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Identifying an unfamiliar caller’s profession is important to protect citizens’ personal safety and property. Owing to limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraud phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for identification of multiple professions. We observed that web service requests issued from users’ mobile phones may exhibit their applications preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users’ mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concern. In this paper, we develop CPFinder —- a system which exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.
Jiaquan Zhang; Hui Chen; XiaoMing Yao; XiaoMing Fu. CPFinder: Finding an Unknown Caller’s Profession from Anonymized Mobile Phone Data. Digital Communications and Networks 2021, 1 .
AMA StyleJiaquan Zhang, Hui Chen, XiaoMing Yao, XiaoMing Fu. CPFinder: Finding an Unknown Caller’s Profession from Anonymized Mobile Phone Data. Digital Communications and Networks. 2021; ():1.
Chicago/Turabian StyleJiaquan Zhang; Hui Chen; XiaoMing Yao; XiaoMing Fu. 2021. "CPFinder: Finding an Unknown Caller’s Profession from Anonymized Mobile Phone Data." Digital Communications and Networks , no. : 1.
The Bulk Synchronous Parallel (BSP) paradigm is gaining tremendous importance recently due to the popularity of computations as distributed machine learning and graph computation. In a typical BSP job, multiple workers concurrently conduct iterative computations, where frequent synchronization is required. Therefore, the workers should be scheduled simultaneously and their placement on different computing devices could significantly affect the performance. Simply retrofitting a traditional scheduling discipline will likely not yield the desired performance due to the unique characteristics of BSP jobs. In this work, we derive SPIN, a novel scheduling designed for BSP jobs with placement-sensitive execution to minimize the makespan of all jobs. We first prove the problem approximation hardness and then present how SPIN solves it with a rounding-based randomized approximation approach. Our analysis indicates SPIN achieves a good performance guarantee efficiently. Moreover, SPIN is robust against misestimation of job execution time by theoretically bounding its negative impact. We implement SPIN on a production-trace driven testbed with 40 GPUs. Our extensive experiments show that SPIN can reduce the job makespan and the average job completion time by up to 3x and 4.68x, respectively. SPIN also demonstrates better robustness to execution time misestimation compared with state-of-the-art heuristic baselines.
Zhenhua Han; Haisheng Tan; Shaofeng H.-C. Jiang; Wanli Cao; XiaoMing Fu; Lan Zhang; Francis C. M. Lau. SPIN: BSP Job Scheduling With Placement-Sensitive Execution. IEEE/ACM Transactions on Networking 2021, PP, 1 -14.
AMA StyleZhenhua Han, Haisheng Tan, Shaofeng H.-C. Jiang, Wanli Cao, XiaoMing Fu, Lan Zhang, Francis C. M. Lau. SPIN: BSP Job Scheduling With Placement-Sensitive Execution. IEEE/ACM Transactions on Networking. 2021; PP (99):1-14.
Chicago/Turabian StyleZhenhua Han; Haisheng Tan; Shaofeng H.-C. Jiang; Wanli Cao; XiaoMing Fu; Lan Zhang; Francis C. M. Lau. 2021. "SPIN: BSP Job Scheduling With Placement-Sensitive Execution." IEEE/ACM Transactions on Networking PP, no. 99: 1-14.
Understanding commuters’ behavior and influencing factors becomes more and more important every day. With the steady increase of the number of commuters, commuter traffic becomes a major bottleneck for many cities. Commuter behavior consequently plays an increasingly important role in city and transport planning and policy making. Although prior studies investigated a variety of potential factors influencing commuting decisions, most of them are constrained by the data scale in terms of limited time duration, space and number of commuters under investigation, largely owing to their dependence on questionnaires or survey panel data; as such only small sets of features can be explored and no predictions of commuter numbers have been made, to the best of our knowledge. To fill this gap, we collected inter-city commuting data in Germany between 1994 and 2018, and, along with other data sources, analyzed the influence of GDP, housing and the labor market on the decision to commute. Our analysis suggests that the access to employment opportunities, housing price, income and the distribution of the location’s industry sectors are important factors in commuting decisions. In addition, different age, gender and income groups have different commuting patterns. We employed several machine learning algorithms to predict the commuter number using the identified related features with reasonably good accuracy.
Hui Chen; Sven Voigt; XiaoMing Fu. Data-Driven Analysis on Inter-City Commuting Decisions in Germany. Sustainability 2021, 13, 6320 .
AMA StyleHui Chen, Sven Voigt, XiaoMing Fu. Data-Driven Analysis on Inter-City Commuting Decisions in Germany. Sustainability. 2021; 13 (11):6320.
