优德官网高性能智能盘算中心主任黄铠教授团队2022年有9篇学术论文被IEEE Trans. on Computers（TC）. on Cloud Computing（TCC），，，，，Industrial Informatics（TII）、ACM Trans. on Internet Technology（TIT）、The ACM Special Interest Group on Management of Data（SIGMOD）、IEEE International Conference on Communications（IEEE ICC）及Journal of Computer Science and Technology（JCST）吸收，，，，，论文先容如下。。。。 。。。。
1. Scenario-based AI Benchmark Evaluation of Distributed Cloud/Edge Platforms
by Tianshu Hao, Kai Hwang, Jianfeng Zhan, Yuejin Li and Yong Cao, IEEE Trans on Computers, accepted June 10, 2022.
文章摘要：This paper proposes a new AI benchmark suite for assessing the performance of DCE platforms in machine learning (ML) and cognitive science applications. The benchmark suite is custom-designed to satisfy scenario-based performance requirements, namely the model training time, inference speed, model accuracy, job response time, quality of service, and system reliability. These metrics are substantiated by intensive experiments with real-life AI workloads. Our work is specially tailored for supporting massive AI multitasking across distributed resources in the networking environment. Our benchmark experiments were conducted on an AI-oriented 优德官网 cloud built at the Chinese University of Hong Kong, Shenzhen. Our benchmark results reveal the advantages of using the DCE systems cost-effectively in smart cities, healthcare, community surveillance, and transportation services. Our technical contributions are in the 优德官网 cloud architecture, benchmark design, testing, and distributed AI computing requirements. Our work will benefit computer system designers and AI application developers on clouds, edge, and mobile devices, that are supported by 5G mobile networks and AIoT resource.
第一作者：郝天舒（中科院盘算所博士生，，，，，原优德官网会见研究助理）
其他主要作者：黄铠（港中大（深圳）校长讲座教授、优德官网中心主任，，，，，通讯作者）、詹剑峰（中科院盘算所研究员，，，，，中科院大学教授）
期刊先容：IEEE Transactions on Computers是IEEE最早设立的学术期刊，，，，，已经有70年历史。。。。 。。。。恒久被盘算机领域的研究职员、开发职员、手艺治理职员和教育事情者关注。。。。 。。。。该期刊的研究领域包括且不限于以下内容: 盘算机组织和系统结构；；；；；；；；操作系统、软件系统和通讯协议；；；；；；；；实时系统和嵌入式系统；；；；；；；；数字装备、盘算机部件和互联网络；；；；；；；；规范、设计、原型和测试要领和工具；；；；；；；；性能、容错性、可靠性、清静性和可测试性；；；；；；；；案例研究以及实验和理论评估；；；；；；；；以及新的和主要的应用和趋势。。。。 。。。。影响因子为2.663。。。。 。。。。
2. Federated Clouds for Efficient Multitasking in Distributed Artificial Intelligence Applications
by Yuejin Li, Kai Hwang, Kefan Shuai, Zheng dao Li, Albert Zomaya, IEEE Trans. on Cloud Computing, accepted June 14, 2022.
文章摘要：Distributed cloud/edge resources are needed to execute pervasive artificial intelligence tasks, collectively. The AI workload and data sets have variable multitasking granularity, privacy constraints, and communication latency concerns. This paper presents a novel federated cloud/edge (FCE) framework, illustrated by distributed medical image processing across multiple hospital sites. This federated cloud system appeals to train many machine learning models efficiently with workload balancing and reduced communication overheads. We tested the FCE model on a multi-cloud platform recently built at the Chinese University of Hong Kong in Shenzhen. We claim three distinct advantages in using the FCE system. First, our federated cloud system results in 41.3% reduction in total AI processing time in large-scale ML/DL experiments. Second, high machine model accuracy was achieved at 87% level in telemedicine experiments. The virtual graph helps reduce internode traffic latencies to avoid ML inference slowdowns. Third, the system can tolerate multiple cloud failures to enter a graceful degradation mode in case of node failures. The scalable performance gains in AI processing speed, model accuracy, and fault tolerance make our federated clouds a truly viable approach to solving massive AI multitasking problem latencies to avoid ML inference slowdowns. Third, the system can tolerate multiple cloud failures to enter a graceful degradation mode in case of node failures. The scalable performance gains in AI processing speed, model accuracy, and fault tolerance make the federated clouds a truly viable approach to solving massive AI multitasking problems.
