Performance Analysis: Impact of Signalling Load over IMS Core on KPIs

Performance Analysis: Impact of Signalling Load over IMS Core on KPIs JIRI HOSEK, LUBOS NAGY, VIT NOVOTNY, PAVEL MASEK, and DOMINIK KOVAC Brno University of Technology, Department of Telecommunications, Technicka 12, 616 00 Brno, CZECH REPUBLIC. hosek@feec.vutbr.cz Abstract: One of the possible way how to determine the behaviour of the whole IMS (IP based Multimedia Subsystem) network for various intensity of signalling load is the performance analysis. The KPIs (Key Performance Indicators) are the most commonly used metrics for measuring and reporting performance from the end-to-end perspective. This article focuses the performance evaluation of the SIP (Session Initiation Protocol) metrics for IMS. The paper is also dealing with the investigation of trend-line functions and their behaviour during execution the multimedia services such as the Video on Demand, Voice over IP or File transfer or during execution some IMS procedures such as the registration procedure over home IMS network. Thanks to obtained results for selected KPIs, the behaviour of a home IMS network can be predicted and evaluated. Finally, the functionality of LDF (Load Detection Function) based on the prediction of KPIs is proposed at the end of this paper. Key Words: IP based Multimedia Subsystem, ixia IxLoad, Key Performance Indicators, LDF, Performance analysis. 1 Introduction The IP based Multimedia Subsystem is considered as a solution for the convergence of fixed, mobile and wireless technologies. Its first concept was defined in UMTS Release 5. Nowadays, this layered architecture is also a part of Evolved Packet Core standard. There are various recommendations ([1] [4]), research papers ([5] [13]) or studies that describe the performance analysis of the whole IMS network using the performance test-beds based on the open source tools. The overall concept of IMS performance testbeds is defined by ETSI (European Telecommunications Standards Institute) in the technical documents (it is divided into four separated parts) [1] or by 3GPP (3 rd Generation Partnership Project) in the technical standard [2]. The SIP metrics (Registration Request Delay, Ineffective Registration Attempts, Session Request Delays, etc.) for analysing the performance of SIP architectures from the end-to-end point of view are defined in the standard [3]. The most important key performance parameters (related to the registration, session establishment procedures, etc.) of IMS network are in more detail defined in the standard [4]. In the research work published by authors in [5], Tang et col. evaluated four SIP KPI metrics (the registration, initial response, initial ringing and deregistration times defined by technical standard [3]) in simple test. Due to the fact that the SIP signalling was generated only by one client application (OpenIC Lite application) into IMS core network, the obtained results are not significant. In the next research paper [6], Mahmood et col. evaluated the SIP end-user metrics (the session request, session reply and session teardown delays, see [3]) for different access networks (GSM, PSTN and WLAN) with respect to distance. Also in this case, the generated signalling is not significant because the authors made only 10 15 calls. The interesting results are presented in the article [7] published by Kulin et col. The authors described the performance test-bed of the IP PBX for various intensity of generated signalling load with respect to the transport protocols over which the SIP session is established. However the results do not offer the characteristics of IMS KPI parameters [4]. Therefore in our previous work [10], we were mainly focused on the performance evaluation of maximum load signalling over our laboratory IMS network according to the specification [1], [2] and the impact of IMS core elements on service latency during various IMS procedures such as the registration procedures. We discovered that the value of the maximum signalling load for these hardware and software configurations is 500 cps for defined IHS threshold 0.025 % and the entity was the failure point of simulated IMS network. The ISBN: 978-960-474-341-4 137

same bottleneck was described in [13] for even lower values of load (during execution of registration procedures). Also, we investigated that the S-CSCF has the highest influence on the service latency. The main motivation of this paper is targeted at the measurement of key performance parameters and at the finding of their trend-line functions. Thanks to the obtained functions, we will be able to predict the behaviour of the whole IMS architecture and find out the correlations between these parameters and failure of some IMS core elements during various intensity of signalling load generated by subscribers. These derived functions can be used to define the load detection function in the role of load balancing mechanism during registration or re-registration procedures. The architecture and interfaces