85 research outputs found
Communication-Aware Computing for Edge Processing
We consider a mobile edge computing problem, in which mobile users offload
their computation tasks to computing nodes (e.g., base stations) at the network
edge. The edge nodes compute the requested functions and communicate the
computed results to the users via wireless links. For this problem, we propose
a Universal Coded Edge Computing (UCEC) scheme for linear functions to
simultaneously minimize the load of computation at the edge nodes, and maximize
the physical-layer communication efficiency towards the mobile users. In the
proposed UCEC scheme, edge nodes create coded inputs of the users, from which
they compute coded output results. Then, the edge nodes utilize the computed
coded results to create communication messages that zero-force all the
interference signals over the air at each user. Specifically, the proposed
scheme is universal since the coded computations performed at the edge nodes
are oblivious of the channel states during the communication process from the
edge nodes to the users.Comment: To Appear in ISIT 201
Private Function Retrieval
The widespread use of cloud computing services raises the question of how one
can delegate the processing tasks to the untrusted distributed parties without
breeching the privacy of its data and algorithms. Motivated by the algorithm
privacy concerns in a distributed computing system, in this paper, we introduce
the private function retrieval (PFR) problem, where a user wishes to
efficiently retrieve a linear function of messages from
non-communicating replicated servers while keeping the function hidden from
each individual server. The goal is to find a scheme with minimum communication
cost. To characterize the fundamental limits of the communication cost, we
define the capacity of PFR problem as the size of the message that can be
privately retrieved (which is the size of one file) normalized to the required
downloaded information bits. We first show that for the PFR problem with
messages, servers and a linear function with binary coefficients the
capacity is . Interestingly, this
is the capacity of retrieving one of messages from servers while
keeping the index of the requested message hidden from each individual server,
the problem known as private information retrieval (PIR). Then, we extend the
proposed achievable scheme to the case of arbitrary number of servers and
coefficients in the field with arbitrary and obtain
Improving Distributed Gradient Descent Using Reed-Solomon Codes
Today's massively-sized datasets have made it necessary to often perform
computations on them in a distributed manner. In principle, a computational
task is divided into subtasks which are distributed over a cluster operated by
a taskmaster. One issue faced in practice is the delay incurred due to the
presence of slow machines, known as \emph{stragglers}. Several schemes,
including those based on replication, have been proposed in the literature to
mitigate the effects of stragglers and more recently, those inspired by coding
theory have begun to gain traction. In this work, we consider a distributed
gradient descent setting suitable for a wide class of machine learning
problems. We adapt the framework of Tandon et al. (arXiv:1612.03301) and
present a deterministic scheme that, for a prescribed per-machine computational
effort, recovers the gradient from the least number of machines
theoretically permissible, via an decoding algorithm. We also provide
a theoretical delay model which can be used to minimize the expected waiting
time per computation by optimally choosing the parameters of the scheme.
Finally, we supplement our theoretical findings with numerical results that
demonstrate the efficacy of the method and its advantages over competing
schemes
Latency Analysis of Coded Computation Schemes over Wireless Networks
Large-scale distributed computing systems face two major bottlenecks that
limit their scalability: straggler delay caused by the variability of
computation times at different worker nodes and communication bottlenecks
caused by shuffling data across many nodes in the network. Recently, it has
been shown that codes can provide significant gains in overcoming these
bottlenecks. In particular, optimal coding schemes for minimizing latency in
distributed computation of linear functions and mitigating the effect of
stragglers was proposed for a wired network, where the workers can
simultaneously transmit messages to a master node without interference. In this
paper, we focus on the problem of coded computation over a wireless
master-worker setup with straggling workers, where only one worker can transmit
the result of its local computation back to the master at a time. We consider 3
asymptotic regimes (determined by how the communication and computation times
are scaled with the number of workers) and precisely characterize the total
run-time of the distributed algorithm and optimum coding strategy in each
regime. In particular, for the regime of practical interest where the
computation and communication times of the distributed computing algorithm are
comparable, we show that the total run-time approaches a simple lower bound
that decouples computation and communication, and demonstrate that coded
schemes are times faster than uncoded schemes
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