Chicago/Turabian StyleHui Chen; Sven Voigt; XiaoMing Fu. 2021. "Data-Driven Analysis on Inter-City Commuting Decisions in Germany." Sustainability 13, no. 11: 6320.
Dating apps have gained tremendous popularity during the past decade. Compared with traditional offline dating means, dating apps ease the process of partner finding significantly. While bringing convenience to hundreds of millions of users, dating apps are vulnerable to become targets of adversaries. In this work, we focus on malicious user detection in dating apps. Existing methods overlooked the signals hidden in the textual information of user interactions, particularly the interplay of temporal-spatial behaviors and textual information, leading to a limited performance. To tackle this, we propose DatingSec, a novel malicious user detection system for dating apps. Concretely, DatingSec leverages long short-term memory neural networks (LSTM) and an attentive module to capture the interplay of users' temporal-spatial behaviors and user-generated textual content. We evaluate DatingSec on a real-world dataset collected from Momo, a widely used dating app with more than 180 million users. Experimental results show that DatingSec outperforms state-of-the-art methods and achieves an F1-score of 0.857 and AUC of 0.940.
Xinlei He; Qingyuan Gong; Yang Chen; Yang Zhang; Xin Wang; XiaoMing Fu. DatingSec: Detecting Malicious Accounts in Dating Apps Using a Content-Based Attention Network. IEEE Transactions on Dependable and Secure Computing 2021, PP, 1 -1.
AMA StyleXinlei He, Qingyuan Gong, Yang Chen, Yang Zhang, Xin Wang, XiaoMing Fu. DatingSec: Detecting Malicious Accounts in Dating Apps Using a Content-Based Attention Network. IEEE Transactions on Dependable and Secure Computing. 2021; PP (99):1-1.
Chicago/Turabian StyleXinlei He; Qingyuan Gong; Yang Chen; Yang Zhang; Xin Wang; XiaoMing Fu. 2021. "DatingSec: Detecting Malicious Accounts in Dating Apps Using a Content-Based Attention Network." IEEE Transactions on Dependable and Secure Computing PP, no. 99: 1-1.
Coreness is an important index to reflect the cohesiveness of a graph. The problems of core computation in static graphs and core update in dynamic graphs, known as the core decomposition and core maintenance problems respectively, have been extensively studied in previous work. However, most of these work focus on unweighted graphs. Considering that graphs are weighted in a lot of realistic applications, it is indispensable to extend the coreness to weighted graphs and devise efficient algorithms for weighted core decomposition and weighted core maintenance. In this work, we present a new definition of weighted coreness for vertices in a weighted graph, by taking into account the weights of vertices, which makes the coreness in unweighted graph be a special case. We propose efficient algorithms for both weighted core decomposition and weighted core maintenance problems. The coreness of vertices can be computed in linear time by the proposed decomposition algorithm, while the proposed core maintenance algorithm can process multiple-edge insertions/deletions simultaneously, which greatly reduces the core update time. Comprehensive experiments on both realistic networks and temporal graphs exhibit our algorithms are efficient and scalable.
Wei Zhou; Hong Huang; Qiang-Sheng Hua; Dongxiao Yu; Hai Jin; XiaoMing Fu. Core decomposition and maintenance in weighted graph. World Wide Web 2021, 24, 541 -561.
AMA StyleWei Zhou, Hong Huang, Qiang-Sheng Hua, Dongxiao Yu, Hai Jin, XiaoMing Fu. Core decomposition and maintenance in weighted graph. World Wide Web. 2021; 24 (2):541-561.
Chicago/Turabian StyleWei Zhou; Hong Huang; Qiang-Sheng Hua; Dongxiao Yu; Hai Jin; XiaoMing Fu. 2021. "Core decomposition and maintenance in weighted graph." World Wide Web 24, no. 2: 541-561.
Network Function Virtualization (NFV) is emerging as an attractive solution, which can transform complex network functions from the dedicated hardware implementations into software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are placed in a predefined order, which is also known as the Service Function Chaining (SFC). While most of the existing work on traffic routing in NFV networks assume deterministic link delay and bandwidth, the real-life network usually behaves in a stochastic manner, due to e.g., inaccurate data, expired exchanged information, insufficient estimation to the network. Motivated by this, we consider the stochastic NFV networks, where the link bandwidth and delay are assumed to be random variables and their cumulative distribution functions are known. We first study how to calculate the delay and bandwidth value in an SFC such that their realizing probabilities are satisfied. Subsequently, we formally define the traffic routing problem in stochastic NFV networks and prove it is NP-hard. We present an exact solution and a tunable heuristic to solve this problem. The proposed heuristic is a sampling-based algorithm, and it leverages the Tunable Accuracy Multiple Constraints Routing Algorithm (TAMCRA) to find a multi-constraint path for each adjacent VNF pair. It dynamically adjusts the link weights as well as delay and bandwidth realizing probability constraint after finding the path for each VNF pair so that the cumulated probabilities will not violate the specified values. Finally, we evaluate the performance of the proposed algorithms via extensive simulations.
