State Key Laboratory of Networking and Switching Technology

As the great prevalence of various Internet of Things (IoT) terminals, how to solve the problem of isolated information among different IoT platforms attracts attention from both academia and industry. It is necessary to establish a trusted access system to achieve secure authentication and collaborative sharing. Therefore, this article proposes a distributed and trusted authentication system based on blockchain and edge computing, aiming to improve authentication efficiency. This system consists of physical network layer, blockchain edge layer and blockchain network layer. Through the blockchain network, an optimized practical Byzantine fault tolerance consensus algorithm is designed to construct a consortium blockchain for storing authentication data and logs. It guarantees trusted authentication and achieves activity traceability of terminals. Furthermore, edge computing is applied in blockchain edge nodes, to provide name resolution and edge authentication service based on smart contracts. Meanwhile, an asymmetric cryptography is designed, to prevent connection between nodes and terminals from being attacked. And a caching strategy based on edge computing is proposed to improve hit ratio. Our proposed authentication mechanism is evaluated with respect to communication and computation costs. Simulation results show that the caching strategy outperforms existing edge computing strategies by 6%-12% in terms of average delay, and 8%-14% in hit ratio.

In this note, a constrained consensus problem is studied for multi-agent systems in unbalanced networks in the presence of communication delays. Here each agent needs to lie in a closed convex constraint set while reaching a consensus. The communication graphs are directed, dynamically changing, and not necessarily balanced and only the union of the graphs is assumed to be strongly connected among each time interval of a certain bounded length. The analysis is performed based on an undelayed equivalent system that is composed of a linear main body and an error auxiliary. To tackle the loss of symmetry caused by unbalanced graphs and communication delays, a novel approach is proposed. The idea is to estimate the distance from each agent to the intersection set of all agents' constraint sets based on the properties of the projection on convex sets so as to show consensus convergence by contradiction. It is shown that the error auxiliary vanishes as time evolves and the linear main body converges to a vector with an exponential rate as a separate system. It is also shown that the communication delays do not affect the consensus stability and constrained consensus is reached even if the communication delays are arbitrarily bounded. Finally, a numerical example is included to illustrate the obtained theoretical results.

Participatory sensing is now becoming more popular and has shown its great potential in various applications. It was originally proposed to recruit ordinary citizens to collect and share massive amounts of sensory data using their portable smart devices. By attracting participants and paying rewards as a return, incentive mechanisms play an important role to guarantee a stable scale of participants and to improve the accuracy/coverage/timeliness of the sensing results. Along this direction, a considerable amount of research activities have been conducted recently, ranging from experimental studies to theoretical solutions and practical applications, aiming at providing more comprehensive incentive procedures and/or protecting benefits of different system stakeholders. To this end, this paper surveys the literature over the period of 2004-2014 from the state of the art of theoretical frameworks, applications and system implementations, and experimental studies of the incentive strategies used in participatory sensing by providing up-to-date research in the literature. We also point out future directions of incentive strategies used in participatory sensing.

Mobile devices equipped with multiple network interfaces can increase their throughput by making use of parallel transmissions over multiple paths and bandwidth aggregation, enabled by the stream control transport protocol (SCTP). However, the different bandwidth and delay of the multiple paths will determine data to be received out of order and in the absence of related mechanisms to correct this, serious application-level performance degradations will occur. This paper proposes a novel quality-aware adaptive concurrent multipath transfer solution (CMT-QA) that utilizes SCTP for FTP-like data transmission and real-time video delivery in wireless heterogeneous networks. CMT-QA monitors and analyses regularly each path's data handling capability and makes data delivery adaptation decisions to select the qualified paths for concurrent data transfer. CMT-QA includes a series of mechanisms to distribute data chunks over multiple paths intelligently and control the data traffic rate of each path independently. CMT-QA's goal is to mitigate the out-of-order data reception by reducing the reordering delay and unnecessary fast retransmissions. CMT-QA can effectively differentiate between different types of packet loss to avoid unreasonable congestion window adjustments for retransmissions. Simulations show how CMT-QA outperforms existing solutions in terms of performance and quality of service.

The Software-Defined Network (SDN) approach decouples control and forwarding planes. Such separation introduces reliability design issues of the SDN control network, since disconnection between the control and forwarding planes may lead to severe packet loss and performance degradation. This paper addresses the problem of placing controllers in SDNs, so as to maximize the reliability of control networks. After presenting a metric to characterize the reliability of SDN control networks, several placement algorithms are developed. We evaluate these algorithms and further quantify the impact of controller number on the reliability of control networks using real topologies. Our approach can significantly improve the reliability of SDN control networks without introducing unacceptable latencies.

