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CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer Topology 33rd

CRAWL: A Trace Routing Algorithm Based on Hybrid Two-Layer T...

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33rd international conference on Advanced Information networking and Applications, AINA 2019

作者： Zheng, Li-ming Long, Wen-feng Liu, Yu-Jia Sun, Wei-dong Department of Message Communication Armed Police Officer Academy Chengdu China National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783030150341

Data distribution is a key technology for resources convergence and sharing in distributed environment. To better meet the requirement for real time data distribution in the dynamic network, a trace routing algorithm named CRAWL based on the hybrid two-layered topology is put forward. The algorithm contains an overlay topology named CBDLO, upper of which consists of multiple distributed balanced binary trees corresponding to different properties and the lower of which is an unstructured topology. CRAWL forwards the data on the lower unstructured topology in the form of random walk, so that the data can be sent to the corresponding upper topology entry. It also includes a matching algorithm named CDM for the parallel matching data properties on the upper distributed and balanced binary tree and transmitting the matched data to the nodes that are interested in the data. The experimental results show that the algorithm can effectively support large-scale data distribution in a dynamical network, reduce distribution overhead and matching delays. © 2019, Springer Nature Switzerland AG.

关键词： Routing algorithms

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Computation of Birnbaum's Importance Using Logic Differential Calculus 42

Computation of Birnbaum's Importance Using Logic Differentia...

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42nd international conference on Telecommunications and Signal Processing (TSP)

作者： Kvassay, Miroslav Rusnak, Patrik Sedlacek, Peter Univ Zilina Fac Management Sci & Informat Zilina Slovakia

ISBN: (纸本)9781728118642

Reliability analysis of technical systems, such as telecommunication, computer, or distribution networks, is a complex problem, which includes a lot of steps. Very important step is evaluation of importance of the system components for operation of the system. For this task, special indices known as importance measures are used. These measures can be structure, time-independent or time-dependent. One of the most commonly used importance measures is Birnbaum's Importance (BI). The structure version of this measure is defined as a relative number of situations in which a failure of a specific system component causes that the system fails. The time-independent version of this measure agrees with the probability that a failure of a given component results in a failure of the system, while the time-dependent version expresses how this probability changes over time. BI is used in computation of many other importance measures. This indicates that development of a unified approach for its computation can be used to develop a comprehensive framework for evaluation of importance of system components. In this paper, we present how logic differential calculus can be used for development of such an approach, and how various BI measures can be used in reliability analysis of a distributed computing system.

关键词： distributed computing system importance analysis logic differential calculus reliability structure function

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The Prediction of Buzz in Social Internet of Things

The Prediction of Buzz in Social Internet of Things

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international conference on Parallel distributed and Grid computing (PDGC)

作者： Himanshu Jindal Monika Bharti Computer Science Engineering and Information Technology Jaypee University of Information Technology Waknaghat Solan

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

Social Internet of Things is a critical digital tool for companies of different sizes. Good governance of the Social Internet of Things allows successful strategy to emphasise brands, making products no longer discretionary. Buzz marketing is a powerful advertising strategy that focuses on exploiting the value of a promotion or item, through conversations between family members and friends, or through wider conversations on the Social Internet of Things platforms. Buzz's forecast allows for an overview of the ranking of retailers by posts shared by future consumers or members on social media platforms. Buzz estimation in the Social Internet of Things Platforms, like Twitter, is a daunting challenge over real-time data by defining multiple aspects for buzz exploration. The bulk of buzz predictor analysis studies are modelled on machine learning methods like Radial Base Feature, Neural Network Models, Ant Colony Optimization, and Support Vector Devices. In this study, Random Forest is being used to make buzz predictions as it creates multiple decision trees and mixes them together again to make predictions more precise. The findings of comparative studies with Support Vector Machine (SVM) in three separate kernels and Radial Basis Function (RBF), it suggest that Random Forest is better at forecasting buzz and has also established a rating list of important features in order to achieve reasonable prediction performance. The features suggest that the main important characteristics to define the subject being generated are either “Buzz” or “Not Buzz”. Random Forest showed an average improvement in accuracy with a rate of 99% and the highest training time. However, although the Radial Based Function method has proved to be quite efficient, the Support Vector Machine solution is of mediocre accuracy.

关键词： Social networking (online) Support vector machines Internet of Things Blogs Radio frequency Kernel Random forests

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An Edge computing Service Model Based on Information-Centric networking

An Edge Computing Service Model Based on Information-Centric...

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international conference on Parallel and distributed Systems (ICPADS)

作者： Zhenyu Fan Wang Yang Kaijin Tian School of Computer Science and Engineering Central South University Changsha P.R. China

ISBN: (纸本)9781728125848

In edge computing scenarios, the computing service provider needs to be selected according to the edge server and network status. In the TCP/IP architecture, edge computing applications rely on centralized proxy servers to select an appropriate edge server, which leads to additional network overhead and increases system response latency. Compared with TCP/IP, Information-Centric networking(ICN) has more potential to provide efficient network support for edge computing, so we consider leveraging ICN facilitate edge computing. However, traditional ICN cannot be used directly in edge computing because the computing results are dynamically generated in edge computing. Therefore, we propose an edge computing service model based on ICN, which contains a new naming scheme, service session models and service request forwarding strategies. Our edge computing service model can forward computing requests to an appropriate edge server in a distributed way, which can provide real-time, dynamic and efficient edge computing services.