Song Yang; Fan Li; Stojan Trajanovski; XiaoMing Fu. Traffic routing in stochastic network function virtualization networks. Journal of Network and Computer Applications 2020, 169, 102765 .
AMA StyleSong Yang, Fan Li, Stojan Trajanovski, XiaoMing Fu. Traffic routing in stochastic network function virtualization networks. Journal of Network and Computer Applications. 2020; 169 ():102765.
Chicago/Turabian StyleSong Yang; Fan Li; Stojan Trajanovski; XiaoMing Fu. 2020. "Traffic routing in stochastic network function virtualization networks." Journal of Network and Computer Applications 169, no. : 102765.
Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms complex network functions from dedicated hardware implementations to software instances running in a virtualized environment. In this survey, we provide an overview of recent advances of resource allocation in NFV. We generalize and analyze four representative resource allocation problems, namely, (1) the VNF Placement and Traffic Routing problem, (2) VNF Placement problem, (3) Traffic Routing problem in NFV, and (4) the VNF Redeployment and Consolidation problem. After that, we study the delay calculation models and VNF protection (availability) models in NFV resource allocation, which are two important Quality of Service (QoS) parameters. Subsequently, we classify and summarize the representative work for solving the VPTR problem and the VRC problem by considering various QoS parameters (e.g., cost, delay, reliability and energy) and different scenarios (e.g., edge cloud, online provisioning and distributed provisioning). Finally, we conclude our survey with a short discussion on the state-of-the-art of literatures and emerging topics in the related field, and highlight areas where we expect high potential for future research.
Song Yang; Fan Li; Stojan Trajanovski; Ramin Yahyapour; XiaoMing Fu. Recent Advances of Resource Allocation in Network Function Virtualization. IEEE Transactions on Parallel and Distributed Systems 2020, 32, 295 -314.
AMA StyleSong Yang, Fan Li, Stojan Trajanovski, Ramin Yahyapour, XiaoMing Fu. Recent Advances of Resource Allocation in Network Function Virtualization. IEEE Transactions on Parallel and Distributed Systems. 2020; 32 (2):295-314.
Chicago/Turabian StyleSong Yang; Fan Li; Stojan Trajanovski; Ramin Yahyapour; XiaoMing Fu. 2020. "Recent Advances of Resource Allocation in Network Function Virtualization." IEEE Transactions on Parallel and Distributed Systems 32, no. 2: 295-314.
Heterogeneous networks raise the challenge on ubiquitous connections among heterogeneous devices and networking protocols. As a promising approach to meet this challenge, Information-centric networking (ICN) offers a new communication paradigm which can conceal the heterogeneity of underlying networks. However, it suffers from the problem of segmented cached chunks, which results in low throughput caused by high frequency of switching among different nodes holding chunk copies, and the large Interest packet overhead (IPO). We conduct the experiments and observe that 1) data chunks are cached in a distributed manner with ICN, and 2) range-based data retrieval can reduce switch-over frequency and IPO. Based on these observations, we propose the adaptive retrieval with consecutive caching (ARCC) scheme, which is composed of the consecutive data chunk caching (ConCaching) and adaptive data chunk retrieval (ACUR). ARCC bridges the gap between caching and transport, where intermediate nodes on transmission path only cache the consecutive data chunks with the size above a threshold, while users can adjust the range of requested data chunks to maximize the throughput. The intensive simulations show that the proposed mechanisms can achieve substantial reduction in IPO and switch-over frequency and higher throughput compared with the existing pipeline mechanism in ICN.
Ruidong Li; Kazuhisa Matsuzono; Hitoshi Asaeda; XiaoMing Fu. Achieving High Throughput for Heterogeneous Networks With Consecutive Caching and Adaptive Retrieval. IEEE Transactions on Network Science and Engineering 2020, 7, 2443 -2455.
AMA StyleRuidong Li, Kazuhisa Matsuzono, Hitoshi Asaeda, XiaoMing Fu. Achieving High Throughput for Heterogeneous Networks With Consecutive Caching and Adaptive Retrieval. IEEE Transactions on Network Science and Engineering. 2020; 7 (4):2443-2455.
