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DDP: distributed Network Updates in SDN 38

DDP: Distributed Network Updates in SDN

引用

38th IEEE international conference on distributed computing Systems (ICDCS)

作者： Li, Geng Qian, Yichen Zhao, Chenxingyu Yang, Y. Richard Yang, Tong Tongji Univ Shanghai Peoples R China Yale Univ New Haven CT 06520 USA Peking Univ Beijing Peoples R China

ISBN: (纸本)9781538668719

How to quickly and consistently update a network is among the most fundamental and common challenges in software defined networking (SDN) systems. Current approaches heavily rely on the (logically) centralized controller to initiate and orchestrate the network updates, resulting in long latency of update completion. In this paper, we present DDP, a system for fast, distributed network updates while preserving various consistency properties. The key technique in DDP is a novel primitive named datapath operation container (DOC), where each DOC is encoded with an individual operation and its dependency logic. DDP adopts the simple, but powerful DOCs to configure the network, so that network updates can be triggered and executed at the data plane in a distributed and local manner. Novel algorithms are designed to compute and optimize the DOCs for consistent updates. We implement DDP to evaluate its performance in various update scenarios. Experimental results show that DDP significantly improves network update speed by up to 52.1% for the real-time updates initiated by the controller, and further improves the speed by 55.6-61.4% for the updates directly triggered at the data plane, such as failure recovery.

关键词： Control systems Logic gates Real-time systems Radio frequency distributed databases Process control Containers

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Adaptive Nature-inspired Fog Architecture 2

Adaptive Nature-inspired Fog Architecture

引用

2nd IEEE international conference on Fog and Edge computing (ICFEC)

作者： Kimovski, Dragi Ijaz, Humaira Saurabh, Nishant Prodan, Radu Univ Klagenfurt Distributed Multimedia Syst Grp Klagenfurt Austria Univ Innsbruck Distributed & Parallel Syst Grp Innsbruck Austria Univ Sargodha Sargodha Pakistan

ISBN: (纸本)9781538664889

During the last decade, Cloud computing has efficiently exploited the economy of scale by providing low cost computational and storage resources over the Internet, eventually leading to consolidation of computing resources into large data centers. However, the nascent of the highly decentralized Internet of Things (IoT) technologies that cannot effectively utilize the centralized Cloud infrastructures pushes computing towards resource dispersion. Fog computing extends the Cloud paradigm by enabling dispersion of the computational and storage resources at the edge of the network in a close proximity to where the data is generated. In its essence, Fog computing facilitates the operation of the limited compute, storage and networking resources physically located close to the edge devices. However, the shared complexity of the Fog and the influence of the recent IoT trends moving towards deploying and interconnecting extremely large sets of pervasive devices and sensors, requires exploration of adaptive Fog architectural approaches capable of adapting and scaling in response to the unpredictable load patterns of the distributed IoT applications. In this paper we introduce a promising new nature-inspired Fog architecture, named SmartFog, capable of providing low decision making latency and adaptive resource management. By utilizing novel algorithms and techniques from the fields of multi-criteria decision making, graph theory and machine learning we model the Fog as a distributed intelligent processing system, therefore emulating the function of the human brain.

关键词： Computer architecture Cloud computing Optimization Decision making Edge computing Adaptation models Electronic mail

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Experiences with Managing Data Ingestion into a Corporate Datalake

Experiences with Managing Data Ingestion into a Corporate Da...

引用

international conference on Collaborative computing: networking, Applications and Worksharing (CollaborateCom)

作者： Sean Rooney Daniel Bauer Luis Garcés-Erice Peter Urbanetz Florian Froese Sasa Tomic IBM Research Ruschlikon Switzerland

We explain our experiences in designing, building and running a large corporate Datalake. Our platform has been running for over two years and makes a wide variety of corporate data assets, such as sales, marketing, customer information, as well as data from less conventional sources such as weather, news and social media available for analytics purposes to many teams across the company. We focus on describing the management of data and in particular how it is transferred and ingested into the platform.

关键词： Tools Companies distributed databases Standards Relational databases Sparks

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ECHO: A Reliable distributed Cellular Core Network for Hyper-scale Public Clouds 18

ECHO: A Reliable Distributed Cellular Core Network for Hyper...

