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A distributed Computational Model of State of Consciousness 1

引用

8th international conference on Genetic and Evolutionary computing (ICGEC)

作者： Bagchi, Susmit Gyeongsang Natl Univ Dept Informat Jinju 660701 South Korea

ISBN: (数字)9783319122861

ISBN: (纸本)9783319122854

the computational modeling of neurobiological phenomena is an important area in bio-inspired computing. the understanding of state of consciousness as cognitive function is central to it. the functioning of neurological structures is inherently distributed in nature having a closer match to distributed computing. However, the role of functional neurophysiology is critical in cognition modeling. this paper proposes a mathematical model of state of consciousness by mapping the functional neurophysiology and by inducing distributed computing structures in coherence. the scopes of evolution of consciousness and memory are incorporated into the model. the numerical simulation of the distributed computational model is conducted by considering different choice functions. the results illustrate that, gradual evolution of positive consciousness is deterministic under fair excitation from environment.

关键词： Cognition consciousness bio-inspired computing distributed computing

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Applying Model-based Principles on a distributed Robotic System Application 8

Applying Model-based Principles on a Distributed Robotic Sys...

引用

IEEE 8th international conference on Intelligent Data Acquisition and Advanced computing System-Technology and Applications (IDAACS)

作者： Lauschner, Uwe Igel, Burkhard Krawczyk, Lukas Wolff, Carsten Univ Appl Sci & Arts Dortmund Otto Hahn Str 23 D-44227 Dortmund Germany

ISBN: (纸本)9781467383615

Although robotic systems with a high amount of actuators, sensors and software-algorithms involve software development that is usually not trivial, model-based methods are rarely used. this paper presents the entire workflow, supporting the development steps from concept specifications to implementation of distributed robotic system, which are developed model-based with Matlab/ Simulink. Different ARM based target platforms, such as the Raspberry Pi, the robot RobotinoXT, Kinect and SensorTag to realize sensors, actuators and a particle filter are used. the distributed system that consist of four computers is modelled in four Simulink models. the communication with each other and with the components takes places via UDP. the implementation of a preprocessing for the integration of the components and the development of Simulink blocks is necessary. this paper presents and evaluates the distributed approach in robotic systems, as well as its development with Matlab/ Simulink.

关键词： model-based development ARM distritbuted systems robotics Matlab/ Simulink embedded systems IoT CPS wireless systems

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Community influence analysis based on social network structures

Community influence analysis based on social network structu...

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IEEE international conference on Smart City, SmartCity 2015

作者： Li, Yi Wu, Xindong Li, Lei School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science and Engineering Beifang University of Nationalities Yinchuan China Department of Computer Science University of Vermont BurlingtonVT United States

ISBN: (纸本)9781509018932

Modeling and measuring social influence is a major problem in Social Network Analysis. Existing models and methods could handle individual influence analysis conveniently, but they rarely estimate the social influence of communities which are ubiquitous in social networks. Based on the structures of online social networks, a community oriented influence analysis model is proposed. then, we provide an algorithm called CommRank for calculating the social influence of communities. Since the algorithm combines both internal structural information and external interaction data of communities, it estimates community influence more precisely on multiple datasets. Experimental results also demonstrate that, at the cost of a little gain loss, CommRank can dramatically improve the efficiency when dealing with the influence maximization problem. © 2015 IEEE.

关键词： Social networking (online)

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Fog computing- Network Based Cloud computing 2

Fog Computing- Network Based Cloud Computing

引用

2nd international conference on Electronics and Communication Systems (ICECS)

作者： Krishnan, Y. Navaneeth Bhagwat, Chandan N. Utpat, Aparajit P. RV Coll Engn Dept Comp Sci Engn 8th Mile Mysore Rd Bangalore Karnataka India

ISBN: (纸本)9781479972258

Cloud computing provides us a means to upload data and use applications over the internet. As the number of devices connecting to the cloud grows, there is undue pressure on the cloud infrastructure. Fog computing or Network Based computing or Edge computing allows to move a part of the processing in the cloud to the network devices present along the node to the cloud. therefore the nodes connected to the cloud have a better response time. this paper proposes a method of moving the computation from the cloud to the network by introducing an android like appstore on the networking devices.

