13,949 research outputs found
When Do Redundant Requests Reduce Latency ?
Several systems possess the flexibility to serve requests in more than one
way. For instance, a distributed storage system storing multiple replicas of
the data can serve a request from any of the multiple servers that store the
requested data, or a computational task may be performed in a compute-cluster
by any one of multiple processors. In such systems, the latency of serving the
requests may potentially be reduced by sending "redundant requests": a request
may be sent to more servers than needed, and it is deemed served when the
requisite number of servers complete service. Such a mechanism trades off the
possibility of faster execution of at least one copy of the request with the
increase in the delay due to an increased load on the system. Due to this
tradeoff, it is unclear when redundant requests may actually help. Several
recent works empirically evaluate the latency performance of redundant requests
in diverse settings.
This work aims at an analytical study of the latency performance of redundant
requests, with the primary goals of characterizing under what scenarios sending
redundant requests will help (and under what scenarios they will not help), as
well as designing optimal redundant-requesting policies. We first present a
model that captures the key features of such systems. We show that when service
times are i.i.d. memoryless or "heavier", and when the additional copies of
already-completed jobs can be removed instantly, redundant requests reduce the
average latency. On the other hand, when service times are "lighter" or when
service times are memoryless and removal of jobs is not instantaneous, then not
having any redundancy in the requests is optimal under high loads. Our results
hold for arbitrary arrival processes.Comment: Extended version of paper presented at Allerton Conference 201
Critical Behavior in Lossy Source Coding
The following critical phenomenon was recently discovered. When a memoryless
source is compressed using a variable-length fixed-distortion code, the fastest
convergence rate of the (pointwise) compression ratio to the optimal
bits/symbol is either or . We show it is always
, except for discrete, uniformly distributed sources.Comment: 2 figure
Characterization of a dense aperture array for radio astronomy
EMBRACE@Nancay is a prototype instrument consisting of an array of 4608
densely packed antenna elements creating a fully sampled, unblocked aperture.
This technology is proposed for the Square Kilometre Array and has the
potential of providing an extremely large field of view making it the ideal
survey instrument. We describe the system,calibration procedures, and results
from the prototype.Comment: 17 pages, accepted for publication in A&
Abstract Canonical Inference
An abstract framework of canonical inference is used to explore how different
proof orderings induce different variants of saturation and completeness.
Notions like completion, paramodulation, saturation, redundancy elimination,
and rewrite-system reduction are connected to proof orderings. Fairness of
deductive mechanisms is defined in terms of proof orderings, distinguishing
between (ordinary) "fairness," which yields completeness, and "uniform
fairness," which yields saturation.Comment: 28 pages, no figures, to appear in ACM Trans. on Computational Logi
On the rate loss and construction of source codes for broadcast channels
In this paper, we first define and bound the rate loss of source codes for broadcast channels. Our broadcast channel model comprises one transmitter and two receivers; the transmitter is connected to each receiver by a private channel and to both receivers by a common channel. The transmitter sends a description of source (X, Y) through these channels, receiver 1 reconstructs X with distortion D1, and receiver 2 reconstructs Y with distortion D2. Suppose the rates of the common channel and private channels 1 and 2 are R0, R1, and R2, respectively. The work of Gray and Wyner gives a complete characterization of all achievable rate triples (R0,R1,R2) given any distortion pair (D1,D2). In this paper, we define the rate loss as the gap between the achievable region and the outer bound composed by the rate-distortion functions, i.e., R0+R1+R2 â„ RX,Y (D1,D2), R0 + R1 â„ RX(D1), and R0 + R2 â„ RY (D2). We upper bound the rate loss for general sources by functions of distortions and upper bound the rate loss for Gaussian sources by constants, which implies that though the outer bound is generally not achievable, it may be quite close to the achievable region. This also bounds the gap between the achievable region and the inner bound proposed by Gray and Wyner and bounds the performance penalty associated with using separate decoders rather than joint decoders. We then construct such source codes using entropy-constrained dithered quantizers. The resulting implementation has low complexity and performance close to the theoretical optimum. In particular, the gap between its performance and the theoretical optimum can be bounded from above by constants for Gaussian sources
- âŠ