7 research outputs found
Coded Computing for Half-Duplex Wireless Distributed Computing Systems via Interference Alignment
Distributed computing frameworks such as MapReduce and Spark are often used
to process large-scale data computing jobs. In wireless scenarios, exchanging
data among distributed nodes would seriously suffer from the communication
bottleneck due to limited communication resources such as bandwidth and power.
To address this problem, we propose a coded parallel computing (CPC) scheme for
distributed computing systems where distributed nodes exchange information over
a half-duplex wireless interference network. The CPC scheme achieves the
multicast gain by utilizing coded computing to multicast coded symbols
{intended to} multiple receiver nodes and the cooperative transmission gain by
allowing multiple {transmitter} nodes to jointly deliver messages via
interference alignment. To measure communication performance, we apply the
widely used latency-oriented metric: \emph{normalized delivery time (NDT)}. It
is shown that CPC can significantly reduce the NDT by jointly exploiting the
parallel transmission and coded multicasting opportunities. Surprisingly, when
tends to infinity and the computation load is fixed, CPC approaches zero
NDT while all state-of-the-art schemes achieve positive values of NDT. Finally,
we establish an information-theoretic lower bound for the NDT-computation load
trade-off over \emph{half-duplex} network, and prove our scheme achieves the
minimum NDT within a multiplicative gap of , i.e., our scheme is order
optimal.Comment: 17 pages, 6 figure
A Fundamental Storage-Communication Tradeoff in Distributed Computing with Straggling Nodes
The optimal storage-computation tradeoff is characterized for a MapReduce-like distributed computing system with straggling nodes, where only a part of the nodes can be utilized to compute the desired output functions. The result holds for arbitrary output functions and thus generalizes previous results that restricted to linear functions. Specifically, in this work, we propose a new information-theoretical converse and a new matching coded computing scheme, that we call coded computing for straggling systems (CCS)
A Fundamental Storage-Communication Tradeoff in Distributed Computing with Straggling Nodes
International audienc
On the Design of Future Communication Systems with Coded Transport, Storage, and Computing
Communication systems are experiencing a fundamental change. There are novel applications that require an increased performance not only of throughput but also latency, reliability, security, and heterogeneity support from these systems. To fulfil the requirements, future systems understand communication not only as the transport of bits but also as their storage, processing, and relation. In these systems, every network node has transport storage and computing resources that the network operator and its users can exploit through virtualisation and softwarisation of the resources. It is within this context that this work presents its results. We proposed distributed coded approaches to improve communication systems. Our results improve the reliability and latency performance of the transport of information. They also increase the reliability, flexibility, and throughput of storage applications. Furthermore, based on the lessons that coded approaches improve the transport and storage performance of communication systems, we propose a distributed coded approach for the computing of novel in-network applications such as the steering and control of cyber-physical systems. Our proposed approach can increase the reliability and latency performance of distributed in-network computing in the presence of errors, erasures, and attackers