7,210 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
Communication Through Collisions: Opportunistic Utilization of Past Receptions
When several wireless users are sharing the spectrum, packet collision is a
simple, yet widely used model for interference. Under this model, when
transmitters cause interference at any of the receivers, their collided packets
are discarded and need to be retransmitted. However, in reality, that receiver
can still store its analog received signal and utilize it for decoding the
packets in the future (for example, by successive interference cancellation
techniques). In this work, we propose a physical layer model for wireless
packet networks that allows for such flexibility at the receivers. We assume
that the transmitters will be aware of the state of the channel (i.e. when and
where collisions occur, or an unintended receiver overhears the signal) with
some delay, and propose several coding opportunities that can be utilized by
the transmitters to exploit the available signal at the receivers for
interference management (as opposed to discarding them). We analyze the
achievable throughput of our strategy in a canonical interference channel with
two transmitter-receiver pairs, and demonstrate the gain over conventional
schemes. By deriving an outer-bound, we also prove the optimality of our scheme
for the corresponding model.Comment: Accepted to IEEE INFOCOM 2014. arXiv admin note: text overlap with
arXiv:1301.530
How to Optimally Allocate Resources for Coded Distributed Computing?
Today's data centers have an abundance of computing resources, hosting server
clusters consisting of as many as tens or hundreds of thousands of machines. To
execute a complex computing task over a data center, it is natural to
distribute computations across many nodes to take advantage of parallel
processing. However, as we allocate more and more computing resources to a
computation task and further distribute the computations, large amounts of
(partially) computed data must be moved between consecutive stages of
computation tasks among the nodes, hence the communication load can become the
bottleneck. In this paper, we study the optimal allocation of computing
resources in distributed computing, in order to minimize the total execution
time in distributed computing accounting for both the duration of computation
and communication phases. In particular, we consider a general MapReduce-type
distributed computing framework, in which the computation is decomposed into
three stages: \emph{Map}, \emph{Shuffle}, and \emph{Reduce}. We focus on a
recently proposed \emph{Coded Distributed Computing} approach for MapReduce and
study the optimal allocation of computing resources in this framework. For all
values of problem parameters, we characterize the optimal number of servers
that should be used for distributed processing, provide the optimal placements
of the Map and Reduce tasks, and propose an optimal coded data shuffling
scheme, in order to minimize the total execution time. To prove the optimality
of the proposed scheme, we first derive a matching information-theoretic
converse on the execution time, then we prove that among all possible resource
allocation schemes that achieve the minimum execution time, our proposed scheme
uses the exactly minimum possible number of servers
Fairness in Multiuser Systems with Polymatroid Capacity Region
For a wide class of multi-user systems, a subset of capacity region which
includes the corner points and the sum-capacity facet has a special structure
known as polymatroid. Multiaccess channels with fixed input distributions and
multiple-antenna broadcast channels are examples of such systems. Any interior
point of the sum-capacity facet can be achieved by time-sharing among corner
points or by an alternative method known as rate-splitting. The main purpose of
this paper is to find a point on the sum-capacity facet which satisfies a
notion of fairness among active users. This problem is addressed in two cases:
(i) where the complexity of achieving interior points is not feasible, and (ii)
where the complexity of achieving interior points is feasible. For the first
case, the corner point for which the minimum rate of the active users is
maximized (max-min corner point) is desired for signaling. A simple greedy
algorithm is introduced to find the optimum max-min corner point. For the
second case, the polymatroid properties are exploited to locate a rate-vector
on the sum-capacity facet which is optimally fair in the sense that the minimum
rate among all users is maximized (max-min rate). In the case that the rate of
some users can not increase further (attain the max-min value), the algorithm
recursively maximizes the minimum rate among the rest of the users. It is shown
that the problems of deriving the time-sharing coefficients or rate-spitting
scheme can be solved by decomposing the problem to some lower-dimensional
subproblems. In addition, a fast algorithm to compute the time-sharing
coefficients to attain a general point on the sum-capacity facet is proposed.Comment: Submitted To IEEE Transactions on Information Theory, June 200
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