Service-differentiated QoS routing based on ant colony optimisation for named data networking Rui Hou, Lang Zhang, Yong Zheng, Yuzhou Chang, Bing Li, Tao Huang & Jiangtao Luo Peer-to-Peer Networking and Applications ISSN 1936-6442 Peer-to-Peer Netw. Appl. DOI 10.1007/s12083-018-0669-6
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Author's personal copy Peer-to-Peer Networking and Applications https://doi.org/10.1007/s12083-018-0669-6
Service-differentiated QoS routing based on ant colony optimisation for named data networking Rui Hou 1 & Lang Zhang 1 & Yong Zheng 1 & Yuzhou Chang 1 & Bing Li 2 & Tao Huang 3 & Jiangtao Luo 4 Received: 12 March 2018 / Accepted: 20 July 2018 # Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract Named data networking (NDN) is an emerging network architecture for serving content-centric applications, which are intended to support diverse services that require various quality of service (QoS) levels. In this paper, an algorithm based on ant colony optimisation—the so-called service-differentiated QoS routing algorithm (SDQR)—is proposed for service-differentiated routing of different types of services in NDN. SDQR adds a control layer on top of NDN to manipulate the underlying forwarding information base (FIB). The FIB defines an evaluation matrix to achieve differentiated updates on pheromone concentrations for different types of services. Simulation results showed that SDQR achieves service-differentiated routing for different types of services with less delay and higher throughput than several conventional approaches. Keywords Content-centric networking . Named data networking . Ant colony optimisation . Service-differentiated routing
1 Introduction With the rapid development of the Internet, the current hostcentric communication model primarily serves content-centric applications. Content-centric services now constitute the majority of network services. Users no longer focus on where the content is stored; rather, they focus on the content itself as well as the speed, quality, mobility, and security of content retrieval and transmission [1–5]. The current TCP/IP network architecture retrieves and transmits content based on IP addresses. This approach is inefficient in adapting to changes in the upper application layer. Content delivery networks (CDNs) [6], peer-to-peer (P2P) technology
* Rui Hou
[email protected] 1
College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China
2
Normal College, Shenzhen University, Shenzhen 518055, China
3
State Key Laboratory of Networking and Switching Technology, Beijing University of Post and Telecommunications, Beijing 100876, China
4
Electronic Information and Networking Research Institute, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
[7, 8], and other technologies were developed to address this issue. However, the interaction between their distributed components incurs a high cost for managing and operating networks. Furthermore, these technologies form a new evolving paradigm in which IP networks are changing into contentcentric networks. At the forefront of this paradigm is named data networking (NDN) [9, 10]. The NDN design assumes hierarchically structured names for naming the content, separating the physical storage address from the content itself, and routing and forwarding content based on the content name. Internet services include traditional File Transfer Protocol (FTP), E-mail, and Web services, as well as (P2P), streaming media, and emerging business networks. The 3rd Generation Partnership Project (3GPP) [11] classifies services into session-based services, streaming media services, interactive services, and background services, as shown in Table 1. The most concerning issue in network optimisation is to develop a means to improve the network transmission efficiency, while satisfying quality of service (QoS) requirements, achieving load balancing, and avoiding network congestion [12–15]. However, different types of services have different network QoS requirements. For example, transmission of real-time services, such as voice over Internet protocol (VoIP), requires a low delay, whereas FTP has large bandwidth requirements and is only minimally affected by delay. To guarantee differentiated QoS for different types of services, each service must be classified and forwarded according to its own optimal path.
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Service classification in 3GPP
Service type
Examples
QoS requirement
Session-based services
Videophone
Real-time services • delay (high)
Streaming media services
VoIP
• bandwidth (low) Real-time services • delay (high) • bandwidth (low)
Interactive services
Web service
Non-real-time services • delay (low) • bandwidth (moderate)
Background services
E-mail, FTP
Best-effort services • delay (low) • bandwidth (high)
Accordingly, NDN can carry different types of services and provide differentiated QoS. The main contribution of this paper is presentation of an effective QoS routing solution for different types of services in NDN. Unlike previous work on QoS routing in NDN, the proposed solution can efficiently explore an optimal path for different types of service traffic. It thus can provide better QoS routing than existing solutions for NDN.
