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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Gao, Zhipeng Miao, Dong Zhao, Langcheng Mo, Zijia Qi, Guangpeng Yan, Liang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China Inspur Grp Ltd Jinan 25001 Peoples R China

ISBN: (纸本)9781728195056

The traditional centralized data processing model represented by cloud computing cannot meet the data processing requirements that are gradually tending to the edge. Therefore, a new distributed computing model coordinated by the end devices, edges and cloud has become the main development direction. However, artificial intelligence algorithms that are widely used in cloud-only approach are difficult to embed in resource constrained distributed frameworks. To address this issue, we propose Triple-partition Network, a neural network model augment with three exit points. The structure of three exit points allows to segment the traditional neural network and deploying them on the end devices, edges, and cloud. By setting up suitable exit points through the Entropy Topsis comprehensive evaluation model, part of the data can exit the network in advance to improve the efficiency of computing services. In this experiment, the classic neural networks (Alexnet, Resnet) are used to study the Triple-partition Network on a state-of-art platform and show that trained Triple-partition Network can greatly reduce the end-to-end latency by over 3x while achieving high accuracy.

关键词： Edge computing distributed computing model End device-edge-cloud Exit points Triple-partition Network

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Study of a distributed computing model for the SuperB experiment

Study of a distributed computing model for the SuperB experi...

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IEEE Nuclear Science Symposium Conference 2009

作者： Fella, Armando Luppi, Eleonora Tomassetti, Luca INFN CNAF Natl Inst Nucl Phys I-40126 Bologna Italy Ferrara Univ Dept Phys I-44100 Ferrara Italy INFN Natl Inst Nucl Phys I-44100 Ferrara Italy

ISBN: (纸本)9781424439614

SuperB is an international enterprise aiming at the construction of a very high luminosity asymmetric e+e- flavor factory. SuperB will be a partner, together with the LHC and eventually the ILC, in searching for new physics. Since the first steps of detector design, the load of computing issues in terms of Monte Carlo algorithm execution requires the exploitation of resources in a full distributed model. The SuperB choice is to build a Grid integrated system identifying both the consolidated and upcoming solutions to accomplish the typical HEP class experiment requirements. The model, moreover, has to be flexible, in order to deal with the rapid technological improvements and the expected long life of the experiment. The in progress design and setup of SuperB distributed computing model is presented including the complete discussion of the Grid services selected for the prototype.

关键词： HEP computing distributed computing model Grid computing

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Misleading generalized itemset mining in the cloud 12

Misleading generalized itemset mining in the cloud

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12th IEEE International Symposium on Parallel and distributed Processing with Applications (ISPA)

作者： Baralis, Elena Cagliero, Luca Cerquitelli, Tania Chiusano, Silvia Garza, Paolo Grimaudo, Luigi Pulvirenti, Fabio Politecn Torino Dipartimento Automat & Informat I-10129 Turin Italy

ISBN: (纸本)9781479942930

In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mining technique, which entails the discovery of multiple-level patterns hidden in the analyzed data by exploiting analyst-provided taxonomies. Among the generalized itemsets, the most peculiar high-level patterns are those with many contrasting correlations among items at different abstraction levels. They represent misleading situations that are worth analyzing separately by experts during manual inspection. This paper proposes a novel cloud-based service, named MGI-CLOUD, to efficiently mine misleading multiple-level patterns, i.e., theMisleading Generalized Itemsets, on a distributed computing environment. MGI-CLOUD consists of a set of distributed MapReduce jobs running in the cloud. As a case study, the system has been contextualized in a real-life scenario, i.e., the analysis of traffic law infractions committed in a smart city environment. The experiments, performed on real datasets, demonstrate the efficiency and effectiveness of MGI-CLOUD.

关键词： Generalized itemset mining distributed computing model cloud-based service smart city

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Quality Control in Crowdsourcing Systems Issues and Directions

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IEEE INTERNET computing 2013年第2期17卷 76-81页

作者： Allahbakhsh, Mohammad Benatallah, Boualem Ignjatovic, Aleksandar Motahari-Nezhad, Hamid Reza Bertino, Elisa Dustdar, Schahram Univ New S Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Univ New S Wales Sydney NSW 2052 Australia Hewlett Packard Labs Palo Alto CA USA Purdue Univ W Lafayette IN 47907 USA Vienna Univ Technol Distributed Syst Grp Inst Informat Syst Vienna Austria

As a new distributed computing model, crowdsourcing lets people leverage the crowd's intelligence and wisdom toward solving problems. This article proposes a framework for characterizing various dimensions of quality control in crowdsourcing systems, a critical issue. The authors briefly review existing quality-control approaches, identify open issues, and look to future research directions.

关键词： crowdsourcing crowdsourcing workflows quality control distributed computing model design year Crowdsource Quality Control Runtime Quality management

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Power and limits of distributed computing shared memory models

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THEORETICAL COMPUTER SCIENCE 2013年 509卷 3-24页

作者： Herlihy, Maurice Rajsbaum, Sergio Raynal, Michel Brown Univ Dept Comp Sci Providence RI 02912 USA Univ Nacl Autonoma Mexico Inst Matemat Mexico City 04510 DF Mexico Univ Rennes 1 IRISA Inst Univ France F-35042 Rennes France

What can and cannot be computed in a distributed system is a complex function of the system's communication model, timing model, and failure model. Considering a canonical distributed system model, where processes execute asynchronously, communicate by reading and writing shared memory, and fail by crashing, this paper surveys important results about computability, and explains the fundamental role that topology plays in the distributed computability theory. The paper also considers different types of additional assumptions that allow impossibility results to be circumvented. These assumptions are known under the names failure detectors and adversaries. Finally, it presents a powerful simulation technique (known under the name BG simulation), which allows to show that, from a computability point of view, t-resilience is not different from wait-freedom. When pieced together, the aim of all the concepts, notions, models, and algorithms presented in the paper, is to provide the reader with a synthetic view of important results on the distributed asynchronous read/write shared-memory model, its power and its limits. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Adversary Agreement Asynchronous system Borowsky-Gafni (BG) simulation Concurrency Crash failure distributed computability distributed computing model Failure detector Fault-tolerance Iterated model Liveness model equivalence Recursion Resilience Shared memory system Snapshot Task Topology Wait-freedom

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Power and limits of distributed computing shared memory models

Power and limits of distributed computing shared memory mode...