Chicago/Turabian StyleRuidong Li; Kazuhisa Matsuzono; Hitoshi Asaeda; XiaoMing Fu. 2020. "Achieving High Throughput for Heterogeneous Networks With Consecutive Caching and Adaptive Retrieval." IEEE Transactions on Network Science and Engineering 7, no. 4: 2443-2455.
Cloud Radio Access Network (C-RAN) is a promising 5G network architecture by establishing Baseband Units (BBU) pools to perform baseband processing functionalities and deploying Remote Radio Heads (RRH) for wireless signal transmission and reception. Mobile Edge Computing (MEC) offers a way to shorten the service delay by building small-scale cloud infrastructures at the network edge. By co-locating BBU pool with edge cloud at the so-called BBU node, we can take full advantages of C-RAN and MEC for better spectrum utilization and delay-guaranteed services. In this paper, we first study how to allocate each user’s task to BBU node and find the path from his/her accessing RRH node to the BBU node such that the maximum service delay among all the requests is minimized. We then consider this problem with survivability concerns, which is to use both primary and backup BBU nodes to issue the request such that the primary path and backup path are link-disjoint. We analyze the complexities of these two problems and prove they are NP-hard in general. Subsequently, we devise a randomized approximation algorithm and an efficient heuristic to solve the considered problems, respectively. The simulation results show that the proposed algorithms outperform two benchmark heuristics in terms of acceptance ratio and maximum service delay.
Song Yang; Nan He; Fan Li; Stojan Trajanovski; Xu Chen; Yu Wang; XiaoMing Fu. Survivable Task Allocation in Cloud Radio Access Networks With Mobile-Edge Computing. IEEE Internet of Things Journal 2020, 8, 1095 -1108.
AMA StyleSong Yang, Nan He, Fan Li, Stojan Trajanovski, Xu Chen, Yu Wang, XiaoMing Fu. Survivable Task Allocation in Cloud Radio Access Networks With Mobile-Edge Computing. IEEE Internet of Things Journal. 2020; 8 (2):1095-1108.
Chicago/Turabian StyleSong Yang; Nan He; Fan Li; Stojan Trajanovski; Xu Chen; Yu Wang; XiaoMing Fu. 2020. "Survivable Task Allocation in Cloud Radio Access Networks With Mobile-Edge Computing." IEEE Internet of Things Journal 8, no. 2: 1095-1108.
Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this paper, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack many-to-one short flows. We have implemented DCMPTCP in both the Linux kernel and ns-3 simulator. Our comprehensive testbed experiments and simulations show that DCMPTCP outperforms MPTCP in both 1Gbps testbed, and 10Gbps large-scale simulation network.
Enhuan Dong; XiaoMing Fu; Mingwei Xu; Yuan Yang. Low-Cost Datacenter Load Balancing With Multipath Transport and Top-of-Rack Switches. IEEE Transactions on Parallel and Distributed Systems 2020, 31, 2232 -2247.
AMA StyleEnhuan Dong, XiaoMing Fu, Mingwei Xu, Yuan Yang. Low-Cost Datacenter Load Balancing With Multipath Transport and Top-of-Rack Switches. IEEE Transactions on Parallel and Distributed Systems. 2020; 31 (10):2232-2247.
Chicago/Turabian StyleEnhuan Dong; XiaoMing Fu; Mingwei Xu; Yuan Yang. 2020. "Low-Cost Datacenter Load Balancing With Multipath Transport and Top-of-Rack Switches." IEEE Transactions on Parallel and Distributed Systems 31, no. 10: 2232-2247.
Recently, the issue of offloading cellular data while reducing the duplicated cellular transmission has gained more and more attention. Several studies have shown that sharing contents through Device-to-Device (D2D) to offload traffic to local connections nearby can offer better performance for mobile users. Nevertheless, most existing proposals are somewhat confined to small-scale data sets or limited feature dimensions, relied on unconsolidated hypotheses and measurements of data sets. This paper presents a prior work of large-scale measurements on 3.56 TBytes of real-world data sets, which contain D2D content sharing activities from a popular D2D sharing application (APP). We conduct a comprehensive analysis of multi-dimensional features, including time series, structural properties, meeting dynamics, location relationship, and propagation tree. Our analysis reveals that (i) D2D sharing makes the hops between users shorter (in 3 or 4 degrees), (ii) the maximum spreading distance of content dissemination is 27 hops, (iii) we provide a new evidence of log-normal distribution of all user encounters (named meeting dynamics in this paper) based the fit of inter-TX time, Inter-Content Time (ICT) and Contact Time, (iv) online factor (O) and social factor (S) demonstrate the largest positive correlation and indicate that the two factors have high linear correlation. Finally, we analyze the correlations among all the impact factors by Pearson coefficient, principal component analysis, and latent semantic analysis, respectively. Results reveal that online factor (O) and social factor (S) have a high correlation, especially both of them have a great effect on D2D sharing activities.