By using increasingly popular smartphones, participatory sensing systems can collect comprehensive sensory data to retrieve context-aware information for different applications (or sensing tasks). However, new challenges arise when selecting the most appropriate participants when considering their different incentive requirements, associated sensing capabilities, and uncontrollable mobility, to best satisfy the quality-of-information (QoI) requirements of multiple concurrent tasks with different budget constraints. This paper proposes a multitask-oriented participant selection strategy called “DPS,” which is used to tackle the aforementioned challenges, where three key design elements are proposed. First is the QoI satisfaction metric, where the required QoI metrics of the collected data are quantified in terms of data granularity and quantity. Second is the multitask-orientated QoI optimization problem for participant selection, where task budgets are treated as the constraint, and the goal is to select a minimum subset of participants to best provide the QoI satisfaction metrics for all tasks. The optimization problem is then converted to a nonlinear knapsack problem and is solved by our proposed dynamic participant selection (DPS) strategy. Third is how to compute the expected amount of collected data by all (candidate) participants, where a probability-based movement model is proposed to facilitate such computation. Real and extensive trace-based simulations show that, given the same budget, the proposed participant selection strategy can achieve far better QoI satisfactions for all tasks than selecting participants randomly or through the reversed-auction-based approaches.

In cloud data centers, different mapping relationships between virtual machines (VMs) and physical machines (PMs) cause different resource utilization, therefore, how to place VMs on PMs to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. The existing VM placement schemes are to optimize physical server resources utilization or network resources utilization, but few of them focuses on optimizing multiple resources utilization simultaneously. To address the issue, this paper proposes a VM placement scheme meeting multiple resource constraints, such as the physical server size (CPU, memory, storage, bandwidth, etc.) and network link capacity to improve resource utilization and reduce both the number of active physical servers and network elements so as to finally reduce energy consumption. Since VM placement problem is abstracted as a combination of bin packing problem and quadratic assignment problem, which is also known as a classic combinatorial optimization and NP-hard problem, we design a novel greedy algorithm by combining minimum cut with the best-fit, and the simulations show that our solution achieves better results.

With the ever-increasing complexity of networks, fine-grained network monitoring enables better network reliability and timely feedback control. The In-band Network Telemetry (INT) allows cost-effective network monitoring by encapsulating device-internal states into probe packets. However, INT only specifies an underlying device-level primitive while how to achieve network-wide traffic monitoring remains undefined. In this work, we propose INT-path, a network-wide telemetry framework, by decoupling the system into a routing mechanism and a routing path generation policy. Specifically, we embed source routing into INT probes to allow specifying the route the probe packet takes through the network. Above the mechanism, we develop an Euler trail-based path planning policy to generate non-overlapped INT paths that cover the entire network with a minimum path number. Besides, an exhaustive analysis of algorithm's run-time complexity is also provided. INT-path can “encode” the network-wide traffic status into a series of “bitmap images”, transforming network troubleshooting into pattern recognition problems. INT-path is very suitable for deployment in data center networks thanks to their symmetric network topologies.

Energy consumption has become a major concern to the widespread deployment of cloud data centers. The growing importance for parallel applications in the cloud introduces significant challenges in reducing the power consumption drawn by the hosted servers. In this paper, we propose an enhanced energy-efficient scheduling (EES) algorithm to reduce energy consumption while meeting the performance-based service level agreement (SLA). Since slacking non-critical jobs can achieve significant power saving, we exploit the slack room and allocate them in a global manner in our schedule. Using random generated and real-life application workflows, our results demonstrate that EES is able to reduce considerable energy consumption while still meeting SLA.

With global coverage as well as ultra-low latency, the Low-Earth-Orbit (LEO) satellite constellation is regarded as an ideal complement to the terrestrial network infrastructure. One technical issue in LEO satellite networks is efficient and resilient routing. Considering the periodic topology changes, straightforwardly leveraging terrestrial routing protocols, such as OSPF, will incur endless route convergence, consuming expensive inter-satellite link bandwidth. Prior work proposes several snapshot-based routing approaches, which either require to store a sequence of routing table snapshots in limited satellite memory, or have to maintain frequent interaction with the ground stations. In this work, we propose OPSPF, a novel routing protocol dedicated to LEO satellite networks. OPSPF takes advantage of the regularity of the constellation and conducts periodic route calculation for instantaneous routing table generation, which well handles the regular topology changes. Moreover, OPSPF proposes an on-demand dynamic routing mechanism, dedicated to the irregular topology changes caused by link failure/recovery. Evaluation shows, compared with OSPF, OPSPF has zero route convergence overhead during regular topology changes and 57% reduction of the communication overhead and 82% reduction of the route convergence time during irregular topology changes.

