April 2009, 16(2): 84–88 www.sciencedirect.com/science/journal/10058885 www.buptjournal.cn/xben

The Journal of China Universities of Posts and Telecommunications

Network parameters awareness for routing discovery in autonomic optical Internet

JIN Jin ( ), WANG Hong-xiang, JI Yue-feng

Key Laboratory of Optical Communication and Lightwave Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract

The article proposes a network parameter-awareness (NPA) method and applies it to routing discovery algorithms in autonomic optical Internet. This NPA method can perceive the main parameters of the network, such as delay, jitter and traffic, which canrepresent the current situation of the network. And these parameters enable network to determine the appropriate nodes for routing discovery. The simulation results of evaluating performance of a network with NPA method and its routing applications show that the method and its applications in routing improve the performance of the network significantly with quality of service (QoS) guaranteed.

Keywords NPA, autonomic optical Internet, self-aware, routing, QoS

1 Introduction

The next-generation network information environment will be characterized by billions of nodes, possibly mobile ones, with embedded capabilities that make them sensitive, adaptive, responsive to their contexts, and capable of providing any service at any moment [1]. Autonomic communication is identified as a key driver of the next-generation network technology under ‘Communication Paradigms for 2020’ initiative [2]. The autonomic network, which will meet the requirements of future network, such as flexibility, reliability, scalability, high usability, and intelligent management, can offer better experience to terminal users.

Autonomic Internet mainly consists of several important modules: self-awareness, self-optimization, self-management, self-learning, and self-configuring, as shown in Fig. 1.

Fig. 1 Relationship between self-x characteristics in autonomic optical Internet

Received date: 19-06-2008 Corresponding author: JIN Jin, E-mail: jinjin0411@gmail.com DOI: 10.1016/S1005-8885(08)60208-3

Self-awareness is responsible for gathering information and characters of services and network parameters, such as the service type, QoS level, delay, jitter, traffic statistic features, etc. Self-learning and self-optimization modules will first process the parameters provided by self-awareness module using highly-intelligent algorithms, and then determine and give the proper operation list to self-management and self-configuration modules, which are able to control and configure equipments and devices respectively. The whole process does not require any administration or direct human intervention.

Self-awareness is the basis of autonomic Internet since it offers all the original NPA information to other modules. The self-awareness module comprises two parts: service-aware part and network-aware part. The former is aware of the service type and QoS level and sends these important characters to self-configuring modules to complete routing and switching. The latter perceives the network information environment. According to these network parameters, the self-configuring modules will select optimal routes between a user’s source node and its destination to optimize network performance. The whole autonomic Internet can be considered as a system where the self-awareness module is the input component. Thus, if self-awareness module is unable to gather sufficient information from the services, we cannot anticipate that the services and applications can be successfully delivered in autonomic Internet.

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Nowadays, the research on network self-awareness and its routing applications in autonomic optical Internet concentrates on adaptive path discovery using algorithms and adaptive routing [3–4]. However, the diversity of network service and network circumstance is not taken into account. All the algorithms are so sophisticated that they can only be applied in a given network or test-bed, while not suitable for a large scale network with so many kinds of multiple services. Moreover, the dynamic environment of networks are always neglected [4–5]. The network should be sensitive, adaptive, and responsive to contexts and services, thus monitoring and collecting parameters of the network plays an indispensable role in routing decision. Thus, choosing the routes with Qos guarantee is also needed to improve network performance.

Taking both changes and services into consideration, a novel parameter-aware measurement for routing is presented. Considering the uncertainty of network context, the network perceives the current state by means of parameters awareness. We utilize timestamp to perceive core node delay and detect packet (DP) for edge-to-edge delay awareness, and jitter is obtained by mathematical method furthermore. For different requirements of QoS, network parameters can be used to look for a better matched routing path. The network chooses different routing nodes with different QoS of service requesting different parameters. Although we apply the method in autonomic optical Internet, it can also be used in many other routing algorithms, protocols and networks.

