than the conventional Fulcrum decoding probabilities. We also find that DSEP achieves somewhat higher encoding and decoding throughputs than the CodornicesRq (Release 2.1) implementation of RaptorQ block coding for small blocks (generations) of source packets, while RaptorQ is substantially faster for large generation sizes. Furthermore, we develop and evaluate an elementary DSEP recoding mechanism that achieves a recoding throughput more than double the decoding throughput.

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Section: Random Linear Network Coding (Rlnc) Has the Potential To Grementioning

confidence: 99%

“…We round the w(i) obtained from Eqn. (13) to the nearest integer and always set at least one coefficient, i.e., set w(i) at least to one.…”

Section: E Sparsity Level As a Function Of Number Of Expansion Packementioning

confidence: 99%

“…, n + r, we set µ = r and adjust w(i) according to Eqn. (13). When an additional expansion packet is first added in, then the corresponding coding coefficient λ is set to one.…”

Section: A Example Policy: Dynamic Sparsity With Expansion Packets Rmentioning

confidence: 99%

“…For a given value of the number of expansion packets µ, the number of non-zero coding coefficients w(i) is evaluated according to Eqn. (13).…”

Section: B Example Policy: Dynamic Sparsity With Expansion Packets Smentioning

confidence: 99%

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DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

et al. 2020

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“…Random linear network coding (RLNC) can significantly enhance the communication over unreliable complex networks, such as body area networks [1], caching networks [2]- [4], cellular networks [5], the Internet of Things (IoT) [6]- [8], radio access networks [9], vehicular networks [10], wireless sensor networks [11], and general wireless networks [12]- [16]. One main challenge of RLNC based communication is that the decoding in receiver nodes involves computationally highly demanding matrix multiplication and matrix inversion [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Progressive Multicore RLNC Decoding With Online DAG Scheduling

2019

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A complete generation of packets coded with Random Linear Network Coding (RLNC) can be quickly decoded on a multicore system by scheduling the involved matrix block operations in parallel with an offline (pre-recorded) directed acyclic graph (DAG). The waiting for a complete generation of packets can be avoided with progressive RLNC decoding that commences the decoding (and can decode some packets) before all packets in a generation have been received. This article develops and evaluates a novel progressive RLNC decoding strategy based on the principle of DAG scheduling of parallel matrix block operations. The novel strategy involves helper matrices for conducting the Gauss Jordan elimination based on rows of blocks of matrix elements. The matrix block computations are dynamically scheduled by an online DAG which permits branching, e.g., to skip unnecessary matrix block operations. The throughput and delay of the novel progressive RLNC decoding strategy are evaluated with experiments on two heterogeneous multicore processor boards. The novel progressive RLNC decoding achieves throughput levels on par with stateof-the-art non-progressive (full-generation) RLNC decoding and achieves three times higher throughput than the fastest (highest-throughput) known progressive RLNC decoder for small generation sizes and short data packets. Also, our progressive RLNC decoding greatly reduces receiver delays for moderate to large generation sizes; the delay reductions are particularly pronounced when a low-delay RLNC version is employed (e.g., reduction to one tenth of the non-progressive decoding delay for a generation size of 256 packets). INDEX TERMS Directed acyclic graph (DAG), helper matrix, heterogeneous multicore architecture, online scheduling, parallel computing, random linear network coding (RLNC).

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Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions. INDEX TERMS Caching, computation offloading, device-to-device (D2D) communication, mobile edge computing (MEC). I. INTRODUCTION A. MOTIVATION

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Modeling the Delivery of Coded Packets in D2D Mobile Caching Networks

Cited by 10 publications

References 53 publications

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

Progressive Multicore RLNC Decoding With Online DAG Scheduling

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

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