关键词： Edge computing Information-Centric networking Service model Forwarding strategy networking computing Service Edge computing Information Network system response

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Blockchain-Enabled Collaborative Intrusion Detection in Software Defined Networks

Blockchain-Enabled Collaborative Intrusion Detection in Soft...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Wenjun Fan Younghee Park Shubham Kumar Priyatham Ganta Xiaobo Zhou Sang-Yoon Chang Computer Science Department College of Engineering and Applied Science University of Colorado Colorado Springs Colorado Springs CO United States Computer Engineering Department San Jose State University San Jose CA United States

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Collaborative intrusion detection system (CIDS) shares the critical detection-control information across the nodes for improved and coordinated defense. Software-defined network (SDN) introduces the controllers for the networking control, including for the networks spanning across multiple autonomous systems, and therefore provides a prime platform for CIDS application. Although previous research studies have focused on CIDS in SDN, the real-time secure exchange of the detection-relevant information (e.g., the detection signature) remains a critical challenge. In particular, the CIDS research still lacks robust trust management of the SDN controllers and the integrity protection of the collaborative defense information to resist against the insider attacks transmitting untruthful and malicious detection signatures to other participating controllers. In this paper, we propose a blockchain-enabled collaborative intrusion detection in SDN, taking advantage of the blockchain's security properties. Our scheme achieves three important security goals: to establish the trust of the participating controllers by using the permissioned blockchain to register the controller and manage digital certificates, to protect the integrity of the detection signatures against malicious detection signature injection, and to attest the delivery/update of the detection signature to other controllers. Our experiments in CloudLab based on a prototype built on Ethereum, Smart Contract, and IPFS demonstrates that our approach efficiently shares and distributes detection signatures in real-time through the trustworthy distributed platform.

关键词： Smart contracts Collaboration Intrusion detection Real-time systems Security Software defined networking Trust management

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Stochastic Joint Bandwidth and Computational Allocation for Multi-Users and Multi-Edge-Servers in 5G D-RANs 4

Stochastic Joint Bandwidth and Computational Allocation for ...

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4th IEEE international conference on Smart Cloud (IEEE SmartCloud) / 3rd IEEE international Symposium on Reinforcement Learning (IEEE ISRL)

作者： Lin, Yingxin Feng, Lei Li, Wenjing Zhou, Fanqin Ou, Qinghai Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Fibrlink Commun Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728155050

Mobile-edge computing (MEC) is now regarded as a promising paradigm for cellular networks, which aims at decreasing end-to-end delay and improving the quality of services. This article innovatively envisions a MEC offloading model for multi-users and collaborative MEC servers for 5G D-RANs, where the tasks can be exchanged feasibly between MEC base stations. Firstly, we formulate the joint computational and radio resource management problem to minimize the power consumption of collaborative MEC servers and terminals, which takes into account the constraint of the task buffer stability. And then, an online Lyapunov optimization method is proposed to solve it to get a trade-off between the average weighted power consumption and the execution delay for all tasks distributed in MEC-enabled 5G D-RANs. The numerical results demonstrate that the proposed method has lower power consumption compared with the non-collaborative case. The reason is that the tasks are more reasonably allocated to each collaborative MEC servers with the acceptable execution delay. The control parameter V and collaboration impact factors in the online algorithm are also proved to be efficient and practical to balance the power consumption and execution delay for MEC-enabled 5G D-RANs.

关键词： Mobile edge computing Collaborative MEC server Computational resource management Bandwidth allocation Lyapunov optimization

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Accelerate Deep Learning in IoT: Human-Interaction Co-Inference networking System for Edge

Accelerate Deep Learning in IoT: Human-Interaction Co-Infere...

引用

conference on Human System Interactions, HSI

作者： Chaofeng Zhang Mianxiong Dong Kaoru Ota Advanced Institute of Industrial Technology Tokyo Metropolitan Public University Corporation Tokyo Japan Muroran Institute of Technology Muroran Japan

ISBN: (数字)9781728173924

ISBN: (纸本)9781728173931

As the core technology of the artificial intelligence in the new era, AI technology applied in health care devices has received significant attention. However, due to the limitation of the power supply and computation resource, it is difficult to implement a stable and large AI based human interaction task processing system from the remote edge devices to the centered clouds. In this paper, we propose a holistic network solution that focuses on solving the potential problems of network congestion with the explosive growth of IoT health care devices supported AI inference tasks. First, we propose a multi-hop maximum weight network to describe a DNN inference network based on edge computing. Then, we propose a Maximum Weight Wave propulsion Algorithm (MWWP) algorithm to reduce the overall network latency. Finally, we build up a prototype of a distributed AI inference system and test the computation and transmission performance. Besides, through large-scale experiments, we prove the optimality of our holistic solution.