第一作者：李岳瑾（港中大（深圳）博士生，，，，，优德官网研究助理）
其他主要作者： 黄铠（港中大（深圳）校长讲座教授、优德官网中心主任，，，，，通讯作者）
期刊先容：IEEE Transactions on Cloud Computing是IEEE云盘算与多学科领域偏向的旗舰期刊，，，，，在国际上享有盛誉。。。。 。。。。该期刊分享与云盘算相关的所有领域的立异研究思绪和效果，，，，，关注云盘算理论、算法、系统、和应用相关的手艺问题，，，，，主要研究偏向包括虚拟化和容器手艺、云资源治理和优化、软件界说系统、基于云的大数据治理和剖析、云清静和隐私、移动云盘算、云性能和可靠性、XaaS等，，，，，影响因子为5.938。。。。 。。。。
3. Transfer Reinforcement Learning for Adaptive Task Offloading over Distributed Edge Clouds
by Kefan Shuai, Yiming Miao, Kai Hwang, and Zhengdao Li, IEEE Trans. on Cloud Computing, accepted July 2022
文章摘要：In the big data era, resource-constrained mobile devices generate an overwhelmingly large amount of data and complex tasks that they cannot handle. Offloading computation-heavy tasks to nearby edge clouds is promising to solve this problem. However, some unsolved problems still exist, such as the heterogeneity of tasks, delay-sensitiveness, energy limit of mobile devices, and the weak adaptability to new devices and the environment. To address and tackle these problems, we present a two-module transfer reinforcement learning (TRL) framework for offloading. A domain adaptation module is used to align heterogeneous characteristics of mobile devices, in order to reduce redundant reinforcement learning training. Then, TRL makes offloading decisions with a deep reinforcement learning (DRL) module. We evaluate the performance of TRL through both simulation experiments and real-world experiments. We show that TRL reduces the average task delay by over 10% compared to other DRL methods. When new mobile devices join, TRL offers a 50% lower user-experienced convergence time than other DRL algorithms.
第一作者：帅克凡（港中大（深圳）博士生、优德官网研究助理）
其他主要作者：黄铠（港中大（深圳）校长讲座教授、优德官网中心主任，，，，，通讯作者）
期刊先容：IEEE Transactions on Cloud Computing是IEEE云盘算与多学科领域偏向的旗舰期刊，，，，，在国际上享有盛誉。。。。 。。。。该期刊分享与云盘算相关的所有领域的立异研究思绪和效果，，，，，关注云盘算理论、算法、系统、和应用相关的手艺问题，，，，，主要研究偏向包括虚拟化和容器手艺、云资源治理和优化、软件界说系统、基于云的大数据治理和剖析、云清静和隐私、移动云盘算、云性能和可靠性、XaaS等，，，，，影响因子为5.938。。。。 。。。。
4. Drone Swarm Path Planning for Mobile Edge Computing in Industrial Internet of Things
by Yiming Miao, Kai Hwang, Di Wu, Yixue Hao, and Min Chen, IEEE Trans.on Industrial Informatics, accepted July 2022
文章摘要：Drone-swarm-assisted mobile edge computing (MEC) provides extra computation and storage capacity for smart city applications and industrial Internet of Things. To solve the problems of traditional fixed base stations in complex terrain, including expensive cost of deployment, transmission loss of telecommunication and limited coverage, this paper brings forward the unmanned aerial vehicles (UAVs) as mobile edge computing nodes in the air. For the purpose of matching the dynamic mobile devices and UAV trajectory, this paper raises a multi-UAVs-assisted mobile edge computing offloading algorithm based on global and local path planning controlled by ground station and onboard computer.
