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Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

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Online Container Scheduling for Low-Latency IoT Services in ...

IEEE International Conference on Communications in China (ICCC)

作者： Hanshuai Cui Zhiqing Tang Jiong Lou Weijia Jia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China School of Artificial Intelligence Beijing Normal University Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai China

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent up-grades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27% compared to baseline algorithms.

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications

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arXiv 2021年

作者： Shi, Zhaoyuan Yang, Helin Xie, Xianzhong Lu, Huabing Cai, Jun Ding, Zhiguo School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada Department of Electrical Engineering Princeton University PrincetonNJ08544 United States School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom

The combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency and spectral efficiency of the upcoming beyond fifth generation network (B5G), especially for support the wireless sensor communications in Internet of things (IoT) system. However, how to realize intelligent frequency, time, and energy resource allocation to support better performances is an important problem to be solved. In this paper, we study joint spectrum, energy, and time resource management for the EH-CR-NOMA IoT systems. Our goal is to minimize the number of data packets losses for all secondary sensing users (SSU), while satisfying the constraints on the maximum charging battery capacity, maximum transmitting power, maximum buffer capacity, and minimum data rate of primary users (PU) and SSUs. Due to the nonconvexity of this optimization problem and the stochastic nature of the wireless environment, we propose a distributed multidimensional resource management algorithm based on deep reinforcement learning (DRL). Considering the continuity of the resources to be managed, the deep deterministic policy gradient (DDPG) algorithm is adopted, based on which each agent (SSU) can manage its own multidimensional resources without collaboration. In addition, a simplified but practical action adjuster (AA) is introduced for improving the training efficiency and battery performance protection. The provided results show that the convergence speed of the proposed algorithm is about 4 times faster than that of DDPG, and the average number of packet losses (ANPL) is about 8 times lower than that of the greedy algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Resource allocation

SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval

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arXiv 2022年

作者： Shen, Yang Sun, Xuhao Wei, Xiu-Shen Jiang, Qing-Yuan Yang, Jian School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Integrated Services Networks Xidian University China State Key Laboratory for Novel Software Technology Nanjing University China Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology China

In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we first develop a suppression-enhancing mask (SEM) based attention to dynamically localize discriminative image regions. More importantly, different from existing attention mechanism simply erasing previous discriminative regions, our SEM is developed to restrain such regions and then discover other complementary regions by considering the relation between activated regions in a stage-by-stage fashion. In each stage, the interactive channel transformation (ICON) module is afterwards designed to exploit correlations across channels of attended activation tensors. Since channels could generally correspond to the parts of fine-grained objects, the part correlation can be also modeled accordingly, which further improves fine-grained retrieval accuracy. Moreover, to be computational economy, ICON is realized by an efficient two-step process. Finally, the hash learning of our SEMICON consists of both global- and local-level branches for better representing fine-grained objects and then generating binary hash codes explicitly corresponding to multiple levels. Experiments on five benchmark fine-grained datasets show our superiority over competing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Image retrieval

RIFFLE: A Distributed Network Operating System with Re-configurability for Multiple Domains

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RIFFLE: A Distributed Network Operating System with Re-confi...

IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Boyang Zhou Chunming Wu Shuangxi Chen Zhen Li Hong Fan Zhejiang Lab Intelligent Networks Institute Hangzhou China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Jiaxing Key Laboratory of Industrial Internet Jiaxing China Research Center for Intelligent Technology Standardization Zhejiang Lab Hangzhou China Department of Electrical Engineering Shanghai University of Electric Power Shanghai China

The distributed network control in software-defined networking is greatly challenged by the design demands of evolving a running distributed control plane (DCP), and by the complexity of handling the control state dynamics. In this paper, we propose RIFFLE to address the problem using a distributed network operating system approach. RIFFLE enables the evolvability of control states of DCP deployed on a global scale. RIFFLE handles spatial and temporal dynamics that occur when updating and migrating the distributed control states, which overcomes the critical barrier to the evolvability. It fills the gap by enabling temporal reconfigurability in DCP. Moreover, RIFFLE reduces the complexities of building and maintaining network control services in DCP by enabling componentization and abstracting the underlying network dynamics. We evaluated the prototype RIFFLE through experiments running in Planetlab. The results show that the prototype scales well for 135 nodes. We also validate that RIFFLE ensures the continuity and low content request delay for the supported the information-centric network supported during cache update periods owing to the enabled evolvability.

关键词： Runtime Roads Decentralized control Information-centric networking Prototypes Ubiquitous computing Dynamic scheduling

Cooperative Data Collection with Multiple UAVs for Information Freshness in the Internet of Things

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arXiv 2023年

作者： Wang, Xijun Yi, Mengjie Liu, Juan Zhang, Yan Wang, Meng Bai, Bo School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China School of Cyber Engineering Xidian University Xi’an710071 China School of Electrical Engineering and Computer Science Ningbo University Zhejiang 315211 China State Key Lab of Integrated Service Networks Information Science Institute Xidian University Shaanxi Xi’an710071 China Theory Lab Central Research Institute 2012 Labs Huawei Technology Co. Ltd. Hong Kong Science Park Hong Kong

Maintaining the freshness of information in the Internet of Things (IoT) is a critical yet challenging problem. In this paper, we study cooperative data collection using multiple Unmanned Aerial Vehicles (UAVs) with the objective of minimizing the total average Age of Information (AoI). We consider various constraints of the UAVs, including kinematic, energy, trajectory, and collision avoidance, in order to optimize the data collection process. Specifically, each UAV, which has limited on-board energy, takes off from its initial location and flies over sensor nodes to collect update packets in cooperation with the other UAVs. The UAVs must land at their final destinations with non-negative residual energy after the specified time duration to ensure they have enough energy to complete their missions. It is crucial to design the trajectories of the UAVs and the transmission scheduling of the sensor nodes to enhance information freshness. We model the multi-UAV data collection problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), as each UAV is unaware of the dynamics of the environment and can only observe a part of the sensors. To address the challenges of this problem, we propose a multi-agent Deep Reinforcement Learning (DRL)-based algorithm with centralized learning and decentralized execution. In addition to the reward shaping, we use action masks to filter out invalid actions and ensure that the constraints are met. Simulation results demonstrate that the proposed algorithms can significantly reduce the total average AoI compared to the baseline algorithms, and the use of the action mask method can improve the convergence speed of the proposed algorithm. © 2023, CC BY.

