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Dispersion of Mobile Robots in the Global Communication Model 20

Dispersion of Mobile Robots in the Global Communication Mode...

引用

21st international conference on distributed computing and networking (ICDCN)

作者： Kshemkalyani, Ajay D. Molla, Anisur Rahaman Sharma, Gokarna Univ Illinois Chicago IL 60607 USA Indian Stat Inst Kolkata 700108 India Kent State Univ Kent OH 44240 USA

ISBN: (纸本)9781450377515

The dispersion problem on graphs asks k <= n robots placed initially arbitrarily on the nodes of an n-node anonymous graph to reposition autonomously to reach a configuration in which each robot is on a distinct node of the graph. This problem is of significant interest due to its relationship to other fundamental robot coordination problems, such as exploration, scattering, load balancing, and relocation of self-driven electric cars (robots) to recharge stations (nodes). In this paper, we consider dispersion in the global communication model where a robot can communicate with any other robot in the graph (but the graph is unknown to robots). We provide three novel deterministic algorithms, two for arbitrary graphs and one for arbitrary trees, in a synchronous setting where all robots perform their actions in every time step. For arbitrary graphs, our first algorithm is based on a DFS traversal and guarantees O(min(m, k Delta)) steps runtime using circle minus(log(max(k, Delta))) bits at each robot, where m is the number of edges and Lambda is the maximum degree of the graph. The second algorithm for arbitrary graphs is based on a BFS traversal and guarantees O(max(D, k)Delta(D + Delta)) steps runtime using O(max(D, Delta log k)) bits at each robot, where D is the diameter of the graph. The algorithm for arbitrary trees is also based on a BFS travesal and guarantees O(D max(D, k)) steps runtime using O(max(D, Delta log k)) bits at each robot. Our results are significant improvements compared to the existing results established in the local communication model where a robot can communication only with other robots present at the same node.

关键词： Multi-agent systems Mobile robots Dispersion Collective exploration Scattering Uniform deployment Load balancing distributed algorithms Time and memory complexity

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distributed Task Offloading and Resource Allocation in Vehicular Edge computing 1

Distributed Task Offloading and Resource Allocation in Vehic...

引用

IEEE international conference on Space-Air-Ground computing (IEEE SAGC)

作者： Li, Shichao Chen, Hongbin Lin, Siyu Zhang, Ning Guilin Univ Elect Technol Guangxi Key Lab Wireless Broadband Commun & Signa Guilin 541004 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781665419406

With the powerful storage and computation capability, vehicular edge computing (VEC) is considered as a promising paradigm to enhance the safety and service quality of vehicles in intelligent transportation systems (ITS). In this paper, we formulate a joint road side units (RSUs) selection and resource allocation problem, which minimizes the total task offloading delay subject to the bandwidth and computation resources constraints in VEC system. Considering the formulated problem is a mixed-integer nonlinear programming (MINLP) problem, the original problem is reformulated as a convex problem. Due to the high complexity of the problem, we decompose it into a distributed manner. By utilizing the alternating direction method of multipliers (ADMM), a joint RSUs selection and resource allocation (JRSRA) algorithm is proposed with low complexity. Simulation results shows that the proposed JRSRA algorithm can reduce the total task offloading delay compared with other benchmark methods.

关键词： Resource allocation

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A Study on Online Shopping Behaviour for Apparel

A Study on Online Shopping Behaviour for Apparel

引用

Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Nikhil Goyal Yuvraj Singh Rahul Shukla Satyajee Srivastava School of Computer Science & Engg. Galgotias University U. P. India

ISBN: (纸本)9781665438124

In Today's Life, Everyone wants to wear the bright and trendy clothes of their own choice but while shopping the clothes it becomes very tedious to select clothes when many choices are available and we don't know anything about clothes to which area it's belong. We all know that Online Shopping is very much developed in India. After analyze the present India in the shopper's scenario, according to estimates by experts, to that e-shopping, the trend is going to be the most important event for us, the consumers in the years to come. The main focus is on the five-line factors include: financial risk, performance risk, delivery risk, trust and security, and web design. The empirical data were collected through a questionnaire distributed online and on the internet, including a 100-in. The results of this study showed that the design is of the most influential and important factor. Even though the performance of the product, and the reliability and protection of the risks of having a significant impact on the behavior of the network, this study shows that other factors, such as financial risk and delivery risk-are not to have a significant impact on consumers ' behavior on the internet. Over the past two years, there has been a significant growth in online clothing sales. The aim of this study is to analyze the trends in online shopping in India, consumers in the apparel segment.