Firstly, this paper considers a drone swarm scheduling and allocation strategy based on the priority of monitoring areas, UAVs residual energy and distance to target points, so that to minimize the global flight length and energy consumption. Secondly, based on user mobility, this paper calculates the optimal communication coverage of UAV, and jointly optimizes the local path planning and computing offloading, so that to maximize the number of offloading services and minimize the total latency in completing the computation task. Finally, based on the total latency and energy consumption of path planning and computation offloading, a UAV cluster computation offloading strategy with optimized energy efficiency is realized. Experimental results prove that the proposed algorithm can provide more offloading services with shorter path length and greater energy efficiency.
第一作者：缪一铭（港中大（深圳）研究助理教授，，，，，优德官网副研究员）
其他主要作者：黄铠（港中大（深圳）校长讲座教授、优德官网中心主任）、陈敏（华中科技大学教授，，，，，原优德官网高级研究员，，，，，通讯作者）
期刊先容：IEEE Transactions on Industrial Informatics聚焦于以知识为基础的工业自动化相关研究，，，，，致力于揭晓智能工程手艺和工程工业生长相关的基础科学方面的论文，，，，，设想未来 5-10 年智能自动化系统和实时中心件手艺领域的手艺分支，，，，，在实时生产控制和调理级别上扩展了网络嵌入式智能。。。。 。。。。该期刊关注智能和盘算机控制系统、机械人、工厂通讯和自动化、柔性制造、视觉系统以及数据收罗和信号处置惩罚方面的最新生长，，，，，影响因子为11.648。。。。 。。。。
5. Drone enabled Smart Air-Agent for 6G Network
by Yiming Miao Jinfeng Xu, Min Chen, and Kai Hwang, IEEE International Conference on Communications, May 17, 2022
文章摘要：The future ubiquitous network, which is mainly characterized by full coverage communication, air-ground integration, multidimensional fusion, network reconfiguration and sensing-communication-computing integration, has become the development trend of 6G technology. The realization of ubiquitous coverage and perceptive fusion of IoT-UAV-Edge is an urgent problem to be solved for complex fusion services. Therefore, this paper proposes a drone-enabled smart air agent in 6G edge fusion system. Firstly, the energy efficient dynamic routing strategy based on joint air-ground control optimization is designed to improve the fusion sensing performance and prolong the service time of drone swarm. Then, the system integration is realized to achieve the functionalities of perception, transmission, computing and analysis. Finally, an airborne data fusion mechanism is designed to solve the associated cognitive optimization problem. Experimental results validate the effectiveness and practicability of our system in all stated goals.
第一作者：缪一铭（港中大（深圳）研究助理教授，，，，，优德官网副研究员）
其他主要作者：陈敏（华中科技大学教授，，，，，原优德官网高级研究员）、黄铠（港中大（深圳）校长讲座教授、优德官网中心主任，，，，，通讯作者）
聚会先容：IEEE International Conference on Communications（ICC）是IEEE通讯协会的两个旗舰聚会之一，，，，，也是通讯领域顶级学术聚会。。。。 。。。。该聚会致力于推动通讯方面的立异。。。。 。。。。每年有凌驾2900名科研职员加入该聚会。。。。 。。。。该聚会的研究偏向包括物联网和传感器网络、认知广播和人工智能网络、通讯和信息系统清静、通讯质量、可靠性和建模、移动和无线网络、下一代网络和互联网、云盘算与大数据、卫星和空间通讯等多个领域。。。。 。。。。该聚会为CCF C类聚会。。。。 。。。。
6. Negative Information Measurement at AI Edge: A New Perspective for Mental Health Monitoring
by Min Chen, Yiming Miao, Kai Hwang, Yixue Hao, Long Hu, and Zhongchun Liu, ACM Trans. on Internet Technology, published in January 2022.
文章摘要：The outbreak of COVID-19 has caused serious harm to people’s physical and mental health. Due to the serious situation of the epidemic, a lot of negative energy information increases people’s psychological burden. However, effective interventions against mental health problems are not in abundance. To address such challenges, in this article, we propose the concept of negative information to describe information that has a negative impact on people’s mental health. To achieve the measurement of negative information, the level of mental health inversely measures the degree of negative information. Specifically, we design a system to measure the negative information used to monitor the mental health state of the user. The cognition of mental health is realized by intelligent algorithm deployed on the edge cloud. Users can be responded to in real time in practical applications. Finally, we use real collected dataset to verify the influence of negative information. The experiments show that the system can achieve negative information measurement and provide an effective countermeasure for solving mental health problems during a pandemic situation.