Xiaofei Wang; Chenyang Wang; Xu Chen; XiaoMing Fu; Jinyoung Han; Xin Wang. Measurement and analysis on large-scale offline mobile app dissemination over device-to-device sharing in mobile social networks. World Wide Web 2020, 23, 2363 -2389.
AMA StyleXiaofei Wang, Chenyang Wang, Xu Chen, XiaoMing Fu, Jinyoung Han, Xin Wang. Measurement and analysis on large-scale offline mobile app dissemination over device-to-device sharing in mobile social networks. World Wide Web. 2020; 23 (4):2363-2389.
Chicago/Turabian StyleXiaofei Wang; Chenyang Wang; Xu Chen; XiaoMing Fu; Jinyoung Han; Xin Wang. 2020. "Measurement and analysis on large-scale offline mobile app dissemination over device-to-device sharing in mobile social networks." World Wide Web 23, no. 4: 2363-2389.
Ensuring high availability (HA) for software-based networks is a critical design feature that will help the adoption of software-based network functions (NFs) in production networks. It is important for NFs to avoid outages and maintain mission-critical operations. However, HA support for NFs on the critical data path can result in unacceptable performance degradation. We present REINFORCE, an integrated framework to support efficient resiliency for NF service chains. REINFORCE includes timely failure detection and consistent failover mechanisms. REINFORCE replicates state to standby NFs (local and remote) while enforcing correctness. It minimizes the number of state transfers by exploiting the concept of external synchrony, and leverages opportunistic batching and multi-buffering to optimize performance. Experimental results show that, even at line-rate packet processing (10 Gbps), REINFORCE achieves chain-level failover across servers in a LAN within 10ms, incurring less than 10% performance overhead, and adds average latency only ~400 μs, with a worst-case latency of less than 1ms. REINFORCE also recovers from software failures within the same node in less than 100 μs, incurring less than 1% performance overhead and adds less than 5 μs latency during normal operation.
Sameer G. Kulkarni; Guyue Liu; K. K. Ramakrishnan; Mayutan Arumaithurai; Timothy Wood; XiaoMing Fu. REINFORCE: Achieving Efficient Failure Resiliency for Network Function Virtualization-Based Services. IEEE/ACM Transactions on Networking 2020, 28, 695 -708.
AMA StyleSameer G. Kulkarni, Guyue Liu, K. K. Ramakrishnan, Mayutan Arumaithurai, Timothy Wood, XiaoMing Fu. REINFORCE: Achieving Efficient Failure Resiliency for Network Function Virtualization-Based Services. IEEE/ACM Transactions on Networking. 2020; 28 (2):695-708.
Chicago/Turabian StyleSameer G. Kulkarni; Guyue Liu; K. K. Ramakrishnan; Mayutan Arumaithurai; Timothy Wood; XiaoMing Fu. 2020. "REINFORCE: Achieving Efficient Failure Resiliency for Network Function Virtualization-Based Services." IEEE/ACM Transactions on Networking 28, no. 2: 695-708.
Managing Network Function (NF) service chains requires careful system resource management. We propose NFVnice, a user space NF scheduling and service chain management framework to provide fair, efficient and dynamic resource scheduling capabilities on Network Function Virtualization (NFV) platforms. The NFVnice framework monitors load on a service chain at high frequency (1000Hz) and employs backpressure to shed load early in the service chain, thereby preventing wasted work. Borrowing concepts such as rate proportional scheduling from hardware packet schedulers, CPU shares are computed by accounting for heterogeneous packet processing costs of NFs, I/O, and traffic arrival characteristics. By leveraging cgroups, a user space process scheduling abstraction exposed by the operating system, NFVnice is capable of controlling when network functions should be scheduled. NFVnice improves NF performance by complementing the capabilities of the OS scheduler but without requiring changes to the OS's scheduling mechanisms. Our controlled experiments show that NFVnice provides the appropriate rate-cost proportional fair share of CPU to NFs and significantly improves NF performance (throughput and latency) by reducing wasted work across an NF chain, compared to using the default OS scheduler. NFVnice achieves this even for heterogeneous NFs with vastly different computational costs and for heterogeneous workloads.