2 Related work Routing is critical in NDN because it directly affects the NDN fault-tolerance performance in terms of the probability of successful packet delivery [16, 17]. Three forwarding approaches comprise the original NDN proposal. The simplest approach randomly selects a face in a forwarding information base (FIB) to forward an interest packet. However, this approach cannot guarantee that a subscriber obtains stable and optimal network performance. The second approach simultaneously sends an interest packet to each face of an FIB. This approach potentially reduces the required time; however, it increases the network load. The third approach selects a smart face in an FIB found in accordance with an intelligent evolutionary algorithm, such as ant colony optimisation (ACO), to forward interest packets. This approach not only achieves good network load balance, but it also guarantees that subscribers will obtain stable and optimal network performance. Khan et al. [18] proposed a QoS-aware path selection strategy that minimised the individual packet delay for real-time traffic and determined the highest bandwidth path for bandwidth intensive traffic. However, it failed to solve the multiconstrained QoS routing selection problem. Li et al. [19, 20] proposed another QoS-aware routing algorithm called the greedy ant colony forwarding algorithm (GACF). This method divides the network into multiple domains, which causes a loss
in global perspective. Shanbhag et al. [21] proposed a method called services over content-centric routing (SoCCeR), which is based on ACO. SoCCeR adds a control layer to the top of the content-centric networking (CCN) architecture to manipulate the underlying FIB, thereby enabling paths with light traffic loads to be selected for quickly providing responses to the dynamic network status. Huang et al. [22] proposed an ACObased routing strategy that enables the cost and delay of paths to quickly converge to a lower level and improve the data successful transmission rate. Eymann et al. [23] proposed a probabilistic routing mechanism based on ACO to support data stream transmission on multiple links to achieve higher throughput than transmission on a single link. Kerrouche et al. [24] proposed a new QoS-aware packet forwarding strategy named the ant colony based QoS-aware forwarding strategy (AC-QoS-FS). It fully leverages forward packets and backward packets to probe real-time network QoS parameters to update the priority of the interface forwarding for finding an optimal path for interest packets. Huang et al. [25] proposed a routing optimisation consisting of genetic algorithm and ACO (GAAC) for CCN. GAAC is based on the distributed caching mechanism of the SoCCeR algorithm and uses an adaptive pheromone updating formula to avoid redundant retrieval. It can obtain an effective local optimal solution, accelerate convergence, and reduce delay. However, the mentioned ACObased CCN routing strategies cannot provide differentiated QoS for different types of services. In this paper, an ACO-based service-differentiated QoS routing algorithm (SDQR) is proposed. The main advantages of SDQR can be summarised as follows: (1) A control layer on top of NDN is proposed in SDQR to manipulate the underlying FIB. It defines an evaluation matrix to achieve a distinguished update on the pheromone concentration for different types of services in which the formats of interest packets and data packets are improved. In addition, a state transition rule and pheromone update rule are proposed to support QoS. (2) SDQR can find optimal paths according to the service type of the requesting interest packets, and it satisfies multi-constrained QoS requirements, i.e. bandwidth and delay constraints. (3) We present the solution design. We evaluate SDQR through simulations and compare it with several classic QoS routing algorithms. QoS routing is a widely studied topic in the ICN field and has been a research focus for several years. However, to the best of our knowledge, almost no previous works focused on ACO itself or the multi-constrained routing issue. In this paper, the proposed SDQR overcomes this limitation of previous work, and it can effectively provide QoS-supported servicedifferentiated routing for multi-type traffic. It is believed that
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SDQR will provide better differentiated services for NDNbased, and even ICN-based, networks. The remainder of this paper is organised as follows. In Section 3, the SDQR routing strategy is described and an analytical model is developed. In Section 4, we outline the simulation conducted to evaluate the performance and verify the robustness and stability of our proposed algorithm. Finally, in Section 5 we present concluding remarks.