关键词： Cloud computing Routing Feature extraction Computational modeling Machine learning Task analysis

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SLA-driven Creditable and Negotiable Resource optimized Allocation Scheme in Cloud

SLA-driven Creditable and Negotiable Resource optimized Allo...

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international Wireless Communications and Mobile computing conference, IWCMC

作者： Yu Peng Yan Yong Qiu Haotian Wang Ying Zhou Fanqin Feng Lei Li Wenjing Qiu Xue-Song State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China State Grid Zhejiang Electric Power Company LTD Hangzhou China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

The cloud computing market is dynamic, distributed, and lacks central authorization. In this environment, cloud resource providers are vulnerable to deception and cloud resources may be abused. How to implement efficient and feasible trusted negotiations with users to expand Benefits is an urgent issue. Based on SLA (Service Level Agreement), this paper proposes a trusted negotiation method to optimize cloud resource allocation from the perspective of cloud resource providers. In a nutshell, it firstly quantifies each indicator based on the total amount of cloud resources requested by the user and the corresponding price, the user's comprehensive credit, and the total amount of resources corresponding to each SLA level, then filters the users who meet the requirements. Next knapsack algorithm and the greedy algorithm based on dynamic programming are used to predict the allocation of cloud resources respectively. Finally, the allocated users are negotiated to reach a transaction. This article takes the resource allocation price, negotiated price, and negotiated success rate as the evaluation index. The simulation results show that compared with the greedy algorithm, the algorithm in this paper has higher resource allocation price, negotiated price and negotiated success rate under different numbers of users, and can effectively realize the optimal allocation of cloud resources.

关键词： Cloud computing Resource management Switches Telecommunications Computer architecture Computational modeling Dynamic scheduling

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GGS: General Gradient Sparsification for Federated Learning in Edge computing*

GGS: General Gradient Sparsification for Federated Learning ...

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IEEE international conference on Communications (ICC)

作者： Shiqi Li Qi Qi Jingyu Wang Haifeng Sun Yujian Li F. Richard Yu The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China The Department of Systems and Computer Engineering Carleton University Canada

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Federated learning is an emerging concept that trains the machine learning models with the distributed datasets, without sending the raw data to the data center. But in an edge computing enviroment where the wireless network resource is constrained, the key problem of federated learning is the communication overhead for parameters synchronization, which wastes bandwidth, increases training time, and even impacts the model accuracy. Gradient sparsification has received increasing attention, which only updates significant gradients and accumulates insignificant gradients locally. However, how to preserve the accuracy after a high ratio sparsification has always been ignored. In this paper, a General Gradient Sparsification (GGS) framework is proposed for adaptive optimizers, to correct the sparse gradient update process. It consists of two important mechanisms: gradient correction and batch normalization update with local gradients (BN-LG). With gradient correction, the optimizer can properly treat the accumulated insignificant gradients, which makes the model converge better. Furthermore, updating the batch normalization layer with local gradients can relieve the impact of delayed gradients without increasing the communication overhead. We have conducted experiments on LeNet-5, CifarNet, DenseNet-121, and AlexNet with adaptive optimizers. Results show that when 99.9% gradients are sparsified, validation datasets are maintained with top-l accuracy.

关键词： Training Edge computing Computational modeling Servers Convergence Machine learning Adaptation models

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Dynamic Resource Allocation of Switched Ethernet Networks in Embedded Real-Time Systems 6th

Dynamic Resource Allocation of Switched Ethernet Networks in...

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6th international conference on Information Technology in Biomedicine (ITIB)

作者： Schmidt, Michael Obermaisser, Roman Wurmbach, Christian Univ Siegen Chair Embedded Syst Holderlinstr 3 D-57068 Siegen Germany

ISBN: (纸本)9783319912110

Manual reconfiguration of a distributed real-time communication system by humans is error-prone and time consuming. To avoid errors, which may result in latter failures of the system, the reconfiguration of the communication resources should be carried out automatically by the system itself. This paper introduces a new broker model which enables the system to respond dynamically to changes in the system composition, like newly added network nodes or services. Further, we introduce a new client model that implements protocols for the agreement and coordination of the reconfiguration phases of the network nodes. Besides the support for ARINC 615A-3 (ARINC615A), the client model also provides a new way for data loading for devices that are not compatible with this standard. Our introduced models are finally implemented and evaluated within an experimental setup for TTEthernet. The main contribution of our approach is to adapt to both changes within the system's service compositions (e.g., new services, changed timing requirements) and changes within the network composition (e.g., newly added switches or nodes).

关键词： Ambient-Assisted Living (AAL) Dynamic resource allocation TTEthernet Time Sensitive networking (TSN) Real-time communication

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