Sameer G. Kulkarni; Wei Zhang; Jinho Hwang; Shriram Rajagopalan; K. K. Ramakrishnan; Timothy Wood; Mayutan Arumaithurai; XiaoMing Fu. NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains. IEEE/ACM Transactions on Networking 2020, 28, 639 -652.
AMA StyleSameer G. Kulkarni, Wei Zhang, Jinho Hwang, Shriram Rajagopalan, K. K. Ramakrishnan, Timothy Wood, Mayutan Arumaithurai, XiaoMing Fu. NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains. IEEE/ACM Transactions on Networking. 2020; 28 (2):639-652.
Chicago/Turabian StyleSameer G. Kulkarni; Wei Zhang; Jinho Hwang; Shriram Rajagopalan; K. K. Ramakrishnan; Timothy Wood; Mayutan Arumaithurai; XiaoMing Fu. 2020. "NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains." IEEE/ACM Transactions on Networking 28, no. 2: 639-652.
With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many collision avoidance strategies have emerged in recent years, but the dynamic and complex nature of driving environment poses a challenge to develop robust collision avoidance algorithms. Therefore, in this paper, we propose a decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging a hierarchical architecture we develop an algorithm named Co-DDPG to efficiently train autonomous vehicles. Through a security abiding channel, the autonomous vehicles distribute their driving policies. We use the relative distances obtained by the opponent sensors to build the VANET instead of locations, which ensures the vehicle's location privacy. With a leader-follower architecture and parameter distribution, RACE accelerates the learning of optimal policies and efficiently utilizes the remaining resources. We implement the RACE framework in the widely used TORCS simulator and conduct various experiments to measure the performance of RACE. Evaluations show that RACE quickly learns optimal driving policies and effectively avoids collisions. Moreover, RACE also scales smoothly with varying number of participating vehicles. We further compared RACE with existing autonomous driving systems and show that RACE outperforms them by experiencing 65% less collisions in the training process and exhibits improved performance under varying vehicle density.
Yali Yuan; Robert Tasik; Sripriya Srikant Adhatarao; Yachao Yuan; Zheli Liu; XiaoMing Fu. RACE: Reinforced Cooperative Autonomous Vehicle Collision Avoidance. IEEE Transactions on Vehicular Technology 2020, 69, 9279 -9291.
AMA StyleYali Yuan, Robert Tasik, Sripriya Srikant Adhatarao, Yachao Yuan, Zheli Liu, XiaoMing Fu. RACE: Reinforced Cooperative Autonomous Vehicle Collision Avoidance. IEEE Transactions on Vehicular Technology. 2020; 69 (9):9279-9291.
Chicago/Turabian StyleYali Yuan; Robert Tasik; Sripriya Srikant Adhatarao; Yachao Yuan; Zheli Liu; XiaoMing Fu. 2020. "RACE: Reinforced Cooperative Autonomous Vehicle Collision Avoidance." IEEE Transactions on Vehicular Technology 69, no. 9: 9279-9291.
With the popularity of smart mobile terminals and advances in wireless communication and positioning technologies, Geo-Social Networks (GSNs), which combine location awareness and social service functions, have become increasingly prevalent. The increasing amount of user and location information in GSNs makes the information overload phenomenon more and more serious. Although massive user-generated data brings convenience to users’ social and travel activities, it also causes certain trouble for their daily life. In this context, users are expecting smarter mobile applications, so that the location information can be employed to perceive their surrounding environment intelligently and further mine their behavior patterns in GSNs, which ultimately provides personalized location-based services for users. Therefore, research on user location prediction comes into existence and has received extensive and in-depth attention from researchers. Through systematically analyzing the location data carried by user check-ins and comments, user location prediction can mine various user behavior patterns and personal preferences, thus determining the visiting location of users in the future. Research on user location prediction is still in the ascendant and it has become an important topic of common concern in both academia and industry. This survey takes Geo-social networking data as the focal point to elaborate the recent progress in user location prediction from multiple aspects such as problem categories, data sources, feature extraction, mathematical models and evaluation metrics. Besides, the difficulties to be studied and the future developmental trends of user location prediction are discussed.
Shuai Xu; XiaoMing Fu; Jiuxin Cao; Bo Liu; Zhixiao Wang. Survey on user location prediction based on geo-social networking data. World Wide Web 2020, 23, 1621 -1664.
