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Energy optimization-based optimal trade-off scheme for job scheduling in fog computing 8

Energy optimization-based optimal trade-off scheme for job s...

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Arri, Harwant Singh Singh, Ramandeep Lovely Professional University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9789380544434

Fog computing is designed to expand the cloud-based storage, computing, and networking features that involve the concept of data processing on the end devices of the network and not in the cloud. In this article, an energy optimization based optimal tradeoff scheme for job scheduling in fog computing platforms using Artificial Bee Colony (ABC) as an optimization technique with Artificial Neural Network (ANN) is proposed. this research is designed to solve the challenges faced by researchers in the field of fog computing during job scheduling in terms of energy consumption, job completion time and successfully task allocation to a Virtual Machine (VM). the experimental results demonstrate that the proposed scheme using ABC with ANN achieved a far better result as compare to the other existing work based on the job completion time. Where we find out the task completion time is reduced by 40.67% as compare to the work with a Genetic Algorithm based model. Also, the successful task allocation and energy computing rate are improved by using the concept of ABC-ANN and other Qualities of Service (QoS) parameters are also contrasted in terms of task completion time and successfully task allocation. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Fog computing

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Uniform Resource Clustering for Service Function Chaining in NFV-based Cloud Environment

Uniform Resource Clustering for Service Function Chaining in...

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international conference for Convergence of Technology (I2CT)

作者： Anshul Mishra Amruta Ayachit Shivani Halemani Sparsha Shetty Narayan D G Somashekar Patil School of Computer Science and Engineering KLE Technological University Hubballi India

Network function virtualization (NFV) and software-defined networking (SDN) have had a significant positive influence on the development of technology that mostly resolves the problems with conventional network management systems. Hypervisors enable the virtualization of numerous network functions and services. Virtualized network services such as firewall, load balancer, and network address translator (NAT) along with others can be chained together to form a service chain. A data packet can go through the service chain as per its defined policy and not every available service. Such service function chaining (SFC) policies can be hosted at end devices to conserve a significant amount of energy and time. In this work, we have proposed an approach in which the SFC policies use an adequate amount of network parameters and a technique that uses uniform resource clustering (URC). Two Parameters namely bandwidth and CPU utilization are considered. Our findings indicate that the URC approach saves energy and time significantly. the average network service time with our approach is 9% less than the default approach.

关键词： Cloud computing Virtual machine monitors Service function chaining Firewalls (computing) Bandwidth Network function virtualization Virtualization

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SpecTextor: End-to-End Attention-based Mechanism for Dense Text Generation in Sports Journalism 8

SpecTextor: End-to-End Attention-based Mechanism for Dense T...

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8th IEEE international conference on Smart computing (SMARTCOMP)

作者： Ghosh, Indrajeet Ivler, Matthew Ramamurthy, Sreenivasan Ramasamy Roy, Nirmalya Univ Maryland Baltimore Cty Mobile Pervas & Sensor Comp Lab Baltimore MD 21228 USA Univ Maryland Baltimore Cty Ctr Real Time Distributed Sensing & Auton Cards Baltimore MD 21228 USA Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD 21228 USA Pomona Coll Dept Comp Sci Claremont CA 91711 USA

ISBN: (纸本)9781665481526

Language-guided smart systems can help to design next-generation human-machine interactive applications. the dense text description is one of the research areas where systems learn the semantic knowledge and visual features of each video frame and map them to describe the video's most relevant subjects and events. In this paper, we consider untrimmed sports videos as our case study. Generating dense descriptions in the sports domain to supplement journalistic works without relying on commentators and experts requires more investigation. Motivated by this, we propose an end-to-end automated text-generator, SpecTextor, that learns the semantic features from untrimmed videos of sports games and generates associated descriptive texts. the proposed approach considers the video as a sequence of frames and sequentially generates words. After splitting videos into frames, we use a pre-trained VGG-16 model for feature extraction and encoding the video frames. With these encoded frames, we posit a Long Short-Term Memory (LSTM) based attention-decoder pipeline that leverages soft-attention mechanism to map the semantic features with relevant textual descriptions to generate the explanation of the game. Because developing a comprehensive description of the game warrants training on a set of dense time-stamped captions, we leverage two available public datasets: ActivityNet Captions and Microsoft Video Description. In addition, we utilized two different decoding algorithms: beam search and greedy search and computed two evaluation metrics: BLEU and METEOR scores.

