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arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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System-Level Energy-Aware Design Methodology Towards End-To-End Response Time Optimization

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2019年

作者： Xie, Guoqi Huang, Jing Li, Yan Liu Renfa Li, Keqin Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan 410082 China. College of Computer Science and Electronic Engineering Hunan University China 410082 China and also is with the Department of Computer Science State University of New York New Paltz New York 12561 USA.

Energy provision is known and limited for battery-constrained/powered systems (e.g., mobile phones, electric cars, robots, and wireless sensor networks). System-level energy-aware design methodologies have been proposed to control energy usage for energy provision guarantee, and to optimize the end-to-end response time for high-performance in heterogeneous energy-aware systems, where energy pre-assignment strategies are considered as their main contributions. However, when pre-assigning energy to tasks, these strategies either are overly pessimistic or adopt an average manner without a customizable approach to specific tasks. In this paper, we propose an effective algorithm called ratio-based energy pre-assignment (REP) to be customizable to split the available energy according to the energy ratios of individual tasks. Experiments confirm that REP outperforms the state-of-the-art three algorithms of the same type. After REP is used, we further propose a new innovation algorithm called energy surplus reduction (ESR) to reduce the execution time of small partial tasks, thereby optimizing the end-to-end response time within its energy provision. Experiments confirm ESR can reduce the energy usages of small partial tasks to achieve sufficient response time optimization. IEEE

关键词： Wireless sensor networks

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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The EDF with Dual Virtual Deadline Algorithm for Mixed Criticality Task System

The EDF with Dual Virtual Deadline Algorithm for Mixed Criti...

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Lining Zeng Cheng Xu Renfa Li Key Laboratory for Embedded and Network Computing Hunan University Changsha Hunan Province China

ISBN: (纸本)9781538665664;9781538665657

In the area of real-time embedded systems, one of the hot topics is scheduling the mixed-criticality tasks to execute the LO tasks better when guaranteeing the correctness of HI tasks. In this paper, we research the schedule mechanism for dual-criticality tasks. Firstly, we modify the correctness condition of MC scheduling. Then, we present an optimized MC scheduling algorithm EDF -DVD, which assigns scale factors for both HI jobs and LO jobs. We analyze the different situation based on system utilizetion and present the strategy of calculating the value of scale factor corresponding to the utilization. The experiments show that our mechanism has better performance in scheduling the dual-criticality tasks.

关键词： Task analysis Schedules Scheduling embedded systems Switches Scheduling algorithms Real-time systems

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A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud Data Centers

A Reinforcement Learning-Based Virtual Machine Placement Str...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Saiqin Long Zhetao Li Yun Xing Shujuan Tian Dongsheng Li Rong Yu Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China School of Computer Science Xiangtan University Xiangtan China College of Computer National University of Defense Technology Changsha China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781728176505

With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms.

关键词： Weight measurement Cloud computing Data centers Energy consumption NP-hard problem High performance computing Virtual machining

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Ai driven heterogeneous MEC system for dynamic environment - Challenges and solutions

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of computing, Communication and Caching (3C) resources at the network edge, Mobile Edge computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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A Novel Lncrna, Lncery, Interacts with Wdr82 to Regulate Erythroid Differentiation By Promoting Globin Gene Transcription

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BLOOD 2020年 136卷 2-2页

作者： Yang, Shangda Sun, Guohuan Wu, Peng Kuang, Yijin Chen, Cong Zheng, Zhaofeng Gu, Quan Zhu, Caiying Gou, Fanglin Yang, Zining Zhao, Xiangnan Yang, Liu Lu, Shihong Lihong, Shi Lv, Xue Dong, Fang Shi, Lihong Ng, Lai Guang Liu, Jing Cheng, Hui Cheng, Tao Institute of Hematology & Blood Diseases Hospital State Key Laboratory of Experimental Hematology National Clinical Research Center for Blood Diseases Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Science & Peking Union Medical College Tianjin China State Key Laboratory of Experimental Hematology National Clinical Research Center for Blood Diseases Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China University of Pittsburgh School of Medicine Tianjin China Molecular Biology Research Center Center for Medical Genetics Hunan Province Key Laboratory of Basic and Applied Hematology School of Life Sciences Central South University Changsha China State Key Laboratory of Experimental Hematology Tianjin China State Key Laboratory of Experimental Hematology National Clinical Research Center for Blood Diseases Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Science & Peking Union Medical College Tianjin China State Key Laboratory of Hematology Institute of Hematology & Blood Diseases Hospital Tianjin China Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China Singapore Immunology Network Agency for Science Technology and Research Biopolis Singapore Department of Hematology Third Xiangya Hospital of Central South University Changsha China