The remainder of this article is organized as follows. In Sect. 2, a method is proposed to perceive network parameters in self-aware optical Internet. Sect. 3 describes an effective method to execute routing. In Sect. 4, the simulation results and performance analysis are presented. In Sect. 5, conclusions are highlighted on network-awareness.

2 Network parameters awareness

The transmission of services and changes of network have no regular rules to follow, and network has dynamic variations including both services and network itself at any instant. However, the process of network changes can be considered as a stationary process and makes slow changes in statistics, which indicates that awareness of current network is important for the next-step routing and other operations. Proper routing has the capability of optimizing network performance, keeping the network smooth, and giving users better experience.

QoS is the level of performance that is judged by an experienced user in a network, and it is an important consideration for many network applications such as real-time voice and video applications [6–7], which may have stringent requirements for the amount of loss, delay, or jitter they can

tolerate. Streaming services, such as Internet protocol television (IPTV), video on demand (VOD), need small jitter to ensure high quality of image. Real-time services such as Internet protocol (IP) telephone, voice over Internet protocol (VoIP) request strict short delay. These should be considered as constraints on routing.

Above all, three parameters including core node delay, edge-to-edge delay, and jitter to be aware for routing are proposed:

1) Core node delay is calculated by the time that is signed on the timestamp when the packets come into and leave the code node.

2) Edge-to-edge delay is obtained by DP. DP is set at the beginning of the packets, which are sent to another edge node, and sent back along the same path. Also, we can get the delay by the timestamps on them.

3) The jitter is defined as the variance value of the edge-to-edge delay. Hence, it is calculated by the edge-to-edge delay using mathematical method.

2.1 Core node delay

Real-time communications are most sensitive to delay by which the quality of real-time service is mainly affected. In the optical network, the core node delay is closely related to the core node, which is a crucial part in the network, in particular for real-time services. It is effective to improve the quality of real-time services by choosing a core node that has little traffic and low delay.

Core node delay is the delay of data packets passing by the core node, and awareness of this parameter is significant. The network collects the measurements of each node and selects the nodes that have smaller delay for routing. Besides the applications of routing, it is also beneficial for the cost of network architecture. In autonomic optical Internet, storage and retransmission need optical buffers. Owing to core node delay, the number of optical buffer devices and their capacity can be chosen to optimize network performance.

The method of being aware of core node delay is to use timestamp. When a data packet comes into the core node, we record the time and sign it in the timestamp. The same procedure will be done when it leaves the core node. Hence, the core node delay can be set as the difference between the two recorded times.

2.2 Edge-to-edge delay

The real-time services over IP are quick, economical and convenient. But packet networks were not designed for real-time applications such as VoIP, as they did not provide a dedicated end-to-end connection provided by circuit switched

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networks [8]. Thus, people cannot always be satisfied with the quality of the call that has greater end-to-end delay. To ensure the quality of calls, the network in this article perceives the edge-to-edge delay for the routing and link states.

Edge-to-edge delay is measured by sending probes across the network and observing the treatment we receive in terms of delay in delivery to the destination [9]. The delay can be used to evaluate the performance of the corresponding path. In this case, edge-to-edge jitter can be calculated. These parameters can be used for routing of different services.

In autonomic optical Internet, we adopt DP to get this parameter. The DP is set at the head of a data packet every ten packets. When the data packet reaches its destination, the node records its arriving time and signs it in the timestamp, and then it will be sent back to the source along the same path. When it arrives at the source node, the time is signed again. Thus, we obtain the edge-to-edge node delay by the margin of the two timestamps.

2.3 Jitter

In recent years, IPTV, VOD, and other new network services have been widely used. However, because of the current IP network transports services with the “best effort” model, it always leads to high jitter and cannot guarantee quality of these streaming services. Besides, streaming service is sensitive to jitter. Low jitter is desired for multimedia service and users request high quality of streaming service, and therefore awareness of jitter for routing is necessary.

