Rapid Convergecast on Commodity Hardware: Performance Limits and Optimal Policies

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ETEA: A euclidean minimum spanning tree-Based evolutionary algorithm for multiobjective optimization

© the Massachusetts Institute of TechnologyAbstract The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in the space, where the edge weight between each pair of points is their Euclidean distance. Since the generation of an EMST is entirely determined by the Euclidean distance between solutions (points), the properties of EMSTs have a close relation with the distribution and position information of solutions. This paper explores the properties of EMSTs and proposes an EMST-based Evolutionary Algorithm (ETEA) to solve multiobjective optimization problems (MOPs). Unlike most EMO algorithms that focus on the Pareto dominance relation, the proposed algorithm mainly considers distance-based measures to evaluate and compare individuals during the evolutionary search. Specifically in ETEA, four strategies are introduced: 1) An EMST-based crowding distance (ETCD) is presented to estimate the density of individuals in the population; 2) A distance comparison approach incorporating ETCD is used to assign the fitness value for individuals; 3) A fitness adjustment technique is designed to avoid the partial overcrowding in environmental selection; 4) Three diversity indicators-the minimum edge, degree, and ETCD-with regard to EMSTs are applied to determine the survival of individuals in archive truncation. From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/K001310/1, and the National Natural Science Foundation of China under Grant 61070088

Greedy Randomized Adaptive Search and Variable Neighbourhood Search for the minimum labelling spanning tree problem

This paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-hard. A Greedy Randomized Adaptive Search Procedure (GRASP) and a Variable Neighbourhood Search (VNS) are proposed in this paper. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics

Unsupervised Learning of Complex Articulated Kinematic Structures combining Motion and Skeleton Information

In this paper we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view image sequence. In contrast to prior motion information based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology by a successive iterative merge process. The iterative merge process is guided by a skeleton distance function which is generated from a novel object boundary generation method from sparse points. Our main contributions can be summarised as follows: (i) Unsupervised complex articulated kinematic structure learning by combining motion and skeleton information. (ii) Iterative fine-to-coarse merging strategy for adaptive motion segmentation and structure smoothing. (iii) Skeleton estimation from sparse feature points. (iv) A new highly articulated object dataset containing multi-stage complexity with ground truth. Our experiments show that the proposed method out-performs state-of-the-art methods both quantitatively and qualitatively

Leader election in synchronous networks

Worst, best and average number of messages and running time of leader election algorithms of different distributed systems are analyzed. Among others the known characterizations of the expected number of messages for LCR algorithm and of the worst number of messages of Hirschberg-Sinclair algorithm are improve

Flexible N-Way MIMO Detector on GPU

This paper proposes a flexible Multiple-Input Multiple-Output (MIMO) detector on graphics processing units (GPU). MIMO detection is a key technology in broadband wireless system such as LTE,WiMAX, and 802.11n. Existing detectors either use costly sorting for better performance or sacrifice sorting for higher throughput. To achieve good performance with high thoughput, our detector runs multiple search passes in parallel, where each search pass detects the transmit stream with a different permuted detection order. We show that this flexible detector, including QR decomposition preprocessing, outperforms existing GPU MIMO detectors while maintaining good bit error rate (BER) performance. In addition, this detector can achieve different tradeoffs between throughput and accuracy by changing the number of parallel search passes.National Science Foundation (NSF

Designs and Analyses in Structured Peer-To-Peer Systems

Peer-to-Peer (P2P) computing is a recent hot topic in the areas of networking and distributed systems. Work on P2P computing was triggered by a number of ad-hoc systems that made the concept popular. Later, academic research efforts started to investigate P2P computing issues based on scientific principles. Some of that research produced a number of structured P2P systems that were collectively referred to by the term "Distributed Hash Tables" (DHTs). However, the research occurred in a diversified way leading to the appearance of similar concepts yet lacking a common perspective and not heavily analyzed. In this thesis we present a number of papers representing our research results in the area of structured P2P systems grouped as two sets labeled respectively "Designs" and "Analyses". The contribution of the first set of papers is as follows. First, we present the princi- ple of distributed k-ary search and argue that it serves as a framework for most of the recent P2P systems known as DHTs. That is, given this framework, understanding existing DHT systems is done simply by seeing how they are instances of that frame- work. We argue that by perceiving systems as instances of that framework, one can optimize some of them. We illustrate that by applying the framework to the Chord system, one of the most established DHT systems. Second, we show how the frame- work helps in the design of P2P algorithms by two examples: (a) The DKS(n; k; f) system which is a system designed from the beginning on the principles of distributed k-ary search. (b) Two broadcast algorithms that take advantage of the distributed k-ary search tree. The contribution of the second set of papers is as follows. We account for two approaches that we used to evaluate the performance of a particular class of DHTs, namely the one adopting periodic stabilization for topology maintenance. The first approach was of an intrinsic empirical nature. In this approach, we tried to perceive a DHT as a physical system and account for its properties in a size-independent manner. The second approach was of a more analytical nature. In this approach, we applied the technique of Master Equations, which is a widely used technique in the analysis of natural systems. The application of the technique lead to a highly accurate description of the behavior of structured overlays. Additionally, the thesis contains a primer on structured P2P systems that tries to capture the main ideas prevailing in the field