Next generation transport network is characterized by the use of in-band signaling, where Internet Protocol (IP) packets carrying signaling or media information are mixed in transmission. Since transport resources are limited, when any segment of access or core network is congested, IP packets carrying signaling information may be discarded. As a consequence, it may be impossible to implement reachability and quality of service (QoS). Since present approaches are insufficient to completely address this problem, a novel approach is proposed, which is based on prioritizing signaling information transmission. To proof the concept, a simulation study was performed using Network Simulator version 2 (ns-2) and independently developed Session Initiation Protocol (SIP) module. The obtained results were statistically processed using Statistical Package for the Social Sciences (SPSS) version 15.0. Summarizing our research results, several issues are identified for future work.
Quality of service (QoS) [
QoS control in the transport technology-dependent aspect is one issue. The standardized NGN QoS concept comes along with a certain volume of signaling information, which is not efficiently controllable by the respective NGN provider [
The aim of this paper is to extend our research findings on signaling information transmission in different network conditions. This paper is a continuation of our previous work that introduced the novel approach to configuring Signaling service class [
The paper is organized as follows. Related work on QoS evolution, signaling, and control is discussed in Section
Providing QoS guarantees in IP networks is a challenging task. First studies proposing QoS frameworks for IP networks started to appear within Internet Engineering Task Force (IETF). To support QoS in IP networks, IETF proposed two frameworks. These are integrated services (IntServ) based on connection-oriented resource reservation principle and differentiated services (DiffServ) based on service differentiation approach. IntServ provides deterministic QoS guarantees and requires a signaling protocol in order to inform network elements about the required reservation. Resource Reservation Protocol (RSVP) is a protocol proposed for IntServ. The limitations of RSVP have inspired the emerging Next Steps in Signaling (NSIS) framework being developed by the IETF, for the purpose of installing and maintaining flow states in the network. The building blocks relevant to the IntServ framework include admission control, queuing, resource reservation, traffic classification, and traffic policing.
The concept behind the DiffServ framework is treating a packet based on its class of service as encoded in its IP header [
Although multiprotocol label switching (MPLS) is not considered as a QoS framework for IP networks, it provides a number of advantages in terms of QoS support. Modern QoS-aware networks are based on the combination of DiffServ and MPLS [
The QoS control architectures have been developed in several standards bodies [
In the NGN, a critical component that provides support for the dynamic QoS control is RACS [
Based on the RACS and the interface relationship between the RACS and the external entities in the NGN, it is possible to support different QoS control scenarios for the services requested by user terminals with different QoS negotiation capabilities [
User equipment can be classified into the following categories: (1) without QoS negotiation capabilities, (2) with QoS negotiation capabilities at transport layer such as RSVP or NSIS, (3) and with QoS negotiation capabilities at service layer such as SIP. In the current network environments, most of the user terminals do not support RSVP or NSIS protocol, because SIP has been adopted by the telecommunications industry as its protocol of choice for signaling. Thereby, during the service setup process, the user terminal can negotiate the QoS parameters required for the current service with the opposite terminal or application server via SIP. After the user terminal obtains the QoS parameters through negotiation, it sends a service request carrying the QoS parameters to the SCF entity. When the SCF entity receives the user service request, it extracts the QoS parameters from the service request and then forwards the QoS request to the RACS. Based on user profile, operator-specific policy rules, and resource availability, the RACS performs authentication and makes admission control decision on the received QoS request. When the RACS determines to permit the service to be transferred in the network in accordance with the requested QoS parameters, it sends gate control, bandwidth allocation, aggregation and adaptation control commands, and so forth, to the TF entity at the boundary of the network for traffic forwarding.
Because of the fact that both service and transport layers are involved in the NGN QoS control process, the signaling between these two layers is compulsory. Combined with the application of traditional IP QoS mechanisms, this leads to a certain volume of signaling traffic [
In an all-IP transport network, a common interface between a user and a network utilizes the IP technology. In an IP access network of the NGN, all IP packets issued by a user are transmitted forward with a best-effort mode or a priority mode and signaling and media information flows of the NGN services are mixed in transmission. Since network resources are always limited, when any segment or any node of the network between a user and a SCF entity is overloaded or congested, the signaling information flow of the NGN services may be discarded and unable to reach the SCF entity. As a result, even if the user requests services of a relatively higher priority, it is possible that no answer will be received and the service reachability as the first ingredient in the QoS perceived by the user fails to be implemented. Moreover, in the case of mixing the signaling and media information flows in transmission, it is difficult for an IP network transmission node to identify whether the transmitted IP packet contains a signaling information or a media information and to guarantee that the IP packets containing a signaling information are not discarded in the case of the congestion, which makes it obviously impossible to implement the service reachability.
