arXiv subjects

Gregory Wornell

Publications and source records attributed to Gregory Wornell.

At least 37 records · Page 2Linked to original sources

arXiv2017

Analysis and Optimization of Aperture Design in Computational Imaging

There is growing interest in the use of coded aperture imaging systems for a variety of applications. Using an analysis framework based on mutual information, we examine the fundamental limits of such systems---and the associated optimum aperture coding---under simple but meaningful propagation and sensor models. Among other results, we show that when thermal noise dominates, spectrally-flat masks, which have 50% transmissivity, are optimal, but that when shot noise dominates, randomly generated masks with lower transmissivity offer greater performance. We also provide comparisons to classical pinhole cameras.

eess.IV↗

arXiv2017

Defect tolerance: fundamental limits and examples

This paper addresses the problem of adding redundancy to a collection of physical objects so that the overall system is more robust to failures. In contrast to its information counterpart, which can exploit parity to protect multiple information symbols from a single erasure, physical redundancy can only be realized through duplication and substitution of objects. We propose a bipartite graph model for designing defect-tolerant systems in which defective objects are replaced by judiciously connected redundant objects. The fundamental limits of this model are characterized under various asymptotic settings and both asymptotic and finite-size systems that approach these limits are constructed. Among other results, we show that simple modular redundancy is in general suboptimal. As we develop, this combinatorial problem of defect tolerant system design has a natural interpretation as one of graph coloring, and the analysis is significantly different from that traditionally used in information redundancy for error-control codes.

cs.IT↗

arXiv2017

Efficient Straggler Replication in Large-scale Parallel Computing

In a cloud computing job with many parallel tasks, the tasks on the slowest machines (straggling tasks) become the bottleneck in the job completion. Computing frameworks such as MapReduce and Spark tackle this by replicating the straggling tasks and waiting for any one copy to finish. Despite being adopted in practice, there is little analysis of how replication affects the latency and the cost of additional computing resources. In this paper we provide a framework to analyze this latency-cost trade-off and find the best replication strategy by answering design questions such as: 1) when to replicate straggling tasks, 2) how many replicas to launch, and 3) whether to kill the original copy or not. Our analysis reveals that for certain execution time distributions, a small amount of task replication can drastically reduce both latency as well as the cost of computing resources. We also propose an algorithm to estimate the latency and cost based on the empirical distribution of task execution time. Evaluations using samples in the Google Cluster Trace suggest further latency and cost reduction compared to the existing replication strategy used in MapReduce.

cs.DC↗

arXiv2017

Efficient Redundancy Techniques for Latency Reduction in Cloud Systems

In cloud computing systems, assigning a task to multiple servers and waiting for the earliest copy to finish is an effective method to combat the variability in response time of individual servers, and reduce latency. But adding redundancy may result in higher cost of computing resources, as well as an increase in queueing delay due to higher traffic load. This work helps understand when and how redundancy gives a cost-efficient reduction in latency. For a general task service time distribution, we compare different redundancy strategies in terms of the number of redundant tasks, and time when they are issued and canceled. We get the insight that the log-concavity of the task service time creates a dichotomy of when adding redundancy helps. If the service time distribution is log-convex (i.e. log of the tail probability is convex) then adding maximum redundancy reduces both latency and cost. And if it is log-concave (i.e. log of the tail probability is concave), then less redundancy, and early cancellation of redundant tasks is more effective. Using these insights, we design a general redundancy strategy that achieves a good latency-cost trade-off for an arbitrary service time distribution. This work also generalizes and extends some results in the analysis of fork-join queues.

cs.DC↗

arXiv2016

Fundamental Limits of Communication with Low Probability of Detection

This paper considers the problem of communication over a discrete memoryless channel (DMC) or an additive white Gaussian noise (AWGN) channel subject to the constraint that the probability that an adversary who observes the channel outputs can detect the communication is low. Specifically, the relative entropy between the output distributions when a codeword is transmitted and when no input is provided to the channel must be sufficiently small. For a DMC whose output distribution induced by the "off" input symbol is not a mixture of the output distributions induced by other input symbols, it is shown that the maximum amount of information that can be transmitted under this criterion scales like the square root of the blocklength. The same is true for the AWGN channel. Exact expressions for the scaling constant are also derived.