3 SDQR design To provide differentiated QoS for different types of services, it is necessary to classify the services in the network and then provide the corresponding QoS to the classified services. The Internet Engineering Task Force (IETF) provides a QoS solution—differentiated services (DiffServ)—which provides a differentiated service for IP networks. The DiffServ model defines three service types: expedited forwarding (EF), besteffort (BE), and assured forwarding (AF). The per-hop behaviour (PHB) determines the QoS requirements of these three types of services. EF guarantees that the delay, delay jitter, and packet loss rate are at a low level for real-time services such as sessions and streaming media. AF ensures the reliability of transmission for services, such as the enterprise virtual private network (VPN) and electronic commerce (e-commerce). BE provides high throughput for services such as FTP, E-mail, and other background businesses. The goal of the SDQR algorithm is to find a path with minimal delay for the EF service and to identify a path with the greatest available bandwidth for the AF/BE service in the network.
3.1 Node design In the proposed method, a control layer is added to the top of the NDN architecture to manipulate the underlying FIB. The control layer includes a pheromone Table (PT) table, which contains four columns of information: the content name, PHB, Faces, and τ. The content name corresponds to the prefix of content, and PHB divides the next-hop selection of the same content service into two behaviours: EF and AF/BE. Faces comprise a set of the next-hop forwarding interface, and τ represents the pheromone concentration of each forwarding interface. The performances of different PHB entries for the same content are different. Thus, the optimal next-hop forwarding interface of different PHB entries for the same content is different. For one content name, the PT table has the same set of forwarding interfaces for different PHB entries. However, the same forwarding interface under different PHB entries has its own pheromone concentrations to meet the QoS of different types of services.
To distinguish each type of services in NDN networks, interest packets and data packets are modified to enable them to calculate different paths for different business types. The improved interest packets contain the content name, type, type of service (ToS), timer stack (TS), and minimum bandwidth stack (MBS) fields. The content name records an information object’s name, which is required by a consumer. ToS records the differentiated services code point (DSCP) value of the service to distinguish the type of the interest packet. TS records the time consumed for path exploration. MBS records the minimum bandwidth of the path that the interest packets explore. The interest packets have a structure similar to that of the data packets, with the type field distinguishing between the two types of packets. In interest packets, Type = 0, whereas in data packets, Type = 1. The improved structure of an interest/data packet is shown in Fig. 1. As shown in Fig. 2, when a node receives an interest packet, it first compares the content name entries in the PT table with the prefix that the interest packet requires. It utilises the longest prefix match to determine the optimal entry. Suppose that the prefix required by the interest packet is google.com/video/ lalaland/part1.mp4. The optimal entry in PT is google.com/. After the longest prefix match, the corresponding forwarding interface set in the PT table is {0,1}. Then, it queries the DSCP value of the ToS field in the interest packet and maps it to the different PHBs according to the DSCP value. If the business type of the interest packet is EF, then the forwarding information is further defined in the EF entry. If the business type is BE/AF, it is defined in the BE/AF entry. Finally, the forwarding probability of each forwarding interface is calculated according to the pheromone concentration in the corresponding entry, and the next-hop forwarding interface is determined by the state transition rule (see Section 3.2). In the initial stage, the pheromone concentration of each forwarding interface in PT is equal, and the interest packet randomly selects the forwarding interface to explore the content.
3.2 State transition rule Upon receiving the interest packets, the node calculates the next-hop forwarding probability of each interface according to the pheromone concentration of the forwarding interface set corresponding to the service type. It finally determines the next-hop forwarding interface. In SDQR, the pheromone concentration of the forwarding interface j in the PT table on node i is denoted as τ stij , where s represents the content server, t represents the service type, and t ∈ {EF, AF, BE}. When node i receives the interest Content Name
Type
ToS
TS
MBS
Fig. 1 Structures of modified interest packets and data packets
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NDN Layer
Control Layer PT Content Name
τ
Faces
PHB
0
sEF g0
1
sEF g1
EF google.com/ 0 BE/AF ...
baidu.com/
sBE=AF
sEF Fig. 2 SDQR node design with (τ sEF g0 > τ g1 ,τ g1
1 ... sBE=AF
> τ g0
sBE / AF g0
google.com/
sBE / AF g1
baidu.com/
Pstij ¼
τ stij
st α
ð2Þ
∑k∈ F ti τ ik
Delay
θ
0 1 ...