In Named Data Networking (NDN), packet forwarding decisions rely upon lookup operations on variable-length hierarchical names instead of fixed-length host addresses. This pivotal feature introduces new challenges in the deployment of NDN at the Internet scale. In this letter, a novel Name Lookup engine with Adaptive Prefix Bloom filter (NLAPB) is conceived, in which each NDN name/prefix is split into B-prefix followed by T-suffix. B-prefix is matched by Bloom filters whereas T-suffix is processed by the small-scale trie. The length of B-prefixes (and T-suffixes) is dynamically throttled based on their popularity in order to accelerate the lookup. Experimental results show that: (i) NLAPB is able to lower the false positive rate with respect to a lookup entirely based on Bloom filters; (ii) it decreases the memory requirements with respect to a trie-based approach; (iii) it reduces processing time with respect to both them.

Epidemic threshold has always been a very hot topic for studying epidemic dynamics on complex networks. The previous studies have provided different theoretical predictions of the epidemic threshold for the susceptible-infected-recovered (SIR) model, but the numerical verification of these theoretical predictions is still lacking. Considering that the large fluctuation of the outbreak size occurs near the epidemic threshold, we propose a novel numerical identification method of SIR epidemic threshold by analyzing the peak of the epidemic variability. Extensive experiments on synthetic and real-world networks demonstrate that the variability measure can successfully give the numerical threshold for the SIR model. The heterogeneous mean-field prediction agrees very well with the numerical threshold, except the case that the networks are disassortative, in which the quenched mean-field prediction is relatively close to the numerical threshold. Moreover, the numerical method presented is also suitable for the susceptible-infected-susceptible model. This work helps to verify the theoretical analysis of epidemic threshold and would promote further studies on the phase transition of epidemic dynamics.

The study of the foraging behavior of group animals (especially ants) is of practical ecological importance, but it also contributes to the development of widely applicable optimization problem-solving techniques. Biologists have discovered that single ants exhibit low-dimensional deterministic-chaotic activities. However, the influences of the nest, ants' physical abilities, and ants' knowledge (or experience) on foraging behavior have received relatively little attention in studies of the collective behavior of ants. This paper provides new insights into basic mechanisms of effective foraging for social insects or group animals that have a home. We propose that the whole foraging process of ants is controlled by three successive strategies: hunting, homing, and path building. A mathematical model is developed to study this complex scheme. We show that the transition from chaotic to periodic regimes observed in our model results from an optimization scheme for group animals with a home. According to our investigation, the behavior of such insects is not represented by random but rather deterministic walks (as generated by deterministic dynamical systems, e.g., by maps) in a random environment: the animals use their intelligence and experience to guide them. The more knowledge an ant has, the higher its foraging efficiency is. When young insects join the collective to forage with old and middle-aged ants, it benefits the whole colony in the long run. The resulting strategy can even be optimal.

A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.

Accurate spatial-temporal prediction is a fundamental building block of many real-world applications such as traffic scheduling and management, environment policy making, and public safety. This problem is still challenging due to nonlinear, complicated, and dynamic spatial-temporal dependencies. To address these challenges, we propose a novel embedded spatial-temporal network (ESTNet), which extracts efficient features to model the dynamic correlations and then exploits three-dimension convolution to synchronously model the spatial-temporal dependencies. Specifically, we propose multi-range graph convolution networks for extracting multi-scale static features from the fine-grained road network. Meanwhile, dynamic features are extracted from real-time traffic using a gated recurrent unit network. These features can be applied to identify the dynamic and flexible correlations among sensors and make it possible to exploit a three-dimension convolution unit (3DCon) to simultaneously model the spatial-temporal dependencies. Furthermore, we propose a residual network by stacking multiple 3DCon to capture the nonlinear and complicated dependencies. The effectiveness and superiority of ESTNet are verified on two real-world datasets, and experiments show ESTNet outperforms the state-of-the-art with a significant margin. The code and models will be publicly available.