AMA StyleShuai Xu, XiaoMing Fu, Jiuxin Cao, Bo Liu, Zhixiao Wang. Survey on user location prediction based on geo-social networking data. World Wide Web. 2020; 23 (3):1621-1664.
Chicago/Turabian StyleShuai Xu; XiaoMing Fu; Jiuxin Cao; Bo Liu; Zhixiao Wang. 2020. "Survey on user location prediction based on geo-social networking data." World Wide Web 23, no. 3: 1621-1664.
Segment Routing is a source routing based architecture that provides an opportunity to include a list of instructions called segments in the packet headers. The segments may allow the inclusion of detours for responding to Traffic Engineering needs or Service Function Chaining implementations. Even though there is an increasing interest towards enhancing and adopting Segment Routing, the administrators are still burdened with the task of manually write and maintain the segment lists. Such type of management presents several challenges ranging from error-prone configurations to increased response time for network updates. In this paper, we present a Segment Routing management framework named Busoni, which automates and simplifies the process of segments lists management. Additionally, we also provide programming tools to compose and manage Segment Routing policies that operate efficiently, even under multi-tenancy environments. Using different use cases, we show the programming capabilities offered by our framework.
Osamah L. Barakat; Pier Luigi Ventre; Stefano Salsano; XiaoMing Fu. Busoni: Policy Composition and Northbound Interface for IPv6 Segment Routing Networks. 2019 IEEE 27th International Conference on Network Protocols (ICNP) 2019, 1 -4.
AMA StyleOsamah L. Barakat, Pier Luigi Ventre, Stefano Salsano, XiaoMing Fu. Busoni: Policy Composition and Northbound Interface for IPv6 Segment Routing Networks. 2019 IEEE 27th International Conference on Network Protocols (ICNP). 2019; ():1-4.
Chicago/Turabian StyleOsamah L. Barakat; Pier Luigi Ventre; Stefano Salsano; XiaoMing Fu. 2019. "Busoni: Policy Composition and Northbound Interface for IPv6 Segment Routing Networks." 2019 IEEE 27th International Conference on Network Protocols (ICNP) , no. : 1-4.
Road accidents and traffic congestion are two critical problems for global transport systems. Connected vehicles (CV) and automated vehicles (AV) are among the most heavily researched and promising automotive technologies to reduce road accidents and improve road efficiency. However, both AV and CV technologies have inherent shortcomings, for example, line of sight sensing limitation of AV sensors and the dependency of high penetration rate for CVs. In this paper we present a cooperative connected intelligent vehicles (CAV) framework. It is motivated by the observation that vehicles are increasingly intelligent with various levels of autonomous functionalities. The vehicles intelligence is boosted by more sensing and computing resources. These sensor and computing resources of CAV vehicles and the transport infrastructure could be shared and exploited. With resource sharing and cooperation CAVs can have comprehensive perception of driving environments, and novel cooperative applications can be developed to improve road safety and efficiency (RSE). The key feature of the cooperative CAV system is the cooperation within and across the key players in the road transport systems and across system layers. For example, the various levels of cooperation include cooperative sensing, cooperative RSE applications and cooperation among the vehicles and among the vehicles and infrastructure. We will present the potentials that could be brought by cooperative CAV, the roadmap for research and development, the preliminary research results and open issues.
Jianhua He; Zuoyin Tang; XiaoMing Fu; Supeng Leng; Fan Wu; Kaisheng Huang; Jianye Huang; Jie Zhang; Yan Zhang; Andrew Radford; Laura Li; Zhiliang Xiong. Cooperative Connected Autonomous Vehicles (CAV): Research, Applications and Challenges. 2019 IEEE 27th International Conference on Network Protocols (ICNP) 2019, 1 -6.
AMA StyleJianhua He, Zuoyin Tang, XiaoMing Fu, Supeng Leng, Fan Wu, Kaisheng Huang, Jianye Huang, Jie Zhang, Yan Zhang, Andrew Radford, Laura Li, Zhiliang Xiong. Cooperative Connected Autonomous Vehicles (CAV): Research, Applications and Challenges. 2019 IEEE 27th International Conference on Network Protocols (ICNP). 2019; ():1-6.
Chicago/Turabian StyleJianhua He; Zuoyin Tang; XiaoMing Fu; Supeng Leng; Fan Wu; Kaisheng Huang; Jianye Huang; Jie Zhang; Yan Zhang; Andrew Radford; Laura Li; Zhiliang Xiong. 2019. "Cooperative Connected Autonomous Vehicles (CAV): Research, Applications and Challenges." 2019 IEEE 27th International Conference on Network Protocols (ICNP) , no. : 1-6.