of Load Detection Function (LDF) are in more detail described in [8]. The support of the load balancing for S-CSCF is defined in [9]. Thanks to LDF, the service latency of the whole network and the KPIs can be optimized. This article follows our previous work published in [10, 11, 12]. In the paper [10], we investigated the impact of overloaded S-CSCF servers on the service latency of whole IMS network. In our previous work published in [11], we designed the mathematical model of IMS network based on queuing theory and in the next paper ([12]), we designed and implemented the load-balancing algorithms for S-CSCF selection during the registration and re-registration procedures. In this paper, first we investigate the impact of signalling load on latencies of the IMS procedures (the IMS/SIP KPIs: the registration and the de-registration procedures, the call setup and the end call procedures, and the post dial and the pick-up delays) and then we propose the method of the S-CSCF assignment based on the LDF and measured KPIs. 2 The Architecture of IMS Test-bed The experimental topology of the realized test-bed (see Fig. 1) is consisted of two main parts: the TS entity (Test System [1]) and the SUT (System under Test [1]) entity. The high-performance tool - ixia IxLoad application (the support of Video on Demand, Voice over IP and File transfer) represents the TS entity used to generate the SIP signalling load. This element is also used for the evaluation of measured performance metrics (defined in [3, 4]). The SUT represents the whole IMS network (see the IP based Multimedia Subsystem and Services in Fig. 1) which is consisted of six servers unlike the TS node which is situated only on one server. The intensity of generated load defined with the Poisson distribution (see λ in the Fig. 1) is always ixia IxLoad TS λ λ P-CSCF I-CSCF S-CSCF IP based Multimedia Subsystem AS+MSS Services Figure 1: The simplified test-bed topology for evaluation of the KPI parameters. increased to 25 cps (5 cps for each of emulated applications in TS) in each measurement. The intensity of load from feedbacks of tested SUT (from the P- CSCF node) is showed as λ. The signalling flows are created with the help of the standardized document [14] and each of emulated services (VoD, VoIP and File transfer over IMS) consists of three main phases: registration procedures with authentication and subscription transactions, session establishment and session release and de-registration procedures initiated always by subscribers. All emulated services are designed as the state machine and each of main phases is always synchronized. The expiration value per SIP messages is set to 3600 s for SIP REGISTER, 90 s for SIP INVITE and 90 s for SIP UPDATE according to the specifications. The SIP timers, T1 and T2 are set to 500 ms and 4000 ms (see [15] for more details about timeouts settings). All interfaces based on DIAMETER protocol use the TCP as transport protocol. The hardware and software configuration in our test-bed were following: 4x Intel Core i5-2400s CPU @2.50 GHz with 6144k L3 cache, 8 GM RAM (DIMM 1333 MHz), 82574L Intel Gigabit Network Connection, OS GNU/Linux (Debian distribution, AMD64 architecture, kernel v3.2), the software implementations of CSCF nodes are based on the SER (SIP Express Router) and the based on FHoSS (FOKUS ) server created by Fraunhofer FOKUS Institute. 3 Results and Analysis In our case, the tested SUT system is in the role of a home IMS network (see section 2 or Fig. 1) and the signalling load goes through each of evaluated IMS core servers only in the case of the (de-)registration procedures (see Fig. 2). From the showed graph, it can be seen that the de-registration procedures (each ISBN: 978-960-474-341-4 138

procedure consists of one SIP request and one SIP response) have the higher delays than the measured registration procedures (each procedure consists of two SIP requests and responses) for all measured load intensities 25 cps, 500 cps. Also, it can be seen that the difference between the attempted registrations (see [4]) and de-registrations (see [4]) is significant only for 500 cps of signalling load when the whole SUT, especially the node during registration procedures, was very unstable. The characteristic of the initial registration procedure is defined in [3] as the SIP KPI registration request delay or as the IMS KPI initial registration set-up time in [4]. where CST is the call setup time calculated as the mean value of time difference between received the SIP ACK (T ACK ) as response to the SIP INVITE (T INV IT E ). Figure 3: The mean values of the call setup and end call procedures, and the attempted calls vs. signalling load. Figure 2: The mean values of the registration and de-registration procedures, and the attempted registrations and de-registrations vs. the signalling load. Let the signalling load intensity be x 25 cps, 500 cps. We deal with the registration (see eq. (1)) and de-registration (see eq. (2)) procedures least squares with the Trust-region