cs.IT↗

arXiv2015

On Throughput-Smoothness Trade-offs in Streaming Communication

Unlike traditional file transfer where only total delay matters, streaming applications impose delay constraints on each packet and require them to be in order. To achieve fast in-order packet decoding, we have to compromise on the throughput. We study this trade-off between throughput and smoothness in packet decoding. We first consider a point-to-point streaming and analyze how the trade-off is affected by the frequency of block-wise feedback, whereby the source receives full channel state feedback at periodic intervals. We show that frequent feedback can drastically improve the throughput-smoothness trade-off. Then we consider the problem of multicasting a packet stream to two users. For both point-to-point and multicast streaming, we propose a spectrum of coding schemes that span different throughput-smoothness tradeoffs. One can choose an appropriate coding scheme from these, depending upon the delay-sensitivity and bandwidth limitations of the application. This work introduces a novel style of analysis using renewal processes and Markov chains to analyze coding schemes.

cs.IT↗

arXiv2015

Compression in the Space of Permutations

We investigate lossy compression (source coding) of data in the form of permutations. This problem has direct applications in the storage of ordinal data or rankings, and in the analysis of sorting algorithms. We analyze the rate-distortion characteristic for the permutation space under the uniform distribution, and the minimum achievable rate of compression that allows a bounded distortion after recovery. Our analysis is with respect to different practical and useful distortion measures, including Kendall-tau distance, Spearman's footrule, Chebyshev distance and inversion-$\ell_1$ distance. We establish equivalence of source code designs under certain distortions and show simple explicit code designs that incur low encoding/decoding complexities and are asymptotically optimal. Finally, we show that for the Mallows model, a popular nonuniform ranking model on the permutation space, both the entropy and the maximum distortion at zero rate are much lower than the uniform counterparts, which motivates the future design of efficient compression schemes for this model.

cs.IT↗

arXiv2015

Efficient Replication of Queued Tasks for Latency Reduction in Cloud Systems

In cloud computing systems, assigning a job to multiple servers and waiting for the earliest copy to finish is an effective method to combat the variability in response time of individual servers. Although adding redundant replicas always reduces service time, the total computing time spent per job may be higher, thus increasing waiting time in queue. The total time spent per job is also proportional to the cost of computing resources. We analyze how different redundancy strategies, for eg. number of replicas, and the time when they are issued and canceled, affect the latency and computing cost. We get the insight that the log-concavity of the service time distribution is a key factor in determining whether adding redundancy reduces latency and cost. If the service distribution is log-convex, then adding maximum redundancy reduces both latency and cost. And if it is log-concave, then having fewer replicas and canceling the redundant requests early is more effective.

cs.DC↗

arXiv2014

Throughput-Smoothness Trade-offs in Multicasting of an Ordered Packet Stream

An increasing number of streaming applications need packets to be strictly in-order at the receiver. This paper provides a framework for analyzing in-order packet delivery in such applications. We consider the problem of multicasting an ordered stream of packets to two users over independent erasure channels with instantaneous feedback to the source. Depending upon the channel erasures, a packet which is in-order for one user, may be redundant for the other. Thus there is an inter-dependence between throughput and the smoothness of in-order packet delivery to the two users. We use a Markov chain model of packet decoding to analyze these throughput-smoothness trade-offs of the users, and propose coding schemes that can span different points on each trade-off.