关键词： Web design Clothing distributed databases Market research Safety Internet Electronic commerce

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Welcome Message from ICDCN 2021 conference Chairs

ACM International Conference Proceeding Series

引用

ACM international conference Proceeding Series 2021年 III-IV页

作者： Yamaguchi, Hirozumi Pandurangan, Gopal Osaka University Japan University of Houston United States

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Dual-Way Gradient Sparsification for Asynchronous distributed Deep Learning 20

Dual-Way Gradient Sparsification for Asynchronous Distribute...

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49th international conference on Parallel Processing (ICPP)

作者： Yan, Zijie Xiao, Danyang Chen, Mengqiang Zhou, Jieying Wu, Weigang Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9781450388160

distributed parallel training using computing clusters is desirable for large scale deep neural networks. One of the key challenges in distributed training is the communication cost for exchanging information, such as stochastic gradients, among training nodes. Recently, gradient sparsification techniques have been proposed to reduce the amount of data exchanged and thus alleviate the network overhead. However, most existing gradient sparsification approaches consider only synchronous parallelism and cannot be applied in asynchronous distributed training. In this paper, we present the dual-way gradient sparsification (DGS) approach for asynchronous distributed training. Different from existing approaches, where workers download the global model from parameter server, our approach lets workers download model difference from the parameter server. With such design, both the gradients sent to server and the model parameters downloaded from server can be sparsified and the dual-way communication cost between server and workers can be significantly reduced. To preserve accuracy under the dual-way sparsification, we design a new momentum, SAMomentum, which is sparsification aware and offers significant optimization boost. Extensive experiments with different scales on ImageNet and CIFAR-10 show that, (1) compared with similar works, DGS with SAMomentum consistently achieves better performance;(2) DGS improves the scalability of asynchronous training, especially with limited networking infrastructure.

关键词： Gradient Sparification Deep Learning distributed Deep Learning Asynchronous Training Asynchronous Gradient Descent

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39th IEEE international conference on Computer Communications (IEEE INFOCOM)

作者： Amadeo, Marica Ruggeri, Giuseppe Campolo, Claudia Molinaro, Antonella Mangiullo, Giuseppe Univ Mediterranea Reggio Calabria Reggio Di Calabria Italy

ISBN: (纸本)9781728186955

Among the distinctive features of the Named Data networking (NDN) paradigm, in-network caching plays a crucial role in improving data delivery performance. At the network edge, the benefits of in-network caching can be remarkable in terms of reduced network traffic and user-perceived access latency. Multiple strategies have been designed for caching static Internet contents in NDN routers, while less attention has been devoted to transient Internet of Things (IoT) data, which expire after a certain amount of time. In this work, we introduce a novel distributed and autonomous caching strategy where NDN nodes take decisions by considering the popularity of IoT data and their lifetime. The target is to cache the most popular data with the highest residual lifetime in order to maximize the cache hit ratio at the edge. Performance evaluation assessed with the ndnSIM network simulator shows that the proposed solution outperforms existing schemes available in the literature by guaranteeing, among others, the highest cache hit ratio and the shortest content retrieval time.

关键词： Named Data networking Information Centric networking Caching Internet of Things Edge computing

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A distributed Reinforcement Learning Approach for Energy and Congestion-Aware Edge Networks 20

A Distributed Reinforcement Learning Approach for Energy and...