The service requested by a user may also be an emergent call service with a top priority such as a burglar alarm call, a fire alarm call, a traffic alarm call, or a first aid call. Such a service requires not only that the signaling information flow of service can always be transmitted instantly and reliably, but also that the media information flow of service can be established on demand and be transmitted reliably. If a signaling information flow is unable to reach a SCF entity reliably, it is impossible to establish an application session and start a media information flow.
Under normal network conditions, national and international networks can meet the need of NGN services, except in the less-developed regions of the world where bandwidth is still badly lacking [
To sum up, it is not possible to guarantee that signaling information flow of the NGN services is transmitted instantly and reliably, which makes it difficult to implement the service reachability. The signaling information flow of the NGN services is unable to reach the SCF entity, so that the media information flow cannot be transmitted, which makes it difficult to implement an emergent call service and trigger the RACS.
The NGN provider should guarantee that the signaling information flow of all NGN services is transmitted instantly and reliably regardless of the service level or the service type. It has not been recognized that guaranteeing the service reachability, that is, the instant and reliable transmission of signaling information flows of all telecom-level services in any case of resource occupancy rate, is of the most importance in the discussion of the QoS problem of the NGN.
To accomplish the task of prioritizing signaling information transmission, DiffServ addresses the clear need for relatively simple and coarse methods of categorizing traffic into various service classes and applying QoS parameters to those classes. The IETF has released Request for Comments (RFC) 4594, which proposes configuration guidelines for DiffServ service classes [
Different service classes are constructed using DSCPs, traffic conditioners, PHBs, and active queue management (AQM) mechanisms. The Signaling service class should be configured to provide a minimum bandwidth assurance for Class Selector 5 (CS5) marked packets as defined in RFC 4594. This service class should use a rate queuing system, such as weighted fair queuing (WFQ) or weighted round robin (WRR). The single rate with burst size token bucket policer should be used to ensure that signaling traffic stays within its negotiated or engineered bounds. Since the traffic in this service class does not respond dynamically to packet loss, AQM should not be applied to CS5 marked packets [
As defined in RFC 4594, the signaling service class should be configured to assure the target values for IP packet transfer delay (IPTD) and IP packet loss ratio (IPLR), while the value of IP packet delay variation (IPDV) is not crucial. According to ITU-T Recommendation Y.1541, the following target values should be guaranteed: upper bound on IPTD below 100 ms and upper bound on IPLR below
Applying the proposed modification to Signaling service class configuration, the QoS objectives for signaling traffic could be guaranteed regardless of the network conditions. This possibility is essential for efficiency of standardized NGN approaches for QoS control. If signaling traffic is able to reach the SCF entity, it is possible to implement NGN service reachability and trigger RACS by means of service request. As a result, it is possible to enable dynamic end-to-end QoS control depending on the availability of the current network resources and thereby to improve transport network resource utility effectively.
To verify the novel approach to signaling information transmission, the simulation study is performed using a Network Simulator version 2 (ns-2) [
Three experiments, which differ from each other according to the cause and localization of congestion, are conducted as shown in Table
Description of experiments and scenarios.
Experiments
Experiment 1 | Experiment 2 | Experiment 3 | |
Cause of congestion | Overload | Overload | Overload |
Failure | |||
Localization of congestion | Network | B→C&D→F | Network |
A→B&D→B |
Scenarios
Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | |
QoSa Model | Best effort | DiffServb | DiffServb | DiffServb |
Service class | Priority level | |||
Telephony (EXPc) | 1 | 1 | 2 | |
Multimedia Streaming (CBRd) | No priority | 2 | 2 | 3 |
Standard (FTPe) | | 4 | 4 | |
Signaling (SIPf) | 3 | 1 |
aQoS: quality of service; bDiffServ: differentiated service; cEXP: exponential; dCBR: constant bit rate; eFTP: file transfer protocol; fSIP: session initiation protocol.
Each experiment includes four different simulation scenarios, which differs from each other according to the forward priority assigned to different traffic flows as shown in Table
The simulations are based on a common network topology consisting of six routers named A, B, C, D, E, and F, with five user domains (D1, D2, D3, D4, and D5) directly attached to each of them. The network topology is shown in Figure
Simulation network topology.
The network is loaded by four types of traffic flows, which follow the same paths
Information about traffic paths.