Mobile Edge Computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures at the network edge, which are in close proximity to the end users. Moreover, Network Function Virtualization (NFV) has been an emerging technology that transforms from traditional dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. Service Function Chaining (SFC) is defined as a chain-ordered set of placed VNFs that handles the traffic of the delivery and control of a specific application. NFV therefore allows to allocate network resources in a more scalable and elastic manner, offer a more efficient and agile management and operation mechanism for network functions and hence can largely reduce the overall costs in MEC. In this paper, we study the problem of how to place VNFs on edge and public clouds and route the traffic among adjacent VNF pairs, such that the maximum link load ratio is minimized and each user's requested delay is satisfied. We consider this problem for both totally ordered SFCs and partially ordered SFCs. We prove that this problem is NP-hard, even for the special case when only one VNF is requested. We subsequently propose an efficient randomized rounding approximation algorithm to solve this problem. Extensive simulation results show that the proposed approximation algorithm can achieve close-to-optimal performance in terms of acceptance ratio and maximum link load ratio.
Song Yang; Fan Li; Stojan Trajanovski; Xu Chen; Yu Wang; XiaoMing Fu. Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds. IEEE Transactions on Mobile Computing 2019, 20, 445 -459.
AMA StyleSong Yang, Fan Li, Stojan Trajanovski, Xu Chen, Yu Wang, XiaoMing Fu. Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds. IEEE Transactions on Mobile Computing. 2019; 20 (2):445-459.
Chicago/Turabian StyleSong Yang; Fan Li; Stojan Trajanovski; Xu Chen; Yu Wang; XiaoMing Fu. 2019. "Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds." IEEE Transactions on Mobile Computing 20, no. 2: 445-459.
Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms from dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order, and it is also known as the Service Function Chaining (SFC). Considering that the delay and resiliency are two important Service Level Agreements (SLA) in a NFV service, in this paper, we first investigate how to quantitatively model the traversing delay of a flow in both totally ordered and partially ordered SFCs. Subsequently, we study how to calculate the VNF placement availability mathematically for both unprotected and protected SFCs. After that, we study the delay-sensitive Virtual Network Function (VNF) placement and routing problem with and without resiliency concerns. We prove that this problem is NP-hard under two cases. We subsequently propose an exact Integer Nonlinear Programming formulation and an efficient heuristic for this problem in each case. Finally, we evaluate the proposed algorithms in terms of acceptance ratio, average number of used nodes and total running time via extensive simulations.
Song Yang; Fan Li; Ramin Yahyapour; XiaoMing Fu. Delay-Sensitive and Availability-Aware Virtual Network Function Scheduling for NFV. IEEE Transactions on Services Computing 2019, PP, 1 -1.
AMA StyleSong Yang, Fan Li, Ramin Yahyapour, XiaoMing Fu. Delay-Sensitive and Availability-Aware Virtual Network Function Scheduling for NFV. IEEE Transactions on Services Computing. 2019; PP (99):1-1.
Chicago/Turabian StyleSong Yang; Fan Li; Ramin Yahyapour; XiaoMing Fu. 2019. "Delay-Sensitive and Availability-Aware Virtual Network Function Scheduling for NFV." IEEE Transactions on Services Computing PP, no. 99: 1-1.
Computational social science has integrated social science theories and methodology with big data analysis. It has opened a number of new topics for big data analysis and enabled qualitative and quantitative sociological research to provide the ground truth for testing the results of data mining. At the same time, threads of evidence obtained by data mining can inform the development of theory and thereby guide the construction of predictive models to infer and explain more phenomena. Using the example of the Internet data of China’s venture capital industry, this paper shows the triadic dialogue among data mining, sociological theory, and predictive models and forms a methodology of big data analysis guided by sociological theories.
Jar-Der Luo; Jifan Liu; Kunhao Yang; XiaoMing Fu. Big data research guided by sociological theory: a triadic dialogue among big data analysis, theory, and predictive models. The Journal of Chinese Sociology 2019, 6, 11 .
AMA StyleJar-Der Luo, Jifan Liu, Kunhao Yang, XiaoMing Fu. Big data research guided by sociological theory: a triadic dialogue among big data analysis, theory, and predictive models. The Journal of Chinese Sociology. 2019; 6 (1):11.
Chicago/Turabian StyleJar-Der Luo; Jifan Liu; Kunhao Yang; XiaoMing Fu. 2019. "Big data research guided by sociological theory: a triadic dialogue among big data analysis, theory, and predictive models." The Journal of Chinese Sociology 6, no. 1: 11.