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16th international conference on Emerging networking Experiments and Technologies

作者： Sacco, Alessio Esposito, Flavio Marchetto, Guido Politecn Torino Turin Italy St Louis Univ St Louis MO 63103 USA

ISBN: (纸本)9781450379489

The abiding attempt of automation has also pervaded computer networks, with the ability to measure, analyze, and control themselves in an automated manner, by reacting to changes in the environment (e.g., demand) while exploiting existing flexibilities. When provided with these features, networks are often referred to as lself-drivingz. Network virtualization and machine learning are the drivers. In this regard, the provision and orchestration of physical or virtual resources are crucial for both Quality of Service guarantees and cost management in the edge/cloud computing ecosystem. Auto-scaling mechanisms are hence essential to effectively manage the lifecycle of network resources. In this poster, we propose Relevant, a distributed reinforcement learning approach to enable distributed automation for network orchestrators. Our solution aims at solving the congestion control problem within Software-Defined Network infrastructures, while being mindful of the energy consumption, helping resources to scale up and down as traffic demands fluctuate and energy optimization opportunities arise.

关键词： reinforcement learning self-driving networks auto-scaling

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Detection of Faulty Nodes in distributed Environment using Machine Learning

Detection of Faulty Nodes in Distributed Environment using M...

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Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Reshma S. Gaykar V. Khanaa S. D. Joshi Bharath Institute of Higher Education and Research Chennai India Information Center Bharath Institute of Higher Education and Research Chennai India Faculty of Engineering and Technology Bharati Vidyapeeth University Pune India

ISBN: (纸本)9781665438124

Faulty nodes are expected to have a significant effect on the performance of Big Data systems. The cause of discharges, on the other hand, is a complex issue. The previous work has mostly focused on identifying Stragglers, optimizing the quality of programming, and root cause investigation. These methods can't offer useful information unless they can assist users automate their services. We are developing a framework that determines when there will be stragglers and must identify gaps to prevent those circumstances. This research proposed a dynamic log collection of heterogeneous virtual machine data and detect the faulty node based on execution performance using various parameters. We introduce multi-task learning mechanisms in this paper, which exchange knowledge between the different versions of nodes. The system first collects log data from available data nodes and applies an unsupervised machine learning technique to detect the straggler. Moreover, once a straggler has been detected by the learning algorithm, it tries mitigating such nodes instead of eliminating those nodes. The major benefit of this research is that the system can handle massive data with minimum data nodes or even system efficiently work with already heated nodes in a distributed environment. Our compositions absorb the shared context in our data, enhancing sweeping statement efficiency on minimal data, unlike naive inter learning frameworks.

关键词： Heating systems distributed databases Machine learning Big Data Programming Prediction algorithms Multitasking

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A Survey on Privacy Approaches for Social Networks

A Survey on Privacy Approaches for Social Networks

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Regina Marin Luciano Gallegos Instituto Federal Goiano (IFGoiano) Ceres Brazil Data Science and Artificial Intelligence Laboratory University of Fortaleza (UNIFOR) Fortaleza Brazil

The popular use of Social Networks (SNs) for online messaging and information exchange provided available online massive amount of personal data. In general, social interactions require information sharing between involved parties, which implies knowing with whom, and to which extent the information is shared. In this paper, we provide a comprehensive overview of SNs state-of-the-art by exploring users’ privacy in SNs implementations from centralized to fully decentralized architecture. To clarify whether existing techniques can help to prevent attacks or not, we survey and compare how privacy policies are specified and operationalized into the SN design. The result of our in-depth analysis suggests that currently privacy enforcement mechanisms are not satisfactory in SNs because they produce unbalanced control of the shared information.

关键词： Privacy Data privacy Social networking (online) Information sharing Information exchange

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Edge-Based Sampling for Complex Network with Self-Similar Structure

Edge-Based Sampling for Complex Network with Self-Similar St...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Wenting Wang Xianghua Fu Xinze Lin Shenzhen Technology University Shenzhen China

The network with self-similarity is an essential data structure widely used in social networks and bioinformatics. The network sampling as an important method for subgraph mining has attracted attention in many applications. However, traditional methods are computationally complexity and time- consuming. Due to the increase of the data scale, the algorithms’ runtime grows exponentially. A new methodology needs to handle the computational complexity challenge from large-scale graph datasets. In this paper, we develop a series of edge-based sampling algorithms: HEPS-1 and HEPS-2. We investigate the relations between edges and their edge neighbours caused by the self- similarity. Then, we determine the edges for the subgraph. In this way, we sample the networks while keeping the original statistical characters. The computational complexity is low. The effectiveness of sampling is enhanced.

关键词： Runtime Social networking (online) Complex networks Data structures Data mining Computational complexity Bioinformatics

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