第一作者：陈敏（华中科技大学教授、原优德官网高级研究员，，，，，通讯作者）
其他主要作者：缪一铭（港中大（深圳）研究助理教授、优德官网副研究员）、黄铠（港中大（深圳）校长讲座教授、优德官网中心主任）
期刊先容：ACM Transactions on Internet Technology是一个多学科期刊，，，，，揭晓关于基础和立异研究、手艺和政策的原创研究论文，，，，，涉及互联网、互联网规模盘算系统、应用和效劳的设计、使用、剖析、工程和治理。。。。 。。。。该期刊搜集了许多有助于互联网系统和手艺的学科，，，，，如盘算机软件工程、盘算机编程语言、数据库治理、清静、知识发明和数据挖掘、网络和漫衍式系统、通讯、性能和可扩展性等领域，，，，，影响因子为3.315。。。。 。。。。
7. Collaborative Cloud-Edge Service Cognition Framework for DNN Configuration Toward Smart IIoT
by W. Xiao, Y. Miao, G. Fortino, D. Wu, M. Chen, Kai Hwang, IEEE Trans. Industrial Informatics, published in October 2022.
文章摘要：With the widespread application of artificial intelligence and the Internet of Things, the intellectualization of the industrial Internet of Things (IIoT) has received more and more attention. However, in the application scenario with numerous sensors, the contradiction between massive requests of computing tasks and high requirements of inference quality affects the operation efficiency and service reliability. Moreover, due to the heterogeneity of computing resources and the randomness of communication environments of the cloud-edge system, how to compute and deploy deep learning models in a cloud-edge collaborative environment has also become a challenging problem. Therefore, this article presents a collaborative cloud-edge service cognitive framework for deep neural network (DNN) model service configuration to provide dynamic and flexible computing services. In order to adapt to different service requirements, we explored the tradeoffs between accuracy, latency, and energy consumption indicators, and a revenue target is established, which considers the quality of service experience and the system energy consumption to improve resource utilization efficiency.
第一作者：肖文婧（华中科技大学博士生，，，，，原优德官网研究助理）
其他主要作者：缪一铭（港中大（深圳）研究助理教授，，，，，优德官网副研究员）、黄铠（港中大（深圳）校长讲座教授、优德官网中心主任）、陈敏（华中科技大学 教授，，，，，原优德官网高级研究员，，，，，通讯作者）
期刊先容：IEEE Transactions on Industrial Informatics聚焦于以知识为基础的工业自动化相关研究，，，，，致力于揭晓智能工程手艺和工程工业生长相关的基础科学方面的论文，，，，，设想未来 5-10 年智能自动化系统和实时中心件手艺领域的手艺分支，，，，，在实时生产控制和调理级别上扩展了网络嵌入式智能。。。。 。。。。该期刊关注智能和盘算机控制系统、机械人、工厂通讯和自动化、柔性制造、视觉系统以及数据收罗和信号处置惩罚方面的最新生长，，，，，影响因子为11.648。。。。 。。。。
8. LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications
by Jinhan Xin, Kai Hwang and Zhibin Yu, ACM Special Interest Group on Management of Data Conference, accepted June, 2022
文章摘要：Spark SQL has been widely deployed in industry but it is challenging to tune its performance. Recent studies try to employ machine learning (ML) to solve this problem, but suffer from two drawbacks. First, it takes a long time (high overhead) to collect training samples. Second, the optimal configuration for one input data size of the same application might not be optimal for others. To address these issues, we propose a novel Bayesian Optimization (BO) based approach named LOCAT to automatically tune the configurations of Spark SQL applications online. LOCAT innovates three techniques. The first technique, named QCSA, eliminates the configuration-insensitive queries by Query Configuration Sensitivity Analysis (QCSA) when collecting training samples.
