Lingering Issues in Distributed Scheduling

Recent advances have resulted in queue-based algorithms for medium access control which operate in a distributed fashion, and yet achieve the optimal throughput performance of centralized scheduling algorithms. However, fundamental performance bounds reveal that the "cautious" activation rules involved in establishing throughput optimality tend to produce extremely large delays, typically growing exponentially in 1/(1-r), with r the load of the system, in contrast to the usual linear growth. Motivated by that issue, we explore to what extent more "aggressive" schemes can improve the delay performance. Our main finding is that aggressive activation rules induce a lingering effect, where individual nodes retain possession of a shared resource for excessive lengths of time even while a majority of other nodes idle. Using central limit theorem type arguments, we prove that the idleness induced by the lingering effect may cause the delays to grow with 1/(1-r) at a quadratic rate. To the best of our knowledge, these are the first mathematical results illuminating the lingering effect and quantifying the performance impact. In addition extensive simulation experiments are conducted to illustrate and validate the various analytical results

Delay Performance and Mixing Times in Random-Access Networks

We explore the achievable delay performance in wireless random-access networks. While relatively simple and inherently distributed in nature, suitably designed queue-based random-access schemes provide the striking capability to match the optimal throughput performance of centralized scheduling mechanisms in a wide range of scenarios. The specific type of activation rules for which throughput optimality has been established, may however yield excessive queues and delays. Motivated by that issue, we examine whether the poor delay performance is inherent to the basic operation of these schemes, or caused by the specific kind of activation rules. We derive delay lower bounds for queue-based activation rules, which offer fundamental insight in the cause of the excessive delays. For fixed activation rates we obtain lower bounds indicating that delays and mixing times can grow dramatically with the load in certain topologies as well

A Multi-Scale Approach to Directional Field Estimation

This paper proposes a robust method for directional field estimation from fingerprint images that combines estimates at multiple scales. The method is able to provide accurate estimates in scratchy regions, while at the same time maintaining correct estimates around singular points. Compared to other methods, the penalty for detecting false singular points is much smaller, because this does not deteriorate the directional field estimate

Queues with random back-offs

We consider a broad class of queueing models with random state-dependent vacation periods, which arise in the analysis of queue-based back-off algorithms in wireless random-access networks. In contrast to conventional models, the vacation periods may be initiated after each service completion, and can be randomly terminated with certain probabilities that depend on the queue length. We examine the scaled queue length and delay in a heavy-traffic regime, and demonstrate a sharp trichotomy, depending on how the activation rate and vacation probability behave as function of the queue length. In particular, the effect of the vacation periods may either (i) completely vanish in heavy-traffic conditions, (ii) contribute an additional term to the queue lengths and delays of similar magnitude, or even (iii) give rise to an order-of-magnitude increase. The heavy-traffic asymptotics are obtained by combining stochastic lower and upper bounds with exact results for some specific cases. The heavy-traffic trichotomy provides valuable insight in the impact of the back-off algorithms on the delay performance in wireless random-access networks

New Protocols for Secure Linear Algebra: Pivoting-Free Elimination and Fast Block-Recursive Matrix Decomposition

Cramer and Damg\aa{}rd were the first to propose a constant-rounds protocol for securely solving a linear system of unknown rank over a finite field in multiparty computation (MPC). For $m$ linear equations and $n$ unknowns, and for the case $m\leq n$, the computational complexity of their protocol is $O(n^5)$. Follow-up work (by Cramer, Kiltz, and Padró) proposes another constant-rounds protocol for solving this problem, which has complexity $O(m^4+n^2 m)$. For certain applications, such asymptotic complexities might be prohibitive. In this work, we improve the asymptotic computational complexity of solving a linear system over a finite field, thereby sacrificing the constant-rounds property. We propose two protocols: (1) a protocol based on pivoting-free Gaussian elimination with computational complexity $O(n^3)$ and linear round complexity, and (2) a protocol based on block-recursive matrix decomposition, having $O(n^2)$ computational complexity (assuming ``cheap\u27\u27 secure inner products as in Shamir\u27s secret-sharing scheme) and $O(n^{1.585})$ (super-linear) round complexity