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Generation of Communication Schedules for Multi-Mode Distributed Real-Time Applications

Generation of Communication Schedules for Multi-Mode Distrib...

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Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Azim, Akramul Carvajal, Gonzalo Pellizzoni, Rodolfo Fischmeister, Sebastian Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9783981537024

A key problem in designing multi-mode real-time systems is the generation of schedules to reduce the complexities of transforming the model semantics to code. Moreover, distributed multi-mode applications are prone to suffer from delays incurred during mode changes. We therefore aim to generate communication schedules that have low average mode-change delay for multi-mode real-time distributed applications. In this paper, we use optimization constraints associated to timing requirements to generate state-based schedules for multi-mode communication systems, and illustrate the workflow for generating schedules from specifications through a real-time video monitoring case-study. Our experiments in the case-study demonstrate that schedules generated using the proposed method reduce the average mode-change delay in relation to a randomized algorithm and the well-known EDF scheduling algorithm.

关键词： bandwidth allocation delays processor scheduling randomised algorithms real-time systems video communication EDF scheduling algorithm average mode-change delay communication schedule generation earliest-deadline-first mode-change delay model semantics multimode communication systems multimode distributed real-time applications optimization constraints randomized algorithm real-time video monitoring state-based schedules Bandwidth Delays Real-time systems Schedules Streaming media Time division multiple access

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COMBINING BIASED RANDOM SAMPLING WITH METAHEURISTICS FOR THE FACILITY LOCATION PROBLEM IN DISTRIBUTED COMPUTER SYSTEMS

COMBINING BIASED RANDOM SAMPLING WITH METAHEURISTICS FOR THE...

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Winter Simulation Conference

作者： Cabrera, Guillem Gonzalez-Martin, Sergio Juan, Angel A. Marques, Joan M. Grasman, Scott E. Univ Oberta Catalunya IN3 Dept Comp Sci Barcelona 08018 Spain Rochester Inst Technol Ind & Syst Engn Dept Rochester NY 14623 USA

ISBN: (纸本)9781479974863

yThis paper introduces a probabilistic algorithm for solving the well-known Facility Location Problem (FLP), an optimization problem frequently encountered in practical applications in fields such as Logistics or Telecommunications. Our algorithm is based on the combination of biased random sampling -using a skewed probability distribution-with a metaheuristic framework. The use of random variates from a skewed distribution allows to guide the local search process inside the metaheuristic framework which, being a stochastic procedure, is likely to produce slightly different results each time it is run. Our approach is validated against some classical benchmarks from the FLP literature and it is also used to analyze the deployment of service replicas in a realistic Internet-distributed system.

关键词： Internet facility location randomised algorithms sampling methods statistical distributions stochastic processes FLP Internet-distributed system biased random sampling distributed computer systems facility location problem local search process metaheuristic framework optimization problem randomized algorithm service replica deployment skewed probability distribution stochastic procedure Abstracts Benchmark testing Computer architecture

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Advanced optimization methods for power systems

Advanced optimization methods for power systems

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2014 Power Systems Computation Conference (PSCC)

作者： Panciatici, P. Campi, M. C. Garatti, S. Low, S. H. Molzahn, D. K. Sun, A. X. Wehenkel, L. RTE R&D Dept Versailles France Univ Brescia Dept Informat Engn Brescia Italy Politecn Milan Dept Elect Comp Sci & Bioengn Milan Italy CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA Univ Michigan Dept EE&CS Ann Arbor MI 48109 USA Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Univ Liege Dept EE&CS Inst Montefiore B-4000 Liege Belgium

ISBN: (纸本)9788393580132

Power system planning and operation offers multitudinous opportunities for optimization methods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine both continuous and discrete variables. In the recent years, a number of complementary theoretical advances in addressing such problems have been obtained in the field of applied mathematics. The paper introduces a selection of these advances in the fields of non-convex optimization, in mixed-integer programming, and in optimization under uncertainty. The practical relevance of these developments for power systems planning and operation are discussed, and the opportunities for combining them, together with high-performance computing and big data infrastructures, as well as novel machine learning and randomized algorithms, are highlighted.

关键词： Big Data concave programming integer programming learning (artificial intelligence) nonlinear programming parallel processing power engineering computing power system planning randomised algorithms Big Data infrastructure advanced optimization method high-performance computing machine learning mixed integer programming nonconvex optimization power system operation randomized algorithm Electronic mail Europe Linear programming Optimization Planning Power systems Uncertainty Power system planning randomized algorithms Integer Programming power systems High Performance Computing big data Power engineering computing electronic mail Nonlinear Programming PARALLEL PROCESSING (COMPUTERS) Optimization methods linear programming Uncertainty

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Balls into bins via local search: cover time and maximum load 31

Balls into bins via local search: cover time and maximum loa...