关键词： Sports Journalism Semantic Knowledge LSTM Soft-Attention Mechanism Beam Search Greedy Search Human-Machine Interactive Applications

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Multi-Agent Trust Evaluation Model based on Reinforcement Learning 8

Multi-Agent Trust Evaluation Model based on Reinforcement Le...

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8th international conference on Dependable Systems and their Applications, DSA 2021

作者： Haoran, Jia Yuyu, Yuan Qiang, Han Pengqian, Zhao Ting, Guo Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781665443913

the establishment of trust between agents is an important part of Multi-agent Reinforcement Learning (MARL). Recent studies on multi-agents have paid more attention to the game relationship between agents, ignoring trust is the basis of cooperation among agents. In this article, we propose a trust evaluation model in MARL to evaluate the trustworthiness of the target agent. the trust evaluation of the target agent is composed of direct trust and reputation. Direct trust is obtained from the historical interaction of agents. Reputation is to evaluate the impact of an agent's behavior by counterfactual action of a high-ranking agent. In addition, this paper also proposes a trust-based reward mechanism in MARL, which divides agent rewards into environmental rewards and trust rewards. Trust rewards are obtained through trust evaluation values, which drive the agents to reach a cooperative state and obtain higher long-term rewards. In this paper, we set up experiments in harvest game to verify that the trust evaluation model can effectively promote the cooperation of agents and avoid the problem of agents facing social dilemmas. © 2021 IEEE.

关键词： Reinforcement learning

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DACTI: a Generation-Based Data Augmentation Method for Cyber threat Intelligence

DACTI: a Generation-Based Data Augmentation Method for Cyber...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Jingbing Chen Yali Gao Xiaoyong Li Key Lab of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

Methods of data augmentation are extensively utilized to boost the efficacy of machine learning, given their superior capability in promoting generalization performance. In this paper, we address the issue of data scarcity in the field of cyber threat intelligence and propose a method for generating high-quality synthetic data by training a language model on linearized labeled phrases. Subsequently, we design a relational classification model to classify relational entities related to cyber threat intelligence in the created synthetic data. We conduct extensive experiments on datasets of different sizes, and the experimental results show that our proposed method outperforms the baseline methods, especially when the amount of gold data provided is insufficient, and the $F_th$ score of our model is improved by up to 1.94 points.

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LR-SGD: Layer-based Random SGD For distributed Deep Learning 22

LR-SGD: Layer-based Random SGD For Distributed Deep Learning

引用

8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Zhang, Zhongyu Hu, Yanqing Ye, Qing Sichuan University Chengdu China

ISBN: (纸本)9781450396110

distributed training is an effective way to accelerate the training of a large-scale deep neural network(DNN) model, while the data communication between computational workers is always a bottleneck because of the frequent parameter communication at each iteration. To alleviate this problem, many communication-efficient distributed training algorithms based on synchronized stochastic gradient descent(SSGD) are proposed to reduce the amount of the exchanged data by compressing or quantizing the gradients such as the top-k SGD, TernGrad. However, these methods introduce extra intensive computation because of the frequent operation on tensor and memory accessing. Especially when the faster communication backends such as nccl(i.e, NVIDIA Collective Communications Library) or faster communication hardware is proffered in the near future, they no longer have a time advantage. In this paper, we propose an efficient sparsification method, layer-based random SGD (LR-SGD), that randomly select a certain number of layers of the DNN model to be exchanged instead of some elements of each tensor, which reduces communication while keep the performance close to the SSGD. Particularly, we use a hyper parameter k (i.e., indicates the number of layers to be selected) to adjust the compressing ratio while two probabilistic models are utilized to select the layers being exchanged. To validate the proposed method, we conduct several experiments on different datasets with two different scale DNN models on a stimulative cluster. the results demonstrate that the layer-based random sparsification method effectively reduce the communication overhead while maintain the high accuracy. Our code is available at https://***/prototype-zzy/LR-SGD. © 2022 ACM.

关键词： Neural network models

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Real-time pricing method through market auction based on multi-agent reinforcement learning

Real-time pricing method through market auction based on mul...