关键词： Internet of things

Multi-stage Moving Target Defense: A Security-enhanced D-FACTS Implementation Approach

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arXiv 2022年

作者： Wang, Jiazhou Tian, Jue Liu, Yang Guan, Xiaohong Yang, Dong Liu, Ting The Ministry of Education Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'An710049 China School of Computer Xi'an University of Posts and Telecommunications Xi'An710061 China Power Infrastructure Cyber Security Research Center Xi'an Thermal Power Research Institute Co. LTD Xi'An710054 China

In recent studies, moving target defense (MTD) has been applied to detect false data injection (FDI) attacks using distributed flexible AC transmission system (D-FACTS) devices. However, the inherent conflict between the security goals of MTD (i.e., detecting FDI attacks) and the economic goals of D-FACTS devices (i.e., reducing power losses) would impede the application of MTD in real systems. Moreover, the detection capabilities of existing MTDs are often insufficient. This paper proposes a multistage MTD (MMTD) approach to resolve these two issues by adding a group of designed security-oriented schemes before D-FACTS' economic-oriented scheme to detect FDI attacks. We keep these security-oriented schemes for a very short time interval and then revert to the economic-oriented scheme for the remaining time to ensure the economic requirements. We prove that a designed MMTD can significantly improve the detection capability compared to existing one-stage MTDs. We find the supremum of MMTD's detection capability and study its relationship with system topology and D-FACTS deployment. Meanwhile, a greedy algorithm is proposed to search the MMTD strategy to reach this supremum. Simulation results show that the proposed MMTD can achieve the supremum against FDI attacks while outperforming current MTD strategies on economic indicators. Copyright © 2022, The Authors. All rights reserved.

关键词： Network security

Machine learning for predictive on-demand deployment of UAVs for wireless communications

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arXiv 2018年

作者： Zhang, Qianqian Mozaffari, Mohammad Saad, Walid Bennis, Mehdi Debbah, Mérouane Bradley Department of Electrical and Computer Engineering Virginia Tech VA United States Center for Wireless Communications University of Oulu Finland Mathematical and Algorithmic Sciences Lab Huawei France R&D Paris France Large Systems and Networks Group CentraleSupélec Université Paris-Saclay 3 rue Joliot-Curie Gif-sur-Yvette91192 France

In this paper, a novel machine learning (ML) framework is proposed for enabling a predictive, efficient deployment of unmanned aerial vehicles (UAVs), acting as aerial base stations (BSs), to provide on-demand wireless service to cellular users. In order to have a comprehensive analysis of cellular traffic, an ML framework based on a Gaussian mixture model (GMM) and a weighted expectation maximization (WEM) algorithm is introduced to predict the potential network congestion. Then, the optimal deployment of UAVs is studied to minimize the transmit power needed to satisfy the communication demand of users in the downlink, while also minimizing the power needed for UAV mobility, based on the predicted cellular traffic. To this end, first, the optimal partition of service areas of each UAV is derived, based on a fairness principle. Next, the optimal location of each UAV that minimizes the total power consumption is derived. Simulation results show that the proposed ML approach can reduce the required downlink transmit power and improve the power efficiency by over 20%, compared with an optimal deployment of UAVs with no ML prediction. Copyright © 2018, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

A Modified Ćuk Converter With Constant Switching Frequency Modulated Sliding Mode Control

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A Modified Ćuk Converter With Constant Switching Frequency ...

Compatibility in Power Electronics (CPE)

作者： Emre Ozsoy Sanjeevikumar Padmanaban Frede Blaabjerg Mahajan Sagar Bhaskar Fiaz Ahmad Research & Development Design Center Danfoss Drives A/S Denmark Department of Energy Technology Aalborg University Esbjerg Denmark Center of Reliable Power Electronics (CORPE) Department of Energy Technology Aalborg University Aalborg Denmark Renewable Energy Lab Department of Communications & Networks College of Engineering Prince Sultan University Riyadh Saudi Arabia Electrical Engineering National University of Computer & Emerging Science5 Peshawar Pakistan

High output gain ratio demand for DC-DC converters have been increasing in recent years due to diverse integration of various renewable sources and industrial applications. This article focused to provide a detailed analysis of a Cuk converter with increased voltage conversion ratio. The circuit only consists of two inductors, five capacitors and four diodes. In particular, configuration is composed of combination of two different cascaded switched capacitor voltage boost network. The voltage in the output is 29 times higher than the input voltage, if 0.9 duty ratio is used. Further, high gain DC-DC converter control is a challenging task due to higher order and nonlinear characteristics. Standard PI controllers cannot accomplish robust operation for all operation regions. Hence, Sliding Mode Controller (SMC) is a reliable and robust method for high order nonlinear systems due to its robustness and independence from system dynamics and parameter variations. Furthermore, this paper develops a constant switching frequency SMC approach to high gain modified Cuk converter. Comprehensive theoretical analysis is presented and numerically verified using Matlab/Simulink software.

关键词： DC-DC power converters Switches Switching frequency PI control Voltage control Hysteresis Inductors