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Energy-efficient solution using stochastic approach for IoT-Fog-Cloud computing 15

Energy-efficient solution using stochastic approach for IoT-...

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15th international conference on Wireless and Mobile computing, networking and Communications (WiMob)

作者： Mebrek, Adila Merghem-Boulahia, Leila Esseghir, Moez Univ Technol Troyes CNRS UMR 6281 ERAICD 12 Rue Marie CurieCS 42060 F-10004 Troyes France

ISBN: (纸本)9781728133164

Fog computing is a distributed architecture which extends cloud computing resources to the end user. Furthermore, fog computing is deployed increasingly to fulfill the requirements of the Internet of Object (IoT) technology. This solution has been proposed to overcome the shortcomings of the cloud. Motivated by recent advances in fog computing, we aim to improve the performances of fog-cloud systems. A novel system design is proposed in which the energy consumption and the delay are considered. Different queue models are applied to our system, where the energy and delay costs are modeled. A coalition game between fog nodes is formulated to minimize the cost, while examining the energy-delay trade-off. To this end, we propose three energy-efficient solutions with the objective of minimizing the total cost of the execution of IoT applications in a fog-cloud system. Our simulation shows the effectiveness of the proposed solutions compared to works from the literature.

关键词： Fog computing Cloud computing Internet of things (IoT) Service latency Energy consumption Queuing model distributed algorithm

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EPIC Collab: Supporting Asynchronous Collaboration in Big Data Analysis Systems

EPIC Collab: Supporting Asynchronous Collaboration in Big Da...

引用

IEEE international conference on Cloud computing and Big Data Analysis (ICCCBDA)

作者： Rsha Mirza Kenneth M. Anderson Stephen Voida Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia University of Colorado Boulder Boulder USA

The rise of big data has led to the creation of large datasets that require teams to collaborate to analyze data effectively. Unfortunately, the software systems that collect and analyze large datasets are not often designed to support this kind of collaboration. Accordingly, our work investigates issues related to supporting collaboration in big data analysis systems. We use the domain of crisis informatics and the software infrastructure of Project EPIC as a case study to gain insight into the features that analysts need to effectively perform analysis at scale. This paper focuses on supporting asynchronous collaboration among analysts who work in small distributed teams on big data software systems. It describes the challenges faced by researchers who work collaboratively to analyze large crisis datasets (consisting, typically, of Twitter data). It then describes the work performed to redesign an existing big data analysis environment to substantially improve its support for collaboration. The impact of this research lies in its ability to improve the work of similar teams performing large-scale data analysis. While our work is based on insights gleaned from crisis informatics, we believe that our design, results, and lessons learned are broadly applicable to other application domains.

关键词： Cloud computing Data analysis Social networking (online) conferences Collaboration Big Data Software systems

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Accelerate Deep Learning in IoT: Human-Interaction Co-Inference networking System for Edge 13

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

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13th IEEE international conference on Human System Interaction (IEEE HSI)

作者： Zhang, Chaofeng Dong, Mianxiong Ota, Kaoru Tokyo Metropolitan Publ Univ Corp Adv Inst Ind Technol Tokyo Japan Muroran Inst Technol Dept Informat & Elect Engn Muroran Hokkaido Japan

ISBN: (纸本)9781728173924

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： Health care

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cuTS: Scaling Subgraph Isomorphism on distributed Multi-GPU Systems Using Trie Based Data Structure

cuTS: Scaling Subgraph Isomorphism on Distributed Multi-GPU ...

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Supercomputing conference

作者： Lizhi Xiang Arif Khan Edoardo Serra Mahantesh Halappanavar Aravind Sukumaran-Rajam Washington State University Pullman Washington USA Pacific Northwest National Lab Richland Washington USA Boise State University PNNL Boise Idaho USA

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

Subgraph isomorphism is a pattern-matching algorithm widely used in many domains such as chem-informatics, bioinformatics, databases, and social network analysis. It is computationally expensive and is a proven NP-hard problem. The massive parallelism in GPUs is well suited for solving subgraph isomorphism. However, current GPU implementations are far from the achievable performance. Moreover, the enormous memory requirement of current approaches limits the problem size that can be handled. This work analyzes the fundamental challenges associated with processing subgraph isomorphism on GPUs and develops an efficient GPU implementation. We also develop a GPU-friendly trie-based data structure to drastically reduce the intermediate storage space requirement, enabling large benchmarks to be processed. We also develop the first distributed sub-graph isomorphism algorithm for GPUs. Our experimental evaluation demonstrates the efficacy of our approach by comparing the execution time and number of cases that can be handled against the state-of-the-art GPU implementations.