The parameter jitter in the context of different applications has different definitions. In digital transmission system, jitter is defined as ‘short-term variations of the significant instants of a digital signal from their ideal positions in time’ (http://www.jitter.de/pdfextern/vecjitter.pdf). In this article, this view is adopted. We define the average delay that can guarantee the image clear as the ‘ideal position’, and the jitter means ‘short-term variation’. The variance Eq. (1) can be used to calculate the edge-to-edge jitter. iX is defined as random variable of edge-to-edge delay, and ( )iE X is the average value of edge-to-edge delay which expresses the delay ensured the image clear. Then, the variance ( )iD Xcan be calculated to represent the jitter that means the degree of deviation from an ideal location.

2( ) [( ( )) ]i i iD X E X E X (1) Based on the definition above, using Eq. (1), we can obtain

the precise and credible result, and this result is useful and accurate in the simulation.

Besides the parameters mentioned, the network can perceive other parameters if necessary such as payload, packet loss ratio, etc. These parameters play different roles in

the network to satisfy different requests.

3 Routing based on NPA

This article proposed a simple, feasible and efficient routing method for the changing network. The aim of the method is to make the network sensitive, adaptive, responsive to its environments and capable of offering user-oriented flexible services at any moment.

Our objective is to enable the relevant service not to be affected by sensitive related parameters to the utmost extent. Thus, the service will be routed and switched fast. The routing algorithm can be described as follows.

The network will use the awareness parameters above to select the appropriate route for different QoS services. In this article, we assume the QoS levels of real-time service and streaming service to be QoS1 and QoS2 respectively. And suppose that the rest services are the best effort (BE) services.

The routing algorithm is shown in Fig. 2

Fig. 2 Flowchart of the routing algorithm

First, the service-aware module of the network identifies the QoS of services.

Second, if it is the QoS1 service, the network examines the delay of core node and edge-to-edge delay and then selects the node with the smallest delay. If it is the QoS2 service, the network examines the jitter and edge-to-edge delay and then chooses the node with the lowest jitter and the smallest delay. Whereas the BE service need not choose the special nodes.

Ultimately, on this basis, the QoS1 service and QoS2 service choose the node as routing node, which has the smallest load. And subsequently the BE service finally chooses the node that has the smallest load.

If the services have contention in routing and switching, the high priority services such as real-time (QoS1) service and

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video (QoS2) service should be sent first. The algorithm should be used every time when the network

needs to choose route paths. And the node’s parameters should be multicast to the other nodes of the whole network.

4 Simulation results

This section gives evaluation of the network performance and service with the proposed method. All simulations are performed using the NS Network Simulator.

4.1 Evaluation scenarios

For simplicity, we perform the simulations on a multiservice autonomic optical network with three access nodes connected to four core nodes. The connection between access nodes and core nodes consists of four wavelengths with a capacity of 10 Gb/s. The network capacity is thus 40 Gb/s. We assume that the mean traffic of the network is 26.4 Gb/s, which corresponds to a mean load of 66%. The mean data rate of source is 10 Mb/s with a peak rate of 30 Mb/s. The topology is shown in Fig. 3.

Fig. 3 Topology of evaluation scenarios

The article considers three classes of service, QoS1, QoS2, and best effort (BE). 10% of the total traffic is of QoS1, whereas 30% is of QoS2 and 60% is of the BE. QoS1 has the highest traffic priority and the BE has the lowest one.

The incoming electronic packet sizes are as follows: 50 B for QoS1, 500 B for QoS2, and 1 500 B for the BE. These sizes are inspired from the internet traffic statistics (http://www.caida.org/analysis/AIX/plen_hist/).

We use two performance metrics for our analysis: the packet loss ratio at the intermediate nodes and the edge- to-edge delay of packets with different QoS levels.

The following cases are studied: Case A Traditional optical network with three QoS levels.

Case B Autonomic optical network with three QoS levels and traditional routing.

Case C Autonomic optical network with three QoS levels using the method described above.