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24th Annual international conference on Mobile computing and networking (MobiCom)

作者： Binh Nguyen Zhang, Tian Radunovic, Bozidar Stutsman, Ryan Karagiannis, Thomas Kocur, Jakub Van der Merwe, Jacobus Univ Utah Salt Lake City UT 84112 USA Microsoft Res Redmond WA USA Core Network Dynam Berlin Germany

ISBN: (纸本)9781450359030

Economies of scale associated with hyper-scale public cloud platforms offer flexibility and cost-effectiveness, resulting in various services and businesses moving to the cloud. One area with little progress in this direction is cellular core networks. A cellular core network manages the state of cellular clients;it is a large distributed state machine with different virtualization challenges compared to typical cloud services. In this paper we present a novel cellular core network architecture, called ECHO, particularly suited to public cloud deployments, where the availability guarantees might be an order of magnitude worse compared to existing (redundant) hardware platforms. We present the design and implementation of our approach and evaluate its functionality on a public cloud platform. Analysis shows ECHO promises higher availability than existing telco solutions.

关键词： reliability distributed systems mobile networks nfv

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Information Centric networking Media Streaming Experiment Platform Design 2nd

Information Centric Networking Media Streaming Experiment Pl...

引用

2nd international conference on Smart computing and Communication (SmartCom)

作者： Lu, Yuming Hu, Tao Wang, Xiaojun Shenzhen Inst Informat Technol Shenzhen Key Lab Visual Media Proc & Streaming Me Shenzhen 518172 Peoples R China

ISBN: (纸本)9783319738307;9783319738291

Information Centric networking (ICN) is a revolutionary concept that considers data content interconnected networking instead of equipment interconnected data transmission. Designing and optimizing such a system require not only a software simulator environment but also an experimental platform to test new type of big-data processing architecture. This paper introduce and contribute on put forward the idea of a new distributed publish-subscribe media big data processing platforms base on the ICN architecture. First part discusses the component that included in a ICN media streaming platform, and then describes the design of a distributed publish-subscribe (P/S) system, which manages the subscription query, storage, transmission and content scheduling that complies with the named data networking (NDN) framework. The simulation utilizes software defined network (SDN) controller to carry out artificial intelligence supported computing in the cloud to generate global view of the distributed media content.

关键词： Streaming media Information center networking Publish-subscribe systems Software defined networks Named data networks

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distributed User-based Collaborative Filtering on an Opportunistic Network 32

Distributed User-based Collaborative Filtering on an Opportu...

引用

32nd IEEE international conference on Advanced Information networking and Applications (IEEE AINA)

作者： Barbosa, Lucas Nunes Gemmell, Jonathan Horvath, Miller Heimfarth, Tales Univ Fed Lavras UFLA Comp Sci Dept Lavras Brazil DePaul Univ Coll Comp & Digital Media Chicago IL USA Ctr Univ FEI Comp Sci Dept Sao Bernardo Do Campo Brazil

ISBN: (纸本)9781538621950

This paper presents a novel collaborative filtering recommender system based on an opportunistic distributed network. Collaborative filtering algorithms are widely used in many online systems. Often, the computation of these recommender systems is performed on a central server, controlled by the provider, requiring constant internet connection for gathering and computing data. However, in many scenarios, such constraints cannot be guaranteed or may not even be desired. This work proposes a recommendation engine where users share information via an opportunistic network independent of a dedicated internet connection. In this framework, each node is responsible for gathering information from nearby nodes and calculating its own recommendations. Using a centralized collaborative filtering recommender as a baseline, we evaluate three simulated scenarios composed by different movement speeds and data exchange parameters. Our results show that in a relatively short time, an opportunistic distributed recommender systems can achieve results similar to a traditional central system. Our analysis shows that the speed at which the opportunistic recommender system stabilizes depends on several factors including density of the users, movement speed and patterns of the users, and transmission strategies.

关键词： opportunistic networks recommender systems mobile ad hoc networks

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distributed-Memory Vertex-Centric Network Embedding for Large-Scale Graphs

Distributed-Memory Vertex-Centric Network Embedding for Larg...