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18th International Colloquium on Structural Information and Communication Complexity (SIROCCO)

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SEARUM: a cloud-based SErvice for Association RUle Mining

SEARUM: a cloud-based SErvice for Association RUle Mining

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12th IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Apiletti, Daniele Baralis, Elena Cerquitelli, Tania Chiusano, Silvia Grimaudo, Luigi Politecn Torino Dipartimento Automat & Informat Turin Italy

ISBN: (纸本)9780769550220

Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. The automatic analysis of such huge data volume is a challenging task since a large amount of interesting knowledge can be extracted. Association rule mining is an exploratory data analysis method able to discover interesting and hidden correlations among data. Since this data mining process is characterized by computationally intensive tasks, efficient distributed approaches are needed to increase its scalability. This paper proposes a novel cloud-based service, named SEARUM, to efficiently mine association rules on a distributed computing model. SEARUM consists of a series of distributed MapReduce jobs run in the cloud. Each job performs a different step in the association rule mining process. As a case study, the proposed approach has been applied to the network data scenario. The experimental validation, performed on two real network datasets, shows the effectiveness and the efficiency of SEARUM in mining association rules on a distributed computing model.

关键词： association rule mining distributed computing model cloud-based service network data analysis

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Platform for General-Purpose distributed Data-Mining on Large Dynamic Graphs

Platform for General-Purpose Distributed Data-Mining on Larg...

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IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises

作者： Matthias Steinbauer Gabriele Kotsis Johannes Kepler University Linz Department of Telecooperation

ISBN: (纸本)9781479904051

We present an approach to data mining on arbitrary graph data that uses a cloud-based distributed computing model for dynamic provisioning of computing resources as the graph model grows or shrinks. Further, we introduce the concept of logging graph changes as a basis for calculating properties of dynamic graphs. We briefly describe queries that leverage the dynamic graph model, for instance, by using a snapshot of the original graph while an algorithm executes or adapting query results as the graph changes. To demonstrate the feasibility of our approach, we conducted an initial evaluation, which shows that our parallel computing model can dramatically improve load times. Raw data imported into our system is processed faster on larger compute clusters.

关键词： Graphical models data mining Platform graph data Snapshots Line graph distributed computing model

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A Survey on Some Recent Advances in Shared Memory models 1

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18th International Colloquium on Structural Information and Communication Complexity (SIROCCO)

作者： Rajsbaum, Sergio Raynal, Michel Univ Nacl Autonoma Mexico Inst Matemat Mexico City 04510 DF Mexico Univ Rennes 1 Inst Univ France F-35014 Rennes France Univ Rennes 1 IRISA F-35014 Rennes France

ISBN: (数字)9783642222122

ISBN: (纸本)9783642222115;9783642222122

Due to the advent of multicore machines, shared memory distributed computing models taking into account asynchrony and process crashes are becoming more and more important. This paper visits models for these systems and analyses their properties from a computability point of view. Among them, the base snapshot model and the iterated model are particularly investigated. The paper visits also several approaches that have been proposed to model failures (mainly the wait-free model and the adversary model) and gives also a look at the BG simulation. The aim of this survey is to help the reader to better understand the power and limits of distributed computing shared memory models.

关键词： Adversary Agreement Asynchronous system BG simulation Concurrency Core Crash failure distributed computability distributed computing model Fault-Tolerance Iterated model Liveness model equivalence Progress condition Recursion Resilience Shared memory system Snapshot Survivor set Task Topology Wait-freedom

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An adaptive programming model for fault-tolerant distributed computing

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE computing 2007年第1期4卷 18-31页

作者： Gorender, Sergio Macedo, Raimundo Jose de Araujo Raynal, Michel Univ Fed Bahia Dept Comp Sci Distributed Syst Lab BR-40170110 Salvador BA Brazil Univ Rennes 1 IRISA F-35042 Rennes France

The capability of dynamically adapting to distinct runtime conditions is an important issue when designing distributed systems where negotiated quality of service (QoS) cannot always be delivered between processes. Providing fault tolerance for such dynamic environments is a challenging task. Considering such a context, this paper proposes an adaptive programming model for fault-tolerant distributed computing, which provides upper-layer applications with process state information according to the current system synchrony ( or QoS). The underlying system model is hybrid, composed by a synchronous part ( where there are time bounds on processing speed and message delay) and an asynchronous part ( where there is no time bound). However, such a composition can vary over time, and, in particular, the system may become totally asynchronous ( e. g., when the underlying system QoS degrade) or totally synchronous. Moreover, processes are not required to share the same view of the system synchrony at a given time. To illustrate what can be done in this programming model and how to use it, the consensus problem is taken as a benchmark problem. This paper also presents an implementation of the model that relies on a negotiated quality of service ( QoS) for communication channels.

关键词： adaptability asynchronous/synchronous distributed system consensus distributed computing model fault tolerance quality of service

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