4.2 Numerical results

We investigate the performance of the whole network in terms of packet loss ratio (PLR) and edge-to-edge delay. Figs. 4 and 5 show the network performance in Case B and Case C respectively. Figs. 6 and 7 show the network performance in Case A and Case C respectively. Case A and Case B have no comparability on the proposed issue in the article; hence, these two cases are not compared in this article. However, the result reveals that the performance of Case B is better than that of Case A since the autonomic optical network is more adaptive to the QoS level than the traditional optical network. Performance parameters are measured of the three classes of services: QoS1, QoS2, and BE.

Fig. 4 PLR in Case B vs. in Case C

Fig. 5 Edge-to-edge delay in Case B vs. in Case C

Fig. 4 illustrates the PLR in Case B and Case C. It shows that the routing application decreases the PLR from

41.5 10 to 58 10 for the QoS1 service, and from 32 10 to 49 10 for the QoS2 service, whereas increases

the PLR of the BE service. Because the higher QoS level

88 The Journal of China Universities of Posts and Telecommunications 2009

services have priority to choosing the nodes with better performance and lower load, their PLR are lower than the BE service. And the BE service has no priority to choosing better matched routing, thus its PLR is worse in the Case C than in the Case B. The fact shows that the routing application is better for QoS1 and QoS2 services than the traditional routing in autonomic optical network.

Fig. 5 shows the services’ edge-to-edge delay in Case B and Case C respectively. The routing algorithm reduces the delay of Qos1 service but others did not show any obvious changes or even a little increase. This is because the algorithm in which the QoS1 service is more sensitive than the other two services to edge-to-edge delay. The routing algorithm proposes that considering the delay can improve the performance of QoS1.

Fig. 6 shows the PLR in Case A and Case C respectively. It illustrates that the parameter awareness and routing application lower the packet loss ratio for the QoS1 and QoS2 services and have no prominent influence on the BE service. The higher QoS level services have priority to choosing the better routing in the whole network, thus their PLR declines. Furthermore, the BE service with no priority has no changes compared with the traditional optical network with three QoS. This fact reflects that parameter awareness and routing algorithm can improve the performance of the whole network compared with the traditional optical network.

Fig. 6 PLR in Case A vs. in Case C

Fig. 7 shows the services’ edge-to-edge delay in Case A and Case C. The edge-to-edge delay is 50.9 10 s in Case A. However, the routing algorithm reduces the delay of Qos1 service to 50% of its primary value. Whereas, others have no significant changes compared with the traditional optical network. Because the QoS1 service is the most delay sensitive, the others are relatively not very sensitive to this parameter. Hence, the performance of QoS1 service is improved. Figs. 6 and 7 illustrate that the parameter awareness and routing application improve the performance of both the network and services.

Fig. 7 Edge-to-edge delay in Case A vs. in Case C

5 Conclusions

In this article, we have discussed the network parameter awareness for routing discovery in autonomic optical Internet. The network perceives the parameters including core node delay, edge-to-edge delay, jitter, and other parameters. And then these parameters are used for routing for different services. According to the simulations, the proposed routing algorithm improves both the quality of transmission and switching of the service, which are respectively sensitive to the parameter, and the network performance.

In future work, an extension of the awareness of network and routing algorithms will be applied not only in the optical network but also in other kinds of networks, such as wireless network, etc. It is believed that intelligent awareness technology and adaptive routing algorithms have more heuristic value in data transfer layer of wireless network.

With the various kinds of services increasing, the network should be smarter, more adaptive and more intelligent. Network parameters awareness and its applications in routing optimize the performance of network and make network more sensitive. The NPA and its applications can also satisfy the QoS constraints. Our future research will focus on various applications of NPA and autonomic Internet.

Acknowledgements

This work was supported by the Hi-Tech Research and Development Program of China (2006AA01Z238), the National Natural Science Foundation of China (90704006), the National Basic Research Program of China (2007CB310705), PCSIRT (IRT0609), ISTCP (2006DFA11040), and 111 Project (B07005).

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Acknowledgements

This work was supported by the Hi-Tech Research and Development Program of China (2006AA01Z229).

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