The Pstij of each interface in F ti is then accumulated in turn. When ∑ j∈ F ti Pstij is greater than r0 for the first time, interface j corresponding to the last accumulated Pstij is selected as the next-hop forwarding interface.
Start
Initialization The time window updated as zero Yes
Update timeout? No NDN nodes receive packets
interest packet
Type of packet?
BE/AF
Output path
1
End Fig. 4 SDQR flow chart
data packet
Use the pheromone update rule to update the pheromone concentration
Use the state transition rule to choose the next-hop forwarding interface
Bandwidth Fig. 3 Evaluation matrix M
EF BE/AF ...
ð1Þ
α
EF
Face
)
where r is a random number between zero and one, and r0 is a constant between zero and one. In addition, F ti represents the set of forwarding interfaces to which the interest packets with a certain content name can be forwarded in node i for service type t. When r > r0, the roulette algorithm is used to select the next-hop forwarding interface to avoid the algorithm becoming trapped in local optima. It is necessary to calculate the proportion of the pheromone concentration of each interface in all interfaces. The proportion is denoted as Pstij , which signifies the proportion of the pheromone concentration of interface j on node i for service type t in all interfaces. It is defined as:
PHB
...
packets, the state transition rule to select the next-hop forwarding interface, j, is as follows. That is, when r ≤ r 0 , SDQR makes full use of the positive feedback mechanism of the ant colony algorithm packets and directly selects the interface with the highest pheromone concentration at the corresponding PHB as the next-hop forwarding interface. Specifically, j ¼ arg maxk∈ F ti f ðk Þ ¼ τ stik if r ≤ r0
FIB Content Name
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3.3 Pheromone update rule When the content server receives an interest packet, a corresponding data packet is generated. The data packets are forwarded along a path opposite that of the corresponding interest packets. The QoS requirements for different types of services are different. To achieve optimal path transmission for different types of services, it is necessary to achieve a distinguished update on the pheromone concentration. Evaluation matrix M is defined to represent the weight of network parameters (e.g. delay) when the pheromone concentration is updated under different types of services. As shown in Fig. 3, each column of M represents different types of services in the network, and each row of M represents different network parameters. On receiving a data packet, the node first determines the service type of the data packet, and then calculates the weight of the network parameters for the pheromone update according to M. As shown in Fig. 3, if the service type of the data packet is EF, when the pheromone concentration is updated, the weight of the path delay recorded in TS is M Delay ¼ θ, and the weight of the path EF bandwidth recorded in MBS is M Bandwidth ¼ 1=θ. EF The update rule of the data packet for the pheromone concentration is as follows: τ stij ←ð1−ρÞ⋅τ stij þ ρ⋅Δτ
ð3Þ
where ρ is the pheromone volatilisation factor. If ρ is small, the pheromone volatilisation is slow, and if ρ is large, the pheromone rapidly volatilises, which means that the importance of prior knowledge is weakened. Δτ signifies the incremental of the pheromone concentration and is expressed as:
Δτ ¼ M Delay ⋅ t
Pdelay MBS best þ M Bandwidth ⋅ bandwidth t TS Pbest
ð4Þ
where M Delay and M Bandwidth represent the weight of the t t delay and bandwidth for service type t when the pheromone concentration is updated, respectively. TS and MBS denote the delay and bottleneck bandwidth collected bandwidth by interest packets, and PDelay represent the best and P best delay and bottleneck bandwidth on the best path during the update round. The data packets are forwarded along a path opposite that of the corresponding interest packets. When the node receives
a data packet, for a specific content name with a certain service type (e.g. EF), the pheromone concentration of all the interfaces is updated as follows: (1) If the interest packets are forwarded by interface j, the pheromone concentration is updated according to (3) and (4). (2) If the interest packets are not forwarded by interface j, the pheromone concentration is updated according to (3) and (4), and TS and MBS are set to ∞ and 0, respectively. Because the interest packets have not been forwarded by interface j, the incremental pheromone concentration Δτ is 0.
