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BCTCP:A Feedback-Based Congestion Control Method

China Communications 2020年第6期17卷 13-25页

作者： Yuyu Zhao Guang Cheng Weici Zhang Xin Chen Jin Li School of Cyber Science&Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast UniversityNanjing 211189China Key Laboratory of Computer Network Technology of Jiangsu Province China Huawei Technologies Co. Ltd.No 101Software AvenueNanjing 211189China

Delay and throughput are the two network indicators that users most care *** congestion control methods try to occupy buffer aggressively until packet loss being detected,causing high delay and *** AQM and ECN can highly reduce packet drop rate and delay,however they may also lead to low *** queue size of routers properly means a lot to congestion control *** traffic size varying around bottleneck bandwidth creates some degree of persistent queue in the router,which brings in additional delay into network unwillingly,but a corporation between sender and router can keep it under *** persistent queue not only keeps routers being fully utilized all the time,but also lower the variation of throughput and delay,achieving the balance between delay and *** this paper,we present BCTCP(Buffer Controllable TCP),a congestion control protocol based on explicit feedback from *** requires sender,receiver and routers cooperating with each other,in which senders adjust their sending rate according to the multiple bit load factor information from *** keeps queue length of bottleneck under control,leading to very good delay and utilization result,making it more applicable to complex network environments.

关键词： explicit congestion control active queue management delay variation low latency high throughput

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Identification of critical variables for soft error detection 2nd

Identification of critical variables for soft error detectio...

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2nd International Conference on Human Centered Computing, HCC 2016

作者： Ma, Junchi Wang, Yun School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China

ISBN: (纸本)9783319318530

As process technology scales, electronic devices become more susceptible to soft error induced by radiation. Silent data corruption occurs without any symptoms and is the most severe result incurred by soft error. Duplication is the effective way to protect the program from soft error, but it has high overhead which is increasingly unacceptable for applications. Duplicating critical variables only can significantly reduce the overhead. This paper introduces an approach that identifying the variables which are critical to the execution of the program. We apply Dynamic Dependence Graph to analyze the propagation between instructions and find out the propagation path. The criticality of memory location is characterized and the criticality of each variable is then calculated. Fault injections show that with our approach the duplication achieves high detection rate with low overhead. © Springer International Publishing Switzerland 2016.

关键词： Application programs

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A primal perspective for indefinite kernel SVM problem

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Frontiers of computer Science 2020年第2期14卷 349-363页

作者： Hui XUE Haiming XU Xiaohong CHEN Yunyun WANG School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) MOENanjing 210096China College of Science Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210046China

Indefinite kernel support vector machine(IKSVM)has recently attracted increasing attentions in machine *** IKSVM essentially is a non-convex problem,existing algorithms either change the spectrum of indefinite kernel directly but risking losing some valuable information or solve the dual form of IKSVM whereas suffering from a dual gap *** this paper,we propose a primal perspective for solving the *** is,we directly focus on the primal form of IKSVM and present a novel algorithm termed as IKSVM-DC for binary and multi-class ***,according to the characteristics of the spectrum for the indefinite kernel matrix,IKSVM-DC decomposes the primal function into the subtraction of two convex functions as a difference of convex functions(DC)*** accelerate convergence rate,IKSVM-DC combines the classical DC algorithm with a line search step along the descent direction at each ***,we construct a multi-class IKSVM model which can classify multiple classes in a unified form.A theoretical analysis is then presented to validate that IKSVM-DC can converge to a local ***,we conduct experiments on both binary and multi-class datasets and the experimental results show that IKSVM-DC is superior to other state-of-the-art IKSVM algorithms.

关键词： indefinite kernel support vector machine multi-class classification non-convex optimization

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A Personalized Service Recommendation Algorithm for Service Functionality 2

A Personalized Service Recommendation Algorithm for Service ...

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2nd International Conference on Advanced Cloud and Big Data, CBD 2014

作者： Li, Wei Hu, Di Luo, Junzhou School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Southeast University Nanjing211189 China

ISBN: (纸本)9781479980857

With the increasing number of services on the Internet, it has become a great challenge to help users find services according to their demands. Personalized recommendation technology is an effective way to solve the problem. Existing service recommendation approaches make recommendations among services with same or similar functionalities to meet the non-functional requirements of users, while the functional requirements for service are seldom taken into account and new services that satisfy the needs of users are difficult to be recommended. Therefore, in this paper, we introduce social tags to the process of service recommendation and build service functionality oriented social tags model to describe user preference for service functionality, then we present a personalized service recommendation algorithm for service functionality (PSR-SF). The proposed algorithm first discovers the neighbors of a target user according to services use frequency of users, and then clusters services that have been used by the target user and his neighborhoods by using the functional characteristic vector of services which based on service functionality oriented social tags model. Finally, target user preference for service classes are generated by using service functionality tags use information of users to make recommendations. The experiment results show that the performance of PSR-SF algorithm is better than those existing recommendation algorithms in terms of service recommendation precision, recall and F values. © 2014 IEEE.