Path followed | Number of flows | ||||
D1-A-B-C-D5 | 1 | 10 | 16 | 200 | 10 |
D1-A-D-F-D4 | 2 | 10 | 16 | 200 | 10 |
D2-B-C-D5 | 3 | 10 | 16 | 200 | 10 |
D3-E-D-B-C-D5 | 4 | 10 | 16 | 200 | 10 |
D3-E-D-F-D4 | 5 | 10 | 16 | 200 | 10 |
D4-F-B-C-D5 | 6 | 10 | 16 | 200 | 10 |
aEXP: exponential; bCBR: constant bit rate; cFTP: file transfer protocol; dSIP: session initiation protocol.
The global generation rate of FTP flows is thus 200 kbps, which equals 3.33% of total amount of generated traffic. By multiplying the number of generated EXP and CBR flows shown in Table
Scenario 1 is characterized by the absence of QoS mechanisms, while the specific combination of them has been considered in Scenarios 2, 3, and 4. It is assumed that in Scenarios 2, 3, and 4 all the routers adopt the same weighted random early detection (WRED) mechanism and priority queuing (PQ) algorithm. The configuration parameters for each WRED router equal to ns-2 default ones [
The output trace file from each simulation scenario in Experiments 1, 2, and 3 is used to measure average IPTD and IPLR obtained for SIP signaling flows. To study the distribution of these packets around the mean, the standard deviation (
Experiment 1 investigates effects of network congestion on the critical performance metrics for six SIP signaling flows through four simulation scenarios [
Network/link load percentage above which performance metrics for SIP signaling flows exceed target QoS values.
Experiment/scenario | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | ||||
IPTDa | IPLRb | IPTDa | IPLRb | IPTDa | IPLRb | IPTDa | IPLRb | |
Experiment 1 | 60 | 40 | 60 | 60 | 90 | 100 | — | — |
Experiment 2 | 80 | 80 | 60 | 100 | 90 | 100 | — | — |
Experiment 3 | 20 | 20 | 20 | 20 | 20 | 100 | — | — |
aIPTD: IP transfer delay; bIPLR: IP loss ratio.
Critical performance metrics for SIP signaling flow 1—Experiment 1: (a) average IPTD for SIP signaling flow 1; (b) standard deviation of IPTD for SIP signaling flow 1; (c) IPLR for SIP signaling flow 1.
Critical performance metrics for SIP signaling flow 1—Experiment 2: (a) average IPTD for SIP signaling flow 1; (b) standard deviation of IPTD for SIP signaling flow 1; (c) IPLR for SIP signaling flow 1.
Critical performance metrics for SIP signaling flow 1—Experiment 3: (a) average IPTD for SIP signaling flow 1; (b) standard deviation of IPTD for SIP signaling flow 1; (c) IPLR for SIP signaling flow 1.
In Experiment 1, the average IPTD exceeds the target value when the network load is above 60% in Scenarios 1 and 2 and above 90% in Scenario 3. The average IPLR exceeds the target value when the bottleneck links load is above 40% in Scenario 1, 60% in Scenario 2, and 100% in Scenario 3. The average IPLR does not exceed 1×10−3 regardless of the network load and equals zero only in Scenario 4.
In Experiment 2, the average IPTD exceeds 100 ms when the bottleneck links load is above 80% in Scenario 1, 60% in Scenario 2, and 90% in Scenario 3. The average IPLR exceeds the target value when the bottleneck links load is above 80% in Scenario 1 and 100% in Scenarios 2 and 3. The average IPLR does not exceed
In Experiment 3, the average IPTD does not exceed 100 ms only in Scenario 4. The average IPLR exceeds the target value when the network load is above 20% both in Scenarios 1 and 2 and 100% in Scenario 3. It is kept below
Summarizing simulation results, it is noticed that the critical performance metrics for SIP signaling flows is possible to keep under target values regardless of network/link load only when the novel approach to configuring Signaling service class is used (Scenario 4). The average value of IPTD obtained for SIP signaling flows in Scenario 4 equals 60.43 ms (
The obtained results are statistically processed using Statistical Package for the Social Sciences (SPSS) version 15.0. The null hypothesis is setup stating that the target QoS objectives for Signaling service class are guaranteed when it is assigned the highest priority. Against the null hypothesis is setup the alternative hypothesis. Statistical tests of the null hypothesis are summarized as a
The results of regression analysis of the relationship between the IPTD for SIP signaling flows and the network/link load for each scenario of conducted experiments are summarized in Table
Regression model for analyzing IPTD for SIP signaling flows.