关键词： Cloud computing Fog computing Network devices app store

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A partial workload offloading framework in a mobile cloud computing context 8

A partial workload offloading framework in a mobile cloud co...

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IEEE 8th international conference on Service-Oriented computing and Applications

作者： Valery, Olivier Chou, Ju-Cheng Tsao, Yulin Liu, Pangfeng Wu, Jan-Jan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Dept Comp Sci & Informat Engn Taipei Taiwan Acad Sinica Res Ctr Informat Technol Innovat Inst Informat Sci Taipei Taiwan

ISBN: (纸本)9781467394697

Mobile devices have become increasingly prevalent in recent years, leading the public to reassess their expectations in terms of user experience. As a result, industry has progressively turned to utilizing hardware components, such as GPUs (graphics processing units), which are traditionally present on computers. Despite their sophistication, mobile devices lack the capacity to execute resource-intensive tasks quickly and efficiently, and the state of the art is to leverage heterogeneous cloud resources to augment mobile devices. However, network transfer costs drastically limit the advantage of this approach. While performing computing tasks on a heterogeneous system is a well-studied area, how to offload workload onto a heterogeneous cloud in the presence of an unstable network remains an outstanding problem. this paper presents the design and implementation of a workload offloading framework that transparently mitigates the network transfer cost and takes advantage of a heterogeneous resource-rich cloud for speeding up mobile devices' GPGPU computations. Our approach is based on an adaptive method that partitions the workload and maps the processing elements to heterogeneous local and remote resources. By partitioning the tasks, our system increases the utilization of mobile and server resources while reducing the amount of data to transfer over the network. Our results show that adaptive partitioning can have a significant impact on the performance of benchmarks, even in a dynamic environment.

关键词： GPGPU mobile cloud computing pervasive computing computation offloading heterogeneous system resource mapping

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UniCrawl: A Practical Geographically distributed Web Crawler 8

UniCrawl: A Practical Geographically Distributed Web Crawler

引用

IEEE 8th international conference on Cloud computing

作者： Le Quoc, Do Fetzer, Christof Felber, Pascal Riviere, Etienne Schiavoni, Valerio Sutra, Pierre Tech Univ Dresden Syst Engn Grp Dresden Germany Univ Neuchatel Neuchatel Switzerland

ISBN: (纸本)9781467372879

As the wealth of information available on the web keeps growing, being able to harvest massive amounts of data has become a major challenge. Web crawlers are the core components to retrieve such vast collections of publicly available data. the key limiting factor of any crawler architecture is however its large infrastructure cost. To reduce this cost, and in particular the high upfront investments, we present in this paper a geo-distributed crawler solution, UniCrawl. UniCrawl orchestrates several geographically distributed sites. Each site operates an independent crawler and relies on well-established techniques for fetching and parsing the content of the web. UniCrawl splits the crawled domain space across the sites and federates their storage and computing resources, while minimizing thee inter-site communication cost. To assess our design choices, we evaluate UniCrawl in a controlled environment using the ClueWeb12 dataset, and in the wild when deployed over several remote locations. We conducted several experiments over 3 sites spread across Germany. When compared to a centralized architecture with a crawler simply stretched over several locations, UniCrawl shows a performance improvement of 93.6% in terms of network bandwidth consumption, and a speedup factor of 1.75.

关键词： web crawler geo-distributed system cloud federation storage map-reduce

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Mycocloud: Elasticity through Self-organized Service Placement in Decentralized Clouds 8

Mycocloud: Elasticity through Self-organized Service Placeme...

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IEEE 8th international conference on Cloud computing

作者： Dubois, Daniel J. Valetto, Giuseppe Lucia, Donato Di Nitto, Elisabetta Imperial Coll London Dept Comp London England Fdn Bruno Kessler Distributed Adapt Syst Unit Trento Italy Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781467372879

We present Mycocloud, a fully self-organized approach to service placement. Mycocloud supports service elasticity within a network of hosts with heterogeneous computational capacity. Mycocloud proposes a completely decentralized algorithm that continuously calculates the dynamic placement of different services on the host nodes, in response to the varying demand for each service, the churn and dynamism of the node set contributing resources to the system, and the changes in the overlay topology. Our simulation results show how Mycocloud provides good performance in terms of convergence rate, response time, system load and network traffic overhead.