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31st International Symposium on Theoretical Aspects of Computer Science (STACS)

作者： Bringmann, Karl Sauerwald, Thomas Stauffer, Alexandre Sun, He Max Planck Inst Informat Saarbrucken Germany Univ Cambridge Cambridge England Univ Bath Bath Avon England

ISBN: (纸本)9783939897651

We study a natural process for allocating in balls into n bins that are organized as the vertices of an undirected graph G. Balls arrive one at a time. When a ball arrives, it first chooses a vertex u in G uniformly at random. Then the ball performs a local search in G starting from u until it reaches a vertex with local minimum load, where the ball is finally placed on. Then the next ball arrives and this procedure is repeated. For the case m = n, we give an upper bound for the maximum load on graphs with bounded degrees. We also propose the study of the cover time of this process, which is defined as the smallest in so that every bin has at least one ball allocated to it. We establish an upper bound for the cover time on graphs with bounded degrees. Our bounds for the maximum load and the cover time are tight when the graph is vertex transitive or sufficiently homogeneous. We also give upper bounds for the maximum load when m >= n.

关键词： Balls and Bins Stochastic Process randomized algorithm

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Quick Detection of High-degree Entities in Large Directed Networks 14

Quick Detection of High-degree Entities in Large Directed Ne...

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14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： Avrachenkov, K. Litvak, N. Prokhorenkova, L. Ostroumova Suyargulova, E. Inria Rocquencourt France Univ Twente NL-7500 AE Enschede Netherlands Yandex Moscow Russia

ISBN: (纸本)9781479943036

In this paper we address the problem of quick detection of high-degree entities in large online social networks. Practical importance of this problem is attested by a large number of companies that continuously collect and update statistics about popular entities, usually using the degree of an entity as an approximation of its popularity. We suggest a simple, efficient, and easy to implement two-stage randomized algorithm that provides highly accurate solutions to this problem. For instance, our algorithm needs only one thousand API requests in order to find the top-00 most followed users, with more than 90% precision, in the online social network Twitter with approximately a billion of registered users. Our algorithm significantly outperforms existing methods and serves many different purposes such as finding the most popular users or the most popular interest groups in social networks. An important contribution of this work is the analysis of the proposed algorithm using Extreme Value Theory - a branch of probability that studies extreme events and properties of largest order statistics in random samples. Using this theory we derive an accurate prediction for the algorithm's performance and show that the number of API requests for finding the top-k most popular entities is sublinear in the number of entities. Moreover, we formally show that the high variability of the entities, expressed through heavy-tailed distributions, is the reason for the algorithm's efficiency. We quantify this phenomenon in a rigorous mathematical way.

关键词： application program interfaces randomised algorithms social networking (online) API requests approximation extreme value theory high-degree entities large directed networks online social network Twitter online social networks quick detection randomized algorithm statistics top-k most popular entities algorithm design and analysis Approximation algorithms Indexes Peer-to-peer computing Prediction algorithms Twitter social networks sublinear complexity

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Almost Linear-Time algorithms for Adaptive Betweenness Centrality using Hypergraph Sketches 14

Almost Linear-Time Algorithms for Adaptive Betweenness Centr...

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20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Yoshida, Yuichi Natl Inst Informat Tokyo Japan Preferred Infrastruct Inc Tokyo Japan

ISBN: (纸本)9781450329569

Betweenness centrality measures the importance of a vertex by quantifying the number of times it acts as a midpoint of the shortest paths between other vertices. This measure is widely used in network analysis. In many applications, we wish to choose the k vertices with the maximum adaptive betweenness centrality, which is the betweenness centrality without considering the shortest paths that have been taken into account by already-chosen vertices. All previous methods are designed to compute the betweenness centrality in a fixed graph. Thus, to solve the above task, we have to run these methods k times. In this paper, we present a method that directly solves the task, with an almost linear runtime no matter how large the value of k. Our method first constructs a hypergraph that encodes the betweenness centrality, and then computes the adaptive betweenness centrality by examining this graph. Our technique can be utilized to handle other centrality measures. We theoretically prove that our method is very accurate, and experimentally confirm that it is three orders of magnitude faster than previous methods. Relying on the scalability of our method, we experimentally demonstrate that strategies based on adaptive betweenness centrality are effective in important applications studied in the network science and database communities.

关键词： Adaptive betweenness centrality Adaptive coverage centrality randomized algorithm

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Comparative Analysis and Development of New Approach for Random Testing

Comparative Analysis and Development of New Approach for Ran...