The second technique, dubbed DAGP, is a Datasize-Aware Gaussian Process (DAGP) which models the performance of an application as a distribution of functions of configuration parameters as well as input data size. The third technique, called IICP, Identifies Important Configuration Parameters (IICP) with respect to performance and only tunes the important ones. As such, LOCAT can tune the configurations of a Spark SQL application with low overhead and adapt to different input data sizes. We employ Spark SQL applications from benchmark suites? ??? ??, ? ?? ? ?, and ??????? running on two significantly different clusters, a four-node ARM cluster and an eight-node x86 cluster, to evaluate LOCAT. The experimental results on the ARM cluster show that LOCAT accelerates the optimization procedures of the state-of-the-art approaches by at least 4.1× and up to 9.7×; moreover, LOCAT improves the application performance by at least 1.9× and up to 2.4×. On the x86 cluster, LOCAT shows similar results to those on the ARM cluster.
第一作者：辛锦瀚（中科院先进院硕士生）
其他主要作者：黄铠（港中大（深圳）校长讲座教授、优德官网中心主任）、喻之斌（中科院先进院研究员、原优德官网研究员，，，，，通讯作者）
聚会先容：数据治理国际聚会（SIGMOD Conf.） 由美国盘算机协会（ACM）数据治理专业委员会SIGMOD）提倡、在数据库领域具有最高学术职位的国际性学术聚会。。。。 。。。；；；；；；；＞刍岬哪康氖窃谌蚬婺Ｄ谖菘饬煊虻难芯空摺⒖⒄咭约坝没峁┮桓鎏剿髯钚卵跬纺院脱芯恳臁⒔涣骺⒓记伞⒐ぞ咭约奥睦钠教，，，，，指导和增进数据库学科的生长。。。。 。。。。该聚会为CCF A类聚会
9. SOCA-DOM: A Mobile SoC Array System for Analyzing Big Data on the Move
by Le-Le Li, Jiang-Yi Liu, Jian-Ping Fan, Xue-Hai Qian , Kai Hwang, Yeh-Ching Chung and Zhi-Bin Yu, Journal of Computer Science and Technology, accepted April 25, 2022
文章摘要：Analyzing big data on the move requires that the hardware resource should be low volume, low power, light in weight, high performance, and highly scalable whereas the management software should be ?exible and consume little hardware resource. To meet these requirements, we present a system named SOCA-DOM that encompasses a mobile system-on-chip array architecture and a two-tier “software-de?ned” resource manager named Chameleon. First, we design an Ethernet communication board to support an array of mobile system-on-chips. Second, we propose a two-tier software architecture for Chameleon to make it ?exible. Third, we devise data, con?guration, and control by software-de?ned” managers. Fourth, we design an accurate synthetic metric that represents the computational power of a computing node. We employ 12 Apache Spark benchmarks to evaluate SOCA-DOM. We find that up to 9.4x less CPU resource and 13.5x less memory than Mesos. A 16-node SOCA-DOM consumes up to 4x less energy than two standard Xeon servers. We conclude that an array architecture with ?ne-grained hardware resources and software-de?ned management works well for analyzing big data on the move.
第一作者：李乐乐（中科院先进院硕士生）
其他主要作者：黄铠（港中大（深圳）校长讲座教授、优德官网中心主任）、喻之斌（中科院先进院研究员、原优德官网研究员，，，，，通讯作者）
期刊先容：Journal of Computer Science and Technology (JCST) 是中国盘算机科学手艺领域惟一的英文学术性期刊。。。。 。。。。由数十位国际盘算机界的著名专家和学者携手编审，，，，，掌握天下盘算机科学手艺最新生长趋势。。。。 。。。。 JCST荟萃了海内外盘算机科学手艺领域中有指导性和开拓性的学术论著，，，，，其内容包括: 盘算机科学理论，，，，，信息清静，，，，，盘算机系统结构与高性能盘算，，，，，模式识别与图像处置惩罚，，，，， 盘算机网络与Internet，，，，，漫衍式盘算与网格盘算，，，，，自然语言处置惩罚，，，，，人工智能，，，，，数据库与知识库系统，，，，，可靠性盘算等。。。。 。。。。本刊实时反应盘算机科学手艺领域最新研究效果，，，，，突出研究热门和有中国特色的高水平研究。。。。 。。。。 影响因子为2.18。。。。 。。。。