关键词： Cloud computing Elasticity Peer-to-peer clouds Performance Self-organization Service placement

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Performance Enhancement of distributed K-Means Clustering for Big Data Analytics through In-memory Computation 8

Performance Enhancement of Distributed K-Means Clustering fo...

引用

8th international conference on Contemporary computing (IC3)

作者： Ketu, Shwet Agarwal, Sonali Indian Inst Informat Technol Allahabad Uttar Pradesh India

ISBN: (纸本)9781467379489

Big Data analytics are recently coming up as prominent research area in the field of Information Technology serving various data driven domains for effective processing of big data. Big data analytics have been facing various challenges such as inefficient storage, processing delays, low rate of information retrieval, complex algorithms which cannot be handled and managed using traditional methods. For assisting software developers to deal with big data challenges, new programming frameworks are required. In this research paper Hadoop MapReduce and Apache Spark are taken for this purpose which supports on-disk and in-memory computation respectively. Clustering is one of the important tasks of big data mining used for information retrieval and knowledge discovery. In this research work, we are analyzing the performance of distributed K-Means clustering based on in-memory and on-disk computational models. For performance enhancement of distributed K-Means clustering, in-memory and on-disk computational models have been adopted and an experimental analysis has been performed.

关键词： Big data Big data analytic distributed K-Mean Hadoop MapReduce Apche Spark On- disk computation In-memory computation

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Method of Migration from Single Server System to Server Cluster 8

Method of Migration from Single Server System to Server Clus...

引用

IEEE 8th international conference on Intelligent Data Acquisition and Advanced computing System-Technology and Applications (IDAACS)

作者： Surkov, Sergii Martynyuk, Oleksandr Odessa Natl Polytech Univ Inst Comp Syst Shevchenko Ave 1 UA-65011 Odesa Ukraine

ISBN: (纸本)9781467383615

Small web services usually are based on a single server which handles requests from all the clients and does not consider scalability. With growth of the network traffic and complexity of the requests to handle large amount of requests solution of upgrading server hardware is usually applied. But even the most powerful single server could not process all the requests if traffic is too high. In this work cluster model of load balancer with dynamic web pages and REST requests and methodic of migration from single server model to server cluster as the result of growing application web services are proposed.

关键词： distributed System Java Web Server Load Balancer REST API

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Scalable Network Traffic Classification Using distributed Support Vector Machines 8

Scalable Network Traffic Classification Using Distributed Su...

引用

IEEE 8th international conference on Cloud computing

作者： Do Le Quoc D'Alessandro, Valerio Park, Byungchul Romano, Luigi Fetzer, Christof Tech Univ Dresden Dresden Germany Univ Naples Federico II Naples Italy Parthenope Univ Naples Naples Italy Univ Toronto Toronto ON Canada

ISBN: (纸本)9781467372879

Internet traffic has increased dramatically in recent years due to the popularization of the Internet and the appearance of wireless Internet mobile devices such as smartphones and tablets. the explosive growth of Internet traffic has introduced a practical example that demonstrates the concept of Big Data. Accurate identification and classification of large network traffic data plays an important role in network management including capacity planning, network forensics, QoS and intrusion detection. However, the state-of-the-art solutions, which rely on a dedicated server, are not scalable for analyzing high volume network traffic data. In this paper, we implement a distributed Support Vector Machines (SVMs) framework for classifying network traffic using Hadoop, an open-source distributed computing framework for Big Data processing. We design a global parameter store that maintains the global shared parameters between SVM training nodes. the distributed SVMs have been deployed on a 20 node cluster to analyze real network traffic trace. the results demonstrate that with 19 Mapper nodes the system is around 30% faster than CloudSVM solution and outperforms the standalone SVM with nearly 9 times faster in training process and 15 times in the classifying process. In addition, the distributed SVMs architecture is designed to analyze large scale datasets. therefore, it can be used not only for processing network traffic dataset, but also other large scale datasets such as Web data.

关键词： Distribted Systems distributed SVMs Hadoop Network traffic analysis Network traffic classification Support vector machines

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