3.4 Flow Chart and Pseudo-Code for SDQR The working procedure of SDQR is shown in Fig. 4. As shown in the flow chart, the pheromone concentration of each node should be initialized as one at the beginning of SDQR, and the updating time window should be reset as zero at each start of the update period. The pseudo-code of the SDQR initialization is presented as Algorithm 1. Algorithm 1 Initialization The main task of the interest packets in the SDQR algorithm is to look up the content server and record the network state information of the path. Meanwhile, they leave information at the node so that data packets can update the pheromone concentration of each node along that path. The algorithm that interest packets use to select the next-hop interface is outlined in Algorithm 2.
Algorithm 1. Initialization
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The main task of data packets in the SDQR algorithm is to update the pheromone concentration of each node and obtain the corresponding data. When the content server receives an interest packet, it encapsulates the corresponding data packets and copies the information recorded by TS and MBS in the interest packets. When the node receives data packets, the pheromone concentration is updated according to the pheromone update rule. The algorithm pseudo-code is outlined in Algorithm 3. Algorithm 3 Updating pheromone concentration by data packets
4 Simulations and results 4.1 Simple network experiment We utilised a simple network with fewer than ten nodes, as shown in Fig. 5, to test the effectiveness of SDQR. μ1 and μ2 were set as clients, and s was a content server. μ1 explored the
Fig. 5 Simple network used in the simulation
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Fig. 6 Number of packets forwarded by nodes r2 and r3
EF service and the name prefix of the interest packet was /prefix/EF/.μ1 sent 20 interest packets per second. μ2 explored the BE service. The name prefix of the interest packet was /prefix/BE/, with μ2 sending 20 interest packets per second while s provided data for the two service types. We used the ndnSIM [26] NDN simulator to perform the simulation and ran the latter for 1000 s. The number of packets forwarded by nodes r2 and r3 was counted. As depicted in Fig. 6, the packets forwarded by node r2 were mainly EF service packets; those forwarded by node r3 were mainly BE service packets. Analysis of the parameter settings of the network topology in Fig. 5 shows that the delay of path r1 → r2 → s is less than that of path r1 → r3 → r4 → s, whereas the minimum bandwidth of path r1 → r3 → r4 → s is higher than that of path r1 → r2 → s. Therefore, for the EF service, which must ensure a low delay, path r1 → r2 → s will achieve better performance. For the BE service, which must ensure a high throughput, path r1 → r3 → r4 → s will achieve better performance. Thus, the BE and EF services do not need to preempt the network resources, resulting in high performance for different service types.
4.2 Complex network experiment To verify the performance of SDQR, we additionally conducted simulations using a complex network. We utilised the topology of the United States National Natural Science
Fig. 7 Topology of NSFNET
Fig. 8 BE service delay for gradually increasing EF service traffic
Foundation (NSFNET), shown in Fig. 7. The NSFNET topology consists of 14 nodes and 21 links. Node 13 was selected as the content server, with the other nodes being SDQR nodes that requested data using the EF and BE services. The bandwidth of each link was set to 1000 Mbps, and the delay of each link was determined by the physical distance. The delay and throughput of the BE service for different routing algorithms with gradually increasing EF service traffic are shown in Figs. 8 and 9. The performance comparison was performed using the delay shortest path routing (DSPR), bandwidth-widest path routing (BWPR), SoCCeR, and SDQR algorithms. As evident in Fig. 8, with the increase in the EF service in the network, the delay of the BE service increases for the single path routing strategy. This is because the EF service has a higher priority; when the EF service increases, it preempts the network resources from the BE service, resulting in a greater delay for the BE service. For SDQR, the delay in the BE service varies marginally and remains at a low level. This is because SDQR provides differentiated QoS services for different types of services to avoid or decrease the preemption of network resources, thereby reducing the impact on the BE service.