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Big Data Intelligence and computing (DATACOM), international conference on

作者： Sara Riazi Boyana Norris Department of Computer and Information Science University of Oregon Eugene OR USA

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

Network embedding is an important step in many different computations based on graph data. However, existing approaches are limited to small or middle size graphs with fewer than a million edges. In practice, web or social network graphs are orders of magnitude larger, thus making most current methods impractical for very large graphs. To address this problem, we introduce a new distributed-memory parallel network embedding method based on Apache Spark and GraphX. We demonstrate the scalability of our method as well as its ability to generate meaningful embeddings for vertex classification and link prediction on both real-world and synthetic graphs.

关键词： Social networking (online) Scalability Cluster computing Big Data Prediction algorithms Classification algorithms

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Personalized Privacy Protection with Spatio-Temporal Features in Social Networks

Personalized Privacy Protection with Spatio-Temporal Feature...

引用

IEEE international Symposium on Parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Xiufeng Xia Boyang Yin Xiangyu Liu Jiajia Li Rui Zhu Chuanyu Zong School of Computer Science Shenyang Aerospace University Shenyang China School of IT and Bussiness Wellington Institute of Technology Lower Hutt New Zealand

ISBN: (纸本)9781728152103

After users publish the social data, the privacy information was available to an attacker through node re-identification. In real life, users have different privacy protection requirements. Due to the personalization of users' privacy needs, the protection technology of attacks with the same background knowledge is insufficient to meets users' privacy protection requirements. To solve the problem, this paper proposes spatio-temporal classification levels of social network data background knowledge, and provides corresponding protection methods for different background knowledge intensification of each level. Specifically, three levels of protection requirements are defined on the basis of the gradually increasing background knowledge of the spatio-temporal features of the attacker. The effectiveness of the framework is verified by a large number of experiments.

关键词： data privacy feature selection social networking (online) Data privacy Feature extraction Social Networks Privacy Consumers Social Capital

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An Apriori-Based Energy-Efficient Algorithm for SDN Data Center

An Apriori-Based Energy-Efficient Algorithm for SDN Data Cen...

引用

IEEE international Symposium on Parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Peng HongYu Hao TianLu Computer School. TangShan College TangShan China

ISBN: (纸本)9781728152103

This paper targets on energy saving for SDN-based datacenter. A SApriori energy-efficient Algorithm is provided. Though full considering the user request, response and relationships between various factors, SApriori is more practical and more valuable than other typical energy-efficient Algorithm. Access efficiency is improved and overall storage requirements are reduced by running the SApriori. The datacenter network's control and forwarding is separated effectively by adopting SDN. Therefore, efficient deployment and implementation of energy saving strategy is possible in SDN structure. Simulation result shows that the strategy proposed by this paper can save more energy than other typical energy-efficient Algorithm and overall storage is reduced.

关键词： computer centres energy conservation software defined networking telecommunication control telecommunication power management Communication and control Energy conservation data center Energy efficiency Storage requirements algorithms Energy sources energy saving

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Symmetry Structure Research of Complex Community from Disconnection Vertexes Perspective

Symmetry Structure Research of Complex Community from Discon...

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IEEE international Symposium on Parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Duo Zhai Jing Shan Jiaying Wang Mingyang Shao Jianzhao Cao Shenyang Jianzhu University Shenyang Shenyang China

ISBN: (纸本)9781728152103

With the development of social network, current research on social network is analyzed by graph theory. According to the topology, studies can determine the social network, and analyze many practical problems, such as social ties, group cooperation, team influence, hot spread node of overlapping community and so on. Now, available research for community mining establishes algorithms widely by analyzing the section topology of neighbor vertexes. However, little consider topology formability or symmetries. For the research, we propose an algorithm, in which the complex graphs are considered as some isolated vertexes through the model of granular graph. This paper proposes an approach to mine outliers based on graph adjacency matrix and combinatorial mathematics, then analyses the symmetries of outliers for overall structure.

关键词： combinatorial mathematics data mining social networking (online) combinatorial mathematics data mining Social Media Social Networks outliers Graph theory Symmetry topology Communities Disconnection

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