End-to-end network monitoring is essential to ensure transmission quality for Internet applications. However, in large-scale networks, full-mesh measurement of network performance between all transmission pairs is infeasible. As a newly emerging sparsity representation technique, matrix completion allows the recovery of a low-rank matrix using only a small number of random samples. Existing schemes often fix the number of samples assuming the rank of the matrix is known, while the data features thus the matrix rank vary over time. In this paper, we propose to exploit the matrix completion techniques to derive the end-to-end network performance among all node pairs by only measuring a small subset of end-to-end paths. To address the challenge of rank change in the practical system, we propose a sequential and information-based adaptive sampling scheme, along with a novel sampling stopping condition. Our scheme is based only on the data observed without relying on the reconstruction method or the knowledge on the sparsity of unknown data. We have performed extensive simulations based on real-world trace data, and the results demonstrate that our scheme can significantly reduce the measurement cost while ensuring high accuracy in obtaining the whole network performance data.

Leveraging the development of mobile communication technologies and increased capabilities of mobile devices, mobile multimedia services have set new trends. To support high-quality Video-on-Demand (VoD) in mobile wireless networks, using virtual community-based approaches to balance the efficiency of content sharing and maintenance cost of performance-aware solutions has attracted increasing research interest. In this paper, we propose a novel Performance-aware Mobile Community-based VoD streaming solution over vehicular ad hoc networks (PMCV). PMCV relies on a newly designed mobile community detection scheme and an innovative community member management mechanism. The former employs a novel fuzzy ant-inspired clustering algorithm and an innovative mobility similarity estimation model to group together the mobile users with similar behavior in terms of playback and movement into mobile communities. The latter introduces the role and task of members, member join and leave, collaborative store and search for resources, and replacement of a broker member. Simulation-based testing shows how PMCV outperforms another state-of-the-art solution in terms of performance.

Future mobile broadband networks are characterized with high data rate and improved coverage, which will enable real-time video multicast and broadcast services. Scalable video coding (SVC), combined with adaptive modulation and coding schemes (MCS) and wireless multicast, provides an excellent solution for streaming video to heterogeneous wireless devices. By choosing different MCSs for different video layers, SVC can provide good video quality to users in good channel conditions while maintaining basic video quality for users in bad channel conditions. A key issue to apply SVC to wireless multicast streaming is to choose appropriate MCS for each video layer and to determine the optimal resource allocation among multiple video sessions. We formulate this problem as total utility maximization, subject to the constraint of available radio resources. We prove that the formulated problem is NP-hard and propose an optimal, two-step dynamic programming solution with pseudo-polynomial time complexity. Simulation results show that our algorithm offers significant improvement on the video quality over a naive algorithm and an adapted greedy algorithm, especially in the scenarios with multiple real video sequences and limited radio resources.

Dedicated device-based and app-based augmented reality (AR) solutions have inherent limitations regarding cross-platform, pervasive AR application provisioning. Web-based AR (web AR), a promising lightweight and cross-platform approach to AR, is gaining increasing attention owing to its extensive application areas. However, for computationally intensive AR applications, the weak computational efficiency of current web browsers seriously hampers applications of web AR on a large scale. The browser + cloud approach suffers from the high-latency dilemma. Now, with the emerging 5G networks, mobile edge computing (MEC) promises to greatly reduce network latency (even to 1 ms) by deploying applications at the network edge closer to users, which provides an opportunity for performance improvement of web AR. In this article, the authors envision that the application of MEC has the potential to bring web AR into a new era. Specifically, an MEC-oriented web AR solution is first proposed, followed by the design and deployment details. The authors also discuss future directions aimed at using MEC to tackle the performance issues of web AR in 3G, 4G, and 5G networks.

Content-Centric Networking (CCN) is an entirely novel networking paradigm, in which packet forwarding relies upon lookup operations on content names directly instead of fixed-length host addresses. The unique features of CCN names, i.e., variable length, huge cardinality, and hierarchical structure, introduce new challenges that could hinder the deployment of such a new architecture at the Internet scale. In this paper, we make an in-depth study of characteristics of large-scale CCN names, and propose a simple yet efficient CCN-customized name lookup engine (named by TB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> F), which capitalizes the strengths of Tree-Bitmap (TB) and Bloom-Filter (BF) mechanisms, while counteracts their main limitations. To this end, TB2F splits CCN prefix into a constant size T-segment and a variable length B-segment with a relative short length, which are treated using TB and BF, respectively. Furthermore, an optimal length of the T-segment is found to improve the lookup efficiency. Experimental comparisons with respect to the reference Name Prefix-Trie and Bloom-Hash have been also carried out. The results show that TB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> F properly configured has good scalability and efficiency by (i) speeding up lookup operations and reducing the false positive rate with respect to Bloom-Hash; (ii) requiring less memory than Name Prefix-Trie; (iii) achieving a low overhead in updating operations in the large scale case.