Fig. 9 BE service throughput rate for gradually increasing EF service traffic
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Fig. 10 EF service delay under heavy network load
As shown in Fig. 9, as the EF service traffic increases, the BE service throughput rapidly decreases for the single path routing strategy, while the throughput rate is slower for the SDQR policy. This is because the EF service preempts the network resources of the BE service. The single path routing policy can only select one path for all services, and it cannot shunt traffic. The multipath routing policy can choose the best path among different types of services to transmit and fully employ the free network resources. In addition, we evaluated the performance of the EF service under a heavy network load. The delay and throughput of the EF service are shown in Figs. 10 and 11. It is evident in Fig. 10 that the SDQR algorithm delay is lower than that of BWPR. This is because the optimisation parameter used in BWPR is bandwidth. However, even though the optimisation parameter of DSPR and SoCCeR is also the delay, the SDQR algorithm delay is still lower than those of both DSPR and SoCCeR. Because heavy loads lead to network congestion, resulting in large delays, the SDQR algorithm chooses a different path for different types of services. Moreover, it reduces network congestion using traffic shunting, which maintains the delay at a low level. As depicted in Fig. 11, SDQR ensures a high throughput for the EF service. This is because the SDQR algorithm
Fig. 11 EF service throughput rate under heavy network load
Fig. 12 The network topology
carries out traffic shunting for different types of services and the EF service has a higher priority than the BE service, thus ensuring that the EF service is transmitted with a high throughput.
4.3 Robustness and stability verification To verify the robustness and stability of SDQR, we compare its performance with those of the three typical routing approaches used by current NDN projects [9]— shortest path routing (SR), maximum bandwidth path routing (MR), and random path routing (RR)—as well as SoCCeR. We define the packet return failure rate, Pfailure, to measure the proportion of failure returned data packets to the total sent interest packets under different QoS requirements for EF and BE services. Pfailure can be expressed as: Pfailure ¼
Count Interest −CountData CountInterest
ð5Þ
Fig. 13 Failure rate comparison of different routing protocols with Dmax = 18
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differentiated updates on pheromone concentration for different types of services. Results from a simulation conducted to compare the performances of SDQR, BWPR, DSPR, and SoCCeR indicated that SDQR achieved servicedifferentiated routing for different service types with less delay and higher throughput than the other algorithms. In addition, a simulation also conducted on the comparison of SDQR with several typical ICN/NDN routing strategies indicated that SDQR has strong robustness and stability. Acknowledgements This work was supported by the National Natural Science Foundation of China under Grant 60841001 and the Special Fund for Basic Scientific Research of Central Colleges, South-Central University for Nationalities, under Grant No. CZD18003. The authors thank all the reviewers for their useful comments. Fig. 14 Failure rate comparison of different routing protocols with Bmin = 100
where CountInterest and CountData are the number of interest packets sent and data packets received, respectively. We use the improved Slama algorithm [27] to randomly generate an NDN network topology that contains 20 CRs, as shown in Fig. 12. Each edge is represented by (b, d), where b and d denote the bandwidth and delay, respectively. To verify the stability of SDQR, we employed EF and BF service types of traffic, respectively. For simplicity, but not loss the generality, we set the maximum delay value to 18 Dmax = 18 for the EF service, while we set the minimum bandwidth value as 100 Bmin = 100 for the BF service. Fig. 13 provides the failure rate of the EF service under different routing protocols with Dmax = 18. It can be observed that the interest packet failure rates of SR, MR, RR, and SoCCeR are higher than that of SDQR at most iterations. This result is because SR and MR only consider a single metric. Moreover, the NDN CRs of RR randomly select Faces to forward interest packets and fail to satisfy the delay requirement. Meanwhile, Fig. 14 presents the failure rate of the BE service with Bmin = 100. It can be observed that, under the condition of bandwidth of no less than 100, although all the strategies have higher failure rates, SDQR still maintains a low level. In other words, SDQR has a lower failure rate increment than that of other routing protocols under the least bandwidth requirement condition for the BE service type. Therefore, it is indicated that SDQR has strong robustness and stability for multi-type ICN/NDN network scenarios.