关键词： Social networking (online)

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

arXiv

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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Privacy Quantification Model Based on the Bayes Conditional Risk in Location-Based Services

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Tsinghua Science and Technology 2014年第5期19卷 452-462页

作者： Xuejun Zhang Xiaolin Gui Feng Tian Si Yu Jian An Department of Computer Science and Technology the Shaanxi Province Key Laboratory of Computer NetworkXi’an Jiaotong University School of Electronic and Information Engineering Lanzhou Jiaotong University

The widespread use of Location-Based Services （LBSs）, which allows untrusted service providers to collect large quantities of information regarding users＇ locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms （LPPMs） have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary＇s estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.

关键词： location-based services Bayes decision estimator privacy metric adversarial mode

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Driving Android apps to trigger target API invocations based on activity and GUI filtering

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Science China(information Sciences) 2017年第7期60卷 262-264页

作者： Hongzhou YUE Yuqing ZHANG Wenjie WANG Qixu LIU State Key Laboratory of Integrated Services Networks Xidian University National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences

For an Android app tester,it is often needed to trigger the specific behaviors of an ***,most of the existing Android GUI exploration tools only pursue high code coverage,rather than triggering the specific behaviors of an app[1,2].It inevitably leads to the unnecessary time consumption in the exploration process of an app if the testers only care about some specific behaviors of this ***,methods of making a more targeted GUI exploration tool had been studied by

关键词： GUI API Driving Android apps to trigger target API invocations based on activity and GUI filtering

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A key management scheme for multicast based on layer 2 control

A key management scheme for multicast based on layer 2 contr...

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10th IEEE International Conference on computer and information Technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, 10th IEEE Int. Conf. Scalable Computing and Communications, ScalCom-2010

作者： Cao, Zheng Yin, Pengpeng Lu, Zhengjun School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network Technology Jiangsu Province Nanjing 210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing 210096 China

ISBN: (纸本)9780769541082

This paper presents a key management scheme for multicast based on layer 2 control, which includes multicast authentication, secure multicast forwarding tree management, key distribution when users are initialized and periodical key updating. This scheme offers a set of methods for key distribution and updating with MLD snooping mechanism and MLD report filtering on access switches. This method reduces the complexity of the key updating problem when members leave the group by layer 2 control and guarantees the efficiency and security of key management. Compared with other schemes, this scheme performs better on computation cost and communication cost than them and has acceptable storage cost. © 2010 IEEE.

关键词： Multicasting

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Tasks-Oriented Joint Resource Allocation Scheme for the Internet of Vehicles with Sensing, Communication and Computing integration

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China Communications 2023年第3期20卷 27-42页

作者： Jiujiu Chen Caili Guo Runtao Lin Chunyan Feng Beijing Laboratory of Advanced Information Networks Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Key Laboratory of Network System Construction and Integration Beijing University of Posts and TelecommunicationsBeijing 100876China

With the development of artificial intelligence(AI)and 5G technology,the integration of sensing,communication and computing in the Internet of Vehicles(Io V)is becoming a ***,the large amount of data transmission and the computing requirements of intelligent tasks lead to the complex resource management *** view of the above challenges,this paper proposes a tasks-oriented joint resource allocation scheme(TOJRAS)in the scenario of Io ***,this paper proposes a system model with sensing,communication,and computing integration for multiple intelligent tasks with different requirements in the Io ***,joint resource allocation problems for real-time tasks and delay-tolerant tasks in the Io V are constructed respectively,including communication,computing and caching ***,a distributed deep Q-network(DDQN)based algorithm is proposed to solve the optimization problems,and the convergence and complexity of the algorithm are ***,the experimental results based on real data sets verify the performance advantages of the proposed resource allocation scheme,compared to the existing *** exploration efficiency of our proposed DDQN-based algorithm is improved by at least about 5%,and our proposed resource allocation scheme improves the m AP performance by about 0.15 under resource constraints.

关键词： IoV resource allocation tasks-oriented communications sensing,communication and com-puting integration deep reinforcement learning

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iBole： A Hybrid Multi-Layer Architecture for Doctor Recommendation in Medical Social networks

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Journal of computer Science & Technology 2015年第5期30卷 1073-1081页

作者：宫继兵王立立孙胜涛彭思维 School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China

In this paper, we try to systematically study how to perform doctor recommendation in medical social net- works （MSNs）. Specifically, employing a real-world medical dataset as the source in our work, we propose iBole, a novel hybrid multi-layer architecture, to solve this problem. First, we mine doctor-patient relationships/ties via a time-constraint probability factor graph model （TPFG）. Second, we extract network features for ranking nodes. Finally, we propose RWR- Model, a doctor recommendation model via the random walk with restart method. Our real-world experiments validate the effectiveness of the proposed methods. Experimental results show that we obtain good accuracy in mining doctor-patient relationships from the network, and the doctor recommendation performance is better than that of the baseline algorithms： traditional Ranking SVM （RSVM） and the individual doctor recommendation model （IDR-Model）. The results of our RWR-Model are more reasonable and satisfactory than those of the baseline approaches.

关键词： doctor recommendation architecture random walk with restart doctor-patient tie mining time-constraintprobability factor graph model medical social network

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