cs.IT↗

arXiv2014

Efficient Task Replication for Fast Response Times in Parallel Computation

One typical use case of large-scale distributed computing in data centers is to decompose a computation job into many independent tasks and run them in parallel on different machines, sometimes known as the "embarrassingly parallel" computation. For this type of computation, one challenge is that the time to execute a task for each machine is inherently variable, and the overall response time is constrained by the execution time of the slowest machine. To address this issue, system designers introduce task replication, which sends the same task to multiple machines, and obtains result from the machine that finishes first. While task replication reduces response time, it usually increases resource usage. In this work, we propose a theoretical framework to analyze the trade-off between response time and resource usage. We show that, while in general, there is a tension between response time and resource usage, there exist scenarios where replicating tasks judiciously reduces completion time and resource usage simultaneously. Given the execution time distribution for machines, we investigate the conditions for a scheduling policy to achieve optimal performance trade-off, and propose efficient algorithms to search for optimal or near-optimal scheduling policies. Our analysis gives insights on when and why replication helps, which can be used to guide scheduler design in large-scale distributed computing systems.

cs.DC↗

arXiv2014

The Effect of Block-wise Feedback on the Throughput-Delay Trade-off in Streaming

Unlike traditional file transfer where only total delay matters, streaming applications impose delay constraints on each packet and require them to be in order. To achieve fast in-order packet decoding, we have to compromise on the throughput. We study this trade-off between throughput and in-order decoding delay, and in particular how it is affected by the frequency of block-wise feedback to the source. When there is immediate feedback, we can achieve the optimal throughput and delay simultaneously. But as the feedback delay increases, we have to compromise on at least one of these metrics. We present a spectrum of coding schemes that span different points on the throughput-delay trade-off. Depending upon the delay-sensitivity and bandwidth limitations of the application, one can choose an appropriate operating point on this trade-off.

cs.IT↗

arXiv2012

Asynchronous Communication: Capacity Bounds and Suboptimality of Training

Several aspects of the problem of asynchronous point-to-point communication without feedback are developed when the source is highly intermittent. In the system model of interest, the codeword is transmitted at a random time within a prescribed window whose length corresponds to the level of asynchronism between the transmitter and the receiver. The decoder operates sequentially and communication rate is defined as the ratio between the message size and the elapsed time between when transmission commences and when the decoder makes a decision. For such systems, general upper and lower bounds on capacity as a function of the level of asynchronism are established, and are shown to coincide in some nontrivial cases. From these bounds, several properties of this asynchronous capacity are derived. In addition, the performance of training-based schemes is investigated. It is shown that such schemes, which implement synchronization and information transmission on separate degrees of freedom in the encoding, cannot achieve the asynchronous capacity in general, and that the penalty is particularly significant in the high-rate regime.

cs.IT↗

arXiv2011

Caching in Wireless Networks

We consider the problem of delivering content cached in a wireless network of n nodes randomly located on a square of area n. The network performance is described by the n2^n-dimensional caching capacity region of the wireless network. We provide an inner bound on this caching capacity region, and, in the high path-loss regime, a matching (in the scaling sense) outer bound. For large path-loss exponent, this provides an information-theoretic scaling characterization of the entire caching capacity region. The proposed communication scheme achieving the inner bound shows that the problems of cache selection and channel coding can be solved separately without loss of order-optimality. On the other hand, our results show that the common architecture of nearest-neighbor cache selection can be arbitrarily bad, implying that cache selection and load balancing need to be performed jointly.

cs.IT↗

arXiv2010

Secret-key Agreement with Channel State Information at the Transmitter

We study the capacity of secret-key agreement over a wiretap channel with state parameters. The transmitter communicates to the legitimate receiver and the eavesdropper over a discrete memoryless wiretap channel with a memoryless state sequence. The transmitter and the legitimate receiver generate a shared secret key, that remains secret from the eavesdropper. No public discussion channel is available. The state sequence is known noncausally to the transmitter. We derive lower and upper bounds on the secret-key capacity. The lower bound involves constructing a common state reconstruction sequence at the legitimate terminals and binning the set of reconstruction sequences to obtain the secret-key. For the special case of Gaussian channels with additive interference (secret-keys from dirty paper channel) our bounds differ by 0.5 bit/symbol and coincide in the high signal-to-noise-ratio and high interference-to-noise-ratio regimes. For the case when the legitimate receiver is also revealed the state sequence, we establish that our lower bound achieves the the secret-key capacity. In addition, for this special case, we also propose another scheme that attains the capacity and requires only causal side information at the transmitter and the receiver.