5 Conclusions In this paper, we proposed the SDQR algorithm, which achieves service-differentiated routing for different types of services for NDN. To perform this function, SDQR adds a control layer on top of NDN to manipulate the underlying FIB. Moreover, it defines an evaluation matrix to achieve
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Rui Hou, received the B.M.E., B. L., and M. Eng. degrees in mechanics, law school, and physical electronics from Wuhan University, Wuhan, China, in 2000, 2003, and 2003, respectively, and the Ph.D degree in optical engineering from Huazhong Univers it y of Sc ience a nd Technology, Wuhan, China, in 2006. Between the years 2003 and 2006, he was a Research Staff Member in the Wuhan National Laboratory for Optoelectronics. He is currently a professor at the College of Computer Science, South-Central University for Nationalities, Wuhan, China. He was sponsored by the Chinese Scholarship Council as a National Senior Visiting Scholar, and conducted research in Lab. Signaling, Communications, and Networking, in the Dept. of Electrical & Computer Engineering, Colorado State University, Fort Collins, CO, USA from 2014-2015. He has authored
and co-authored over 80 papers in international journals and conferences. His main research interests include computer networks, WDM networks, and optical switching. Dr. Rui Hou is a Member of the IEEE, the IEICE, the OSA, and the CCF.
Lang Zhang, He is working toward M. Eng. at the College of Computer Science, SouthCentral University for Nationalities, Wuhan, China. He has authored and co-authored several international journal papers. His area of research is optimal routing and caching in information-centric networking.
Yong Zheng, He is currently working toward M. Eng. at the College of Computer Science, South-Central University for Nationalities, Wuhan, China. His area of research is artificial intelligence in information- centric networking.
Yuzhou Chang, received his M. Eng. degree from the College of Computer Science, South-Central University for Nationalities, Wuhan, China in 2017. He is currently working as a researcher in the Laboratory of Computer Network Technology at SouthCentral University for Nationalities. He has authored and co-authored over ten international journal papers. His area of research is information-centric networking .
Author's personal copy Peer-to-Peer Netw. Appl. Bing Li, received the B.E. degree and Ph.D. degree in computer science and technology from Tianjin University, Tianjin, China, in 2006 and 2010, respectively. She was a Visiting Research Scholar with Nanyang Technological University, Singapore, from 2008 to 2010. From 2012 to 2013, she was a Post-Doctoral Research Fellow with Concordia Un i ve rs it y, M on t rea l, QC , Canada. In 2014, she joined Shenzhen University, Shenzhen, China, as a Lecturer. Her research interests include secure multicast, network architecture, and traffic identification and measurement.
Tao Huang, received the B.S. degree in communication engineering from Nankai University, Tianjin, China, in 2002, and the M.S. and Ph.D. degrees in communication and information system from the Beijing University of Posts and Telecommunications, Beijing, China, in 2004 and 2007, respectively. He is currently a Professor with the Beijing University of Posts and Telecommunications.Hiscurrent research interests include network architecture, routing and forwarding, and network virtualization.
Jiangtao Luo, received his B.S. and PhD degrees from Nankai University and the Chinese Academy of Science in 1993 and 1998, respectively. Currently, he is a full professor, PhD supervisor and deputyDean of the Electronic Information and Networking Research Institute at Chongqing University of Posts & Telecommunications (CQUPT). His major research interests are network protocol analysis, network data mining, urban computing, and future internet architecture. He has published more than 100 papers and owned 21 patents in these fields. He was awarded the Chinese State Award of Scientific and Technological Progress in 2011, the Chongqing Provincial Award of Scientific and Technological Progress twice in 2010 and 2007, respectively, and the Chongqing Science and Technology Award for Youth in 2010. He has been selected as IEEE Senior Member since June 2015 and ACM member since Nov 2013.