algorithm with high correlation indexes (R-square=0.99 and 0.98, and Adjusted R-square=0.99 and 0.98): f(x) = (0.014) x (1.386) + 39.270 (1) f(x) = (3.777e 005) x (2.420) + 55.030 (2) The following graph (see Fig. 3) shows the characteristics of the call setup (the number of SIP requests and responses depends on the emulated service, see eq. (3)) and end call (consists of one SIP request and one SIP response, see Session Disconnect Delay in [3]) times vs. intensity of generated load (see λ in Fig. 1). Due to the fact that the values of attempted calls (see [4]) and attempted end calls initiated (see [4]) are very similar, the characteristic of end calls initiated is missing in the Fig. 3. The call setup time is calculated using following equation: CST = T ACK T INV IT E (3) From the comparison of Fig. 2 and Fig. 3 can be seen the interesting fact that the measured call setup and end call times are significantly lower than the registration or de-registration times. This may be due to the fact that the generated signalling goes (during the establishment or termination of the session) only through the P-CSCF and S-CSCF nodes (all tested nodes have the same hardware and software configuration, see section 2) and also that each SIP request or response has different response time in each of tested IMS core nodes. Let the intensity of signalling load be x 25 cps, 500 cps. We deal with the call setup (see eq. (4)) and end call (see eq. (5)) procedures least squares with the Trust-region algorithm with the correlation indexes (R-square=0.99 and Adjusted R- square=0.99): f(x) = (8.045) exp(0.003 x) (4) f(x) = (0.051) x (0.831) (5) In the Fig. 4, the characteristics of post dial (see Session Request Delay in [3] or see Successful session establishment time in [4]) and post pick-up (see eq. (6)) delays are showed. These times are the lowest measured times of all selected delays (see Fig. 2 or Fig. 3). P P D = T RT P T 200 (6) where P P D is calculated the post pick-up delay as the mean value of time difference between first received RTP packet (T RT P ) and the SIP 200 sent for INVITE (T 200 ). ISBN: 978-960-474-341-4 139

Figure 4: The mean values of the post dial and post pick-up delays vs. signalling load. Let the intensity of signalling load be x 25 cps, 500 cps. We deal with the selected procedures (see PDD in eq. (7) and PPD in eq. (8)) least squares with the Trust-region algorithm with high correlation indexes (R-square=0.98 and Adjusted R-square=0.98): f(x) = (3.119) exp(0.001 x)+ + (0.054) exp(0.009 x) (7) f(x) = (4.919) x (0.170) 4.553 (8) 3.1 Proposal of a new Algorithm for LDF In technical report 3GPP TR 23.812 (see [8]), four variants of LS are described in more details. Our proposal of the LDF architecture based on variant no. 1 ([8]) is showed in Fig. 5. In our proposal, we designed the new functionality of P-CSCF node - the AMB (the Advanced Measuring Block, Fig. 5), for support of S-CSCF assignment executed by I-CSCF. The main task of this functionality is to measure and evaluate the selected KPI indicators (the registration and de-registration delays IP-CAN AMB P-CSCFs List of KPIs and NoS for each S-CSCF S-CSCF S-CSCF 1 n EMS/NMS Ln LDF Cx Priority of S-CSCF (PID) Cx I-CSCFs PID Data from Figure 5: The load detection function based on [8]. and the session setup and session release delays) separately for traffic through each of S-CSCF server. The next function of AMB is the counter of the actual number of subscribers (see NoS in Fig. 5). The number of subscribers is one of possible parameters related to the load balancing that is defined in 3GPP TR 23.812 [8]. In our previous works published in [11, 12], using the mathematical model, we investigated the influence of NoS parameter (as one parameter for load balancing of S-CSCF servers) on the optimized latency of the whole IMS network. The obtained results show that the influence of this parameter is lower than in the method based on prediction of time (PT) and than the signalling time spent in the S-CSCF node. The results of this method showed that the average improvement of the service latency of the whole IMS network compared to the round robin as the best-fit function is 3.769 %. Therefore, this predicted time (calculated by LDF element) has in our proposal the higher weight than the NoS parameter (see Tab. 1). Table 1: The weight of performance parameters Parameter Weight PT (see [12]) 6 Registration delays (see eq. (1), Fig. 2) 5 De-registration delays (see eq. (2), Fig. 2) 4 Call Setup delays (see eq. (4), Fig. 3) 3 End Call delays (see eq. (5), Fig. 3) 2 NoS, server utilizations, used memory 1 The LDF element (see Fig. 5) receives the list of KPIs and NoSs from AMB and the value of PT parameter from each of available S-CSCF servers through Ln interface. First, the LDF evaluates (see eq. (9)) the received information related to the load balancing of S-CSCF servers (see tab. 1) and then the LDF sends the list of PID (priority of ID S-CSCF servers) to I- CSCF (see Fig. 5). W (i) = p(i, j) w(i, j) (9) 1