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IEEE international conference on Power and Power and Renewable Energy (ICPRE)

作者： Peng Hong Shuping He Xiaohan Fang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

With the development of distributed energy systems, pricing different energy sources in microgrids has become a significant challenge. To solve this problem, this paper proposes an real-time pricing method through market auction based on multi-agent reinforcement learning. the energy supply side provides different energy production and quotations, while consumers on the energy demand side freely quote and purchase the required energy in the energy market based on their own needs. the final transaction price of different energy sources is determined by the quotes of the buyer and seller who have reached the transaction. the simulation results indicate that the proposed pricing method have a good performance.

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Stackelberg Game Model for Optimal Dispatching of Electricity Consumption for Community Microgrid Considering Demand Response Mechanism

Stackelberg Game Model for Optimal Dispatching of Electricit...

引用

IEEE international conference on Power and Power and Renewable Energy (ICPRE)

作者： Di Tan Xiaohan Fang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

Community microgrid is a kind of new power system that acts as an intermediary between community consumers and the grid, playing an important role in realising distributed autonomy and improving the power supply level. To achieve optimal power dispatch in community microgrid, this paper establishes a Stackelberg game model with the community microgrid operator (CMO) as the leader and the consumers aggregator as the follower, and combines the internal pricing method with the demand response mechanism. the paper uses a combination of genetic algorithm and solver to solve the Stackelberg game model to maximise the interests of all parties. Simulation results show that the model can secure the interests of each game subject, balance power supply and demand, and promote peak shaving and valley filling of electrical loads.

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Intelligent Temperature Control System Based on Cloud Platform

Intelligent Temperature Control System Based on Cloud Platfo...

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Advanced Algorithms and Control Engineering (ICAACE), international conference on

作者： Xiaowei Zhao Dongyang Pan Chong Luo Jianhua Zhang School of Information Engineering Hainan Vocational University of Science and Technology Haikou China Department of Physics Hainan University Haikou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

the design of this project starts from the Internet of things technology, integrating short-range wireless communication with network wireless communication technology. Starting from sensor technology, wireless communication technology, and cloud platform technology, an intelligent temperature control system based on cloud platform is proposed. the system consists of an indoor temperature control terminal and an outdoor window household terminal. the two ends are wireless networked through ZigBee modules, and the temperature control terminal is network through WiFi modules to achieve single device networking and control of non networked devices. CC2530 is used as the main control chip at both ends to control temperature monitoring, smoke alarm, gateway control mode, automatic control, device linkage, data cloud command issuance, and other functions.

关键词： Wireless communication Couplings Temperature sensors Temperature measurement Cloud computing Zigbee Logic gates Temperature control Internet of things Monitoring

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Leveraging Synergies Between AI and networking to Build Next Generation Edge Networks

Leveraging Synergies Between AI and Networking to Build Next...

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

作者： Sen Lin Ming Shi Anish Arora Raef Bassily Elisa Bertino Constantine Caramanis Kaushik Chowdhury Eylem Ekici Atilla Eryilmaz Stratis Ioannidis Nan Jiang Gauri Joshi Jim Kurose Yingbin Liang Zhiqiang Lin Jia Liu Mingyan Liu Tommaso Melodia Aryan Mokhtari Rob Nowak Sewoong Oh Srini Parthasarathy Chunyi Peng Hulya Seferoglu Ness Shroff Sanjay Shakkottai Kannan Srinivasan Ameet Talwalkar Aylin Yener Lei Ying The Ohio State University Purdue University University of Texas Austin Northeastern University University of Illinois Urbana Champaign Carnegie Mellon University University of Massachusetts Amherst University of Michigan University of Wisconsin Madison University of Washington University of Illinois Chicago

networking and Artificial Intelligence (AI) are two of the most transformative information technologies over the last few decades. Building upon the synergies of these two powerful technologies, we envision designing next generation of edge networks to be highly efficient, reliable, robust and secure. To this end, in this paper, we delve into interesting and fundamental research challenges and opportunities that span two major broad and symbiotic areas: AI for Networks and Networks for AI. the former deals with the development of new AI tools and techniques that can enable the next generation AI-assisted networks; while the latter focuses on developing networking techniques and tools that will facilitate the vision of distributed intelligence, resulting in a virtuous research cycle where advances in one will help accelerate advances in the other. A wide range of applications will be further discussed to illustrate the importance of the foundational advances developed in these two areas.

关键词： Wireless communication Symbiosis Privacy Uncertainty Program processors Reliability engineering Security

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