关键词： Social networking (online) NP-hard problem Databases Scalability High performance computing Memory management Graphics processing units

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distributed Task Offloading based on Multi-Agent Deep Reinforcement Learning

Distributed Task Offloading based on Multi-Agent Deep Reinfo...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Shucheng Hu Tao Ren Jianwei Niu Zheyuan Hu Guoliang Xing State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China The Chinese University of Hong Kong HKSAR China

ISBN: (纸本)9781665406697

Recent years have witnessed the increasing popularity of mobile applications, e.g., virtual reality, unmanned driving, which are generally computation-intensive and latency-sensitive, posing a major challenge for resource-limited user equipment (UE). Mobile edge computing (MEC) has been proposed as a promising approach to alleviate the problem, by offloading mobile tasks to the edge server (ES) deployed in close proximity to UE. However, most existing task offloading algorithms are primarily based on centralized scheduling, which could suffer from the ‘curse of dimensionality’ in large MEC environments. To address this issue, this paper proposes a fully distributed task offloading approach based on multi-agent deep reinforcement learning, whose critic and actor neural networks are trained under the assistance of global and local network states, respectively. In addition, we design a model parameter aggregation mechanism, along with a normalized fine-tuned reward function, to further improve the learning efficiency of the training process. Simulation results show that our proposed approach could achieve substantial performance improvements over baseline approaches.

关键词： Training Solid modeling Processor scheduling Simulation Reinforcement learning Virtual reality Scheduling

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AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data

AgEBO-Tabular: Joint Neural Architecture and Hyperparameter ...

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Supercomputing conference

作者： Romain Égelé Prasanna Balaprakash Isabelle Guyon Venkatram Vishwanath Fangfang Xia Rick Stevens Zhengying Liu École polytechnique Palaiseau France Argonne National Laboratory Lemont Illinois USA CNRS/Inria-LISN U. Paris-Saclay France

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

Developing high-performing predictive models for large tabular data sets is a challenging task. Neural architecture search (NAS) is an AutoML approach that generates and evaluates multiple neural networks with different architectures concurrently to automatically discover an high performing model. A key issue in NAS, particularly for large data sets, is the large computation time required to evaluate each generated architecture. While data-parallel training has the potential to address this issue, a straightforward approach can result in significant loss of accuracy. To that end, we develop AgEBO-Tabular, which combines Aging Evolution (AE) to search over neural architectures and asynchronous Bayesian optimization (BO) to search over hyperparameters to adapt data-parallel training. We evaluate the efficacy of our approach on two large predictive modeling tabular data sets from the Exascale computing Project-CANcer distributed Learning Environment (ECP-CANDLE).

关键词： Training Exascale computing Distance learning Computational modeling Neural networks distributed databases Computer architecture

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Efficient Dual Batch Size Deep Learning for distributed Parameter Server Systems

Efficient Dual Batch Size Deep Learning for Distributed Para...

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IEEE Annual international Computer Software and Applications conference (COMPSAC)

作者： Kuan-Wei Lu Pangfeng Liu Ding-Yong Hong Jan-Jan Wu Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Academia Sinica Institute of Information Science Taipei Taiwan

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

distributed machine learning is essential for applying deep learning models with many data and parameters. Current researches on distributed machine learning focus on using more hardware devices powerful computing units for fast training. Consequently, the model training prefers a larger batch size to accelerate the training speed. However, the large batch training often suffers from poor accuracy due to poor generalization ability. Researchers have come up with many sophisticated methods to address this accuracy issue due to large batch sizes. These methods usually have complex mechanisms, thus making training more difficult. In addition, powerful training hardware for large batch sizes is expensive, and not all researchers can afford it. We propose a dual batch size learning scheme to address the batch size issue. We use the maximum batch size of our hardware for maximum training efficiency we can afford. In addition, we introduce a smaller batch size during the training to improve the model generalization ability. Using two different batch sizes in the same training simultaneously will reduce the testing loss and obtain a good generalization ability, with only a slight increase in the training time. We implement our dual batch size learning scheme and conduct experiments. By increasing 5% of the training time, we can reduce the loss from 1.429 to 1.246 in some cases. In addition, by appropriately adjusting the percentage of large and small batch sizes, we can increase the accuracy by 2.8% in some cases. With the additional 10% increase in training time, we can reduce the loss from 1.429 to 1.193. And after moderately adjusting the number of large batches and small batches used by GPUs, the accuracy can increase by 2.9%. Using two different batch sizes in the same training introduces two complications. First, the data processing speeds for two different batch sizes are different, so we must assign the data proportionally to maximize the overall processing speed. I