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International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA)

作者： Rangari, Jaypal D. Karmore, Swapnili P. GH Raisoni Coll Engn Dept CSE Nagpur Maharashtra India

ISBN: (纸本)9781479924943

Testing is the process of finding errors in a system or its component. It runs a system in order to find gaps or bugs. It also checks that system is fulfilling its requirement or not. During execution of software testing randomized algorithm is used to generate test cases. It generates random number of choices during test cases execution to produce a result. It selects test cases from the whole input set randomly. The process of test cases generation can be minimized using randomized algorithm. For generating test cases uses two types of randomized algorithm. We used different randomized algorithm on the basis of complexity of code. If the complexity is low then use Monte Carlo randomized algorithm because it has deterministic running time, but whose output may be incorrect with little probability. Las Vegas randomized algorithm is use for high complexity uses because it always gives the correct result. This algorithm compares the correlation between two far inputs and failure-causing inputs. It can avoid complex analysis and calculation of code. In this we focuses on various randomized testing algorithms and its working and functions. We present a method that improves performance random test generation by incorporating result obtained from executing test cases.

关键词： Testing random testing randomized algorithm performance of random testing software testing

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Linear list-approximation for short programs (or the power of a few random bits)

Linear list-approximation for short programs (or the power o...

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IEEE 29th Conference on Computational Complexity (CCC)

作者： Bauwens, Bruno Zimand, Marius Univ Lorraine Nancy France Towson Univ Baltimore MD 21287 USA

ISBN: (纸本)9781479936267

A c-short program for a string x is a description of x of length at most C(x) + c, where C(x) is the Kolmogorov complexity of x. We show that there exists a randomized algorithm that constructs a list of n elements that contains a O(log n)-short program for x. We also show a polynomial-time randomized construction that achieves the same list size for O(log(2) n)-short programs. These results beat the lower bounds shown by Bauwens et al. [1] for deterministic constructions of such lists. We also prove tight lower bounds for the main parameters of our result. The constructions use only O(log n) (O(log(2) n) for the polynomial-time result) random bits. Thus using only few random bits it is possible to do tasks that cannot be done by any deterministic algorithm regardless of its running time.

关键词： Bipartite graph Complexity theory Entropy Polynomials Probabilistic logic Standards Turing machines Kolmogorov complexity approximation randomized algorithm Kolmogorov complexity randomized algorithms Probabilistic logic Automata theory-Turing machines Bipartite graph Complexity theory Polynomial entropy approximation Lower bound Drill bits nitrogen

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A Decentralized Approach to Robust Subspace Recovery

A Decentralized Approach to Robust Subspace Recovery

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Annual Allerton Conference on Communication, Control, and Computing

作者： Mostafa Rahmani George K. Atia Department of Electrical Engineering and Computer Science at the University of Central Florida Orlando USA

ISBN: (纸本)9781509018253

This paper considers subspace recovery in the presence of outliers in a decentralized setting. The intrinsic low-dimensional geometry of the data is exploited to substantially reduce the processing and communication overhead given limited sensing and communication resources at the sensing nodes. A small subset of the data is first selected. The data is embedded into a random low-dimensional subspace then forwarded to a central processing unit that runs a low-complexity algorithm to recover the subspace directly from the data sketch. We derive sufficient conditions on the compression and communication rates to successfully recover the subspace with high probability. It is shown that the proposed approach is robust to outliers and its complexity is independent of the dimension of the whole data matrix. The proposed algorithm provably achieves notable speedups in comparison to existing approaches for robust subspace recovery.

关键词： Robust PCA randomized algorithm Low Rank Matrix Subspace Learning Big Data Outlier Detection Random Embedding

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Signature-Free Asynchronous Byzantine Consensus with t < n/3 and O(n²) Messages 14

Signature-Free Asynchronous Byzantine Consensus with <i>t</i...

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ACM Symposium on Principles of Distributed Computing (PODC)

作者： Mostefaoui, Achour Moumen, Hamouma Raynal, Michel Univ Nantes LINA F-44322 Nantes France Univ Bejaia Bejaia Algeria Univ Rennes Inst Univ France Rennes France Univ Rennes IRISA Rennes France

ISBN: (纸本)9781450329446

This paper presents a new round-based asynchronous consensus algorithm that copes with up to t < n/3 Byzantine processes, where n is the total number of processes. In addition of not using signature, not assuming a computationally-limited adversary, while being optimal with respect to the value of t, this algorithm has several noteworthy properties: the expected number of rounds to decide is four, each round is composed of two or three communication steps and involves O(n(2)) messages, and a message is composed of a round number plus a single bit. To attain this goal, the consensus algorithm relies on a common coin as defined by Rabin, and a new extremely simple and powerful broadcast abstraction suited to binary values. The main target when designing this algorithm was to obtain a cheap and simple algorithm. This was motivated by the fact that, among the first-class properties, simplicity -albeit sometimes under-estimated or even ignored- is a major one.

关键词： Abstraction Asynchronous message-passing system Broadcast abstraction Byzantine process Common coin Consensus Distributed algorithm Optimal resilience randomized algorithm Signature-free algorithm Simplicity

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