cs.IT↗

arXiv2010

Secure Transmission with Multiple Antennas II: The MIMOME Wiretap Channel

The capacity of the Gaussian wiretap channel model is analyzed when there are multiple antennas at the sender, intended receiver and eavesdropper. The associated channel matrices are fixed and known to all the terminals. A computable characterization of the secrecy capacity is established as the saddle point solution to a minimax problem. The converse is based on a Sato-type argument used in other broadcast settings, and the coding theorem is based on Gaussian wiretap codebooks. At high signal-to-noise ratio (SNR), the secrecy capacity is shown to be attained by simultaneously diagonalizing the channel matrices via the generalized singular value decomposition, and independently coding across the resulting parallel channels. The associated capacity is expressed in terms of the corresponding generalized singular values. It is shown that a semi-blind "masked" multi-input multi-output (MIMO) transmission strategy that sends information along directions in which there is gain to the intended receiver, and synthetic noise along directions in which there is not, can be arbitrarily far from capacity in this regime. Necessary and sufficient conditions for the secrecy capacity to be zero are provided, which simplify in the limit of many antennas when the entries of the channel matrices are independent and identically distributed. The resulting scaling laws establish that to prevent secure communication, the eavesdropper needs 3 times as many antennas as the sender and intended receiver have jointly, and that the optimimum division of antennas between sender and intended receiver is in the ratio of 2:1.

cs.IT↗

arXiv2009

Quasi light fields: extending the light field to coherent radiation

Imaging technologies such as dynamic viewpoint generation are engineered for incoherent radiation using the traditional light field, and for coherent radiation using electromagnetic field theory. We present a model of coherent image formation that strikes a balance between the utility of the light field and the comprehensive predictive power of Maxwell's equations. We synthesize research in optics and signal processing to formulate, capture, and form images from quasi light fields, which extend the light field from incoherent to coherent radiation. Our coherent cameras generalize the classic beamforming algorithm in sensor array processing, and invite further research on alternative notions of image formation.

physics.optics↗

arXiv2009

Secret-Key Generation using Correlated Sources and Channels

We study the problem of generating a shared secret key between two terminals in a joint source-channel setup -- the sender communicates to the receiver over a discrete memoryless wiretap channel and additionally the terminals have access to correlated discrete memoryless source sequences. We establish lower and upper bounds on the secret-key capacity. These bounds coincide, establishing the capacity, when the underlying channel consists of independent, parallel and reversely degraded wiretap channels. In the lower bound, the equivocation terms of the source and channel components are functionally additive. The secret-key rate is maximized by optimally balancing the the source and channel contributions. This tradeoff is illustrated in detail for the Gaussian case where it is also shown that Gaussian codebooks achieve the capacity. When the eavesdropper also observes a source sequence, the secret-key capacity is established when the sources and channels of the eavesdropper are a degraded version of the legitimate receiver. Finally the case when the terminals also have access to a public discussion channel is studied. We propose generating separate keys from the source and channel components and establish the optimality of this approach when the when the channel outputs of the receiver and the eavesdropper are conditionally independent given the input.

cs.IT↗

arXiv2008

Adaptive Alternating Minimization Algorithms

The classical alternating minimization (or projection) algorithm has been successful in the context of solving optimization problems over two variables. The iterative nature and simplicity of the algorithm has led to its application to many areas such as signal processing, information theory, control, and finance. A general set of sufficient conditions for the convergence and correctness of the algorithm is quite well-known when the underlying problem parameters are fixed. In many practical situations, however, the underlying problem parameters are changing over time, and the use of an adaptive algorithm is more appropriate. In this paper, we study such an adaptive version of the alternating minimization algorithm. As a main result of this paper, we provide a general set of sufficient conditions for the convergence and correctness of the adaptive algorithm. Perhaps surprisingly, these conditions seem to be the minimal ones one would expect in such an adaptive setting. We present applications of our results to adaptive decomposition of mixtures, adaptive log-optimal portfolio selection, and adaptive filter design.

cs.IT↗