关键词： Training Deep learning Computational modeling Neural networks Predictive models Data models Hardware

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Comparative Analysis of the Efficiency of Modern Fake Detection Algorithms in Scope of Information Warfare

Comparative Analysis of the Efficiency of Modern Fake Detect...

引用

IEEE international Workshop on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications

作者： Yevhenii Shtefaniuk Ivan Opirskyy Lviv Polytechnic National University Lviv Lviv Oblast

ISBN: (纸本)9781665426060

The problem of detecting fake (false) information passing through various channels of the Internet is becoming increasingly important task. One of the most dangerous types of such information is targeted propaganda, which always has a specific purpose and uses specially prepared resources. To protect a person from such informational influence, we can use already developed tools and algorithms. This article considers the main features of information propaganda and approaches to combating it; the effectiveness of several well-known modern algorithms for recognizing fake news; the comparative efficiency analysis of these algorithms in the context of possibility of their application for counteracting purposeful information influences is carried out. Based on the study, the most promising algorithm for recognizing information propaganda in social networks was selected. This will allow to build more efficient systems of detecting fake news that are delivered in scope of information propaganda campaigns as a part of information warfare in social media networks.

关键词： Training Social networking (online) distributed databases Manuals Feature extraction Internet Reliability

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A Survey of Blockchain-based Cybersecurity for Vehicular Networks 16

A Survey of Blockchain-based Cybersecurity for Vehicular Net...

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16th IEEE international Wireless Communications and Mobile computing conference, IWCMC 2020

作者： Wang, Xifeng Xu, Changqiao Zhou, Zan Yang, Shujie Sun, Limin State Key Laboratory of Networking and Switching Technology Bupt Beijing China Beijing Key Laboratory of IoT Information Security Cas Beijing China

ISBN: (纸本)9781728131290

The development of vehicular networks has greatly improved the efficiency and safety of intelligent traffic systems. However, it also introduces additional security threats into the system. The special characteristics of vehicular networks, such as dynamic topology, huge network scale and so on, make it difficult to adopt the traditional security solutions directly into this scenario. In addition, the single point failure problem of the centralized security mechanisms is also a big challenge. Recently, blockchain technology, which is a distributed database, is a potential approach to address these security issues. In this paper, a comprehensive review of the existing blockchain-based cybersecurity mechanisms is presented with the corresponding performance analysis. The purpose of this work is to provide a guideline for the further study in the application of blockchain in the vehicular network security area. © 2020 IEEE.

关键词： Cybersecurity

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Effects of Different Recommendation Algorithms on Structure of Social Networks

Effects of Different Recommendation Algorithms on Structure ...

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international conference on Computational Science and Computational Intelligence (CSCI)

作者： Sepideh Banihashemi Abdolreza Abhari Department of Computer Science Distributed Systems and Multimedia Processing Laboratory (DSMP lab) Ryerson University Toronto Canada

ISBN: (纸本)9781665458429

In this paper, the effects of two algorithms (i.e., K-means clustering, Cosine similarity) on the structure of a social network (i.e., Twitter, scientific research papers) is studied to examine the formation of communities when the users follow the recommendations provided by a simulator. The relationship among the users can be either follower-followee in a Twitter dataset or a paper-publication venue in a scientific research paper dataset. The purpose is to evaluate how detected communities and the resulted network graph differ when following the recommendations provided by the employed algorithms after the system recommends the top-N recommendations for a selected user.

关键词： Social networking (online) Scientific computing Blogs Clustering algorithms Recommender systems Computational intelligence

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