Hematopoietic differentiation is controlled by both genetic and epigenetic regulators. Long non-coding RNAs (lncRNAs) have been demonstrated to be important for normal hematopoiesis, but their function in erythropoiesis needs to be further explored. Here, we profiled the transcriptome of 17 murine hematopoietic cell populations by deep sequencing and identified a novel lncRNA, that was highly expressed in erythroid-related progenitors and erythrocytes. For this reason, we named it lncEry. We also identified a novel lncEry isoform, which was the principal transcript and has not been reported before. Furthermore, we found that nearly 90% of lncEry molecules localized to the nucleus. Next, we performed knockdown and knockout assays to study the function of lncEry, and found that lncEry depletion impaired erythroid differentiation. RNA sequencing analysis showed that lncEry depletion decreased the expression of erythrocyte homeostasis or differentiation related genes, including globin genes, thus indicating its important role in regulating erythroid differentiation. Mechanistically, we performed RNA-pulldown assays and found that lncEry could interact with Wdr82, a component of the Set1A histone H3-Lys4 methyltransferase complex. In addition, a series of molecular assays indicated that lncEry could stabilize the localization of Set1A/Wdr82 complex to facilitate H3K4me3 on the promoter region of globin genes and participate in regulating erythropoiesis. These findings identify lncEry as an important player in the transcriptional regulation of globin genes to coordinate erythropoiesis. Disclosures No relevant conflicts of interest to declare.

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Asymmetric residual neural network for accurate human activity recognition

arXiv

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arXiv 2019年

作者： Long, Jun Sun, WuQing Raymond, Osolo Ian Yang, Zhan School of Information Science and Engineering Central South University Changsha410083 China Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province

Human Activity Recognition (HAR) using deep neural network has become a hot topic in human-computer interaction. Machine can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem, but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of the automatic learning, we propose a novel Asymmetric Residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, yet still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared with some conventional and state-of-the-art learning-based methods. Then, we discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Asymmetric deep semantic quantization for image retrieval

arXiv

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arXiv 2019年

作者： Yang, Zhan Raymond, Osolo Ian Sun, WuQing Long, Jun School of Computer Science and Engineering Central South University Changsha410083 China Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province

Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning based techniques, hashing can outperform non-learning based hashing technique in many applications. However, we argue that the current deep learning based hashing methods ignore some critical problems (e.g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes). In this paper, we propose a novel image hashing method, termed as Asymmetric Deep Semantic Quantization (ADSQ). ADSQ is implemented using three stream frameworks, which consist of one LabelNet and two ImgNets. The LabelNet leverages the power of three fully-connected layers, which are used to capture rich semantic information between image pairs. For the two ImgNets, they each adopt the same convolutional neural network structure, but with different weights (i.e., asymmetric convolutional neural networks). The two ImgNets are used to generate discriminative compact hash codes. Specifically, the function of the LabelNet is to capture rich semantic information that is used to guide the two ImgNets in minimizing the gap between the real-continuous features and the discrete binary codes. Furthermore, ADSQ can utilize the most critical semantic information to guide the feature learning process and consider the consistency of the common semantic space and Hamming space. Experimental results on three benchmarks (i.e., CIFAR-10, NUS-WIDE, and ImageNet) demonstrate that the proposed ADSQ can outperforms current state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Hash functions

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