IPTDa | Regression model | Parameters | |||||
Experiment 1 | Scenario 1 | 0.0458 | 0.0199 | 0.0932 | 0.3054 | <0.05 | |
Scenario 2 | 0.0385 | 0.0251 | 0.1812 | 0.4257 | <0.05 | ||
Scenario 3 | 0.0123 | 0.6913 | 0.0384 | 0.1960 | <0.05 | ||
Scenario 4 | 0.0716 | 0.0027 | 0.0516 | >0.05 | |||
Experiment 2 | Scenario 1 | 0.0243 | 0.0135 | 0.3788 | 0.6155 | <0.05 | |
Scenario 2 | 0.0213 | 0.0280 | 0.4314 | 0.6568 | <0.05 | ||
Scenario 3 | 0.0240 | 0.0167 | 0.3119 | 0.5584 | <0.05 | ||
Scenario 4 | 0.0613 | 0.0027 | 0.0516 | >0.05 | |||
Experiment 3 | Scenario 1 | 0.0458 | 0.0199 | 0.0932 | 0.3054 | <0.05 | |
Scenario 2 | 0.0385 | 0.0251 | 0.1812 | 0.4257 | <0.05 | ||
Scenario 3 | 0.0938 | 0.0121 | 0.0351 | 0.1873 | <0.05 | ||
Scenario 4 | 0.0715 | 0.0027 | 0.0516 | >0.05 |
aIPTD: IP packet transport delay; b
The results of regression analysis of the relationship between the IPLR for SIP signaling flows and the network/link load for each scenario of conducted experiments are summarized in Table
Regression model for analyzing IPLR for SIP signaling flows.
IPLRa | Regression model | Parameters | |||||||
Experiment 1 | Scenario 1 | −0.3573 | −0.0251 | −0.0004 | 0.6858 | 0.8281 | <0.05 | ||
Scenario 2 | 0.1538 | −0.0094 | 0.0001 | 0.8369 | 0.9148 | <0.05 | |||
Scenario 3 | — | — | — | — | — | — | — | — | |
Scenario 4 | — | — | — | — | — | — | — | — | |
Experiment 2 | Scenario 1 | 0.1734 | −0.0110 | 0.0002 | 0.9485 | 0.9739 | <0.05 | ||
Scenario 2 | 0.1807 | −0.0144 | 0.0003 | 0.9673 | 0.9836 | <0.05 | |||
Scenario 3 | 0.3318 | −0.0176 | 0.0002 | 0.8643 | 0.9296 | <0.05 | |||
Scenario 4 | — | — | — | — | — | — | — | — | |
Experiment 3 | Scenario 1 | −0.5489 | 0.0370 | −0.0005 | 0.8847 | 0.9405 | <0.05 | ||
Scenario 2 | −1.2363 | 0.0881 | −0.0015 | 0.9682 | 0.9839 | <0.05 | |||
Scenario 3 | — | — | — | — | — | — | — | — | |
Scenario 4 | — | — | — | — | — | — | — | — |
aIPLR: IP packet loss ratio; b
The evolution of NGN raises the issue of exploiting customizable service for nomadic user within the environment of heterogeneity and mobility. In this context, the end-to-end QoS remains always as an issue. The novel approach based on prioritizing signaling information transmission is proposed to address this issue. It has been recognized that prioritizing the signaling information transmission is the main precondition to guarantee the service reachability and implement dynamic end-to-end QoS control depending on the availability of the current network resources and thereby improve transport network resource utility effectively.
This paper extends our previous work in terms of statistical analysis of signaling performance metrics. It shows that there is statistically significant relationship between the signaling performance metrics and network/link load when standard approach to signaling information transmission is used. Although the network links seem to be underutilized, the signaling performance degradation present in this case could be explained by microcongestion events. The detailed analysis of the frequency and duration of microcongestion is needed to determine its impact on the performance degradation on underutilized links. The results obtained in this work indicate that there is almost no statistical relationship between the signaling performance metrics and the network/link load when novel approach is used. This is important for guaranteeing service reachability and continuity throughout the session while moving or changing terminal.
In addition to summarizing our research findings, the opportunity for the improvement of the novel approach may be identified. Prioritizing signaling information transmission may be based on the network utilization. With the network utilization around the 90%, higher priority level should be assigned to those signaling packets that terminate session and, thus, reduce the overall network congestion. On the other hand, with the network utilization around the 20%, higher priority level should be assigned to those signaling packets that establish new sessions.
Two additional issues may be identified for future work. They are related to the difficulties in cross-domain and cross-layer priority level configuration for Signaling service class. The target QoS objectives for Signaling service class are ideally achieved by identical QoS marking and packet treatment policies across layers and networks involved in the end-to-end packet transport. However, the local policy enforcement and possible remarking options likely lead to suboptimal signaling packet treatment traveling through several domains. Furthermore, the cross-layer QoS mapping occurs internally in an uncoordinated manner and is not signaled across network boundaries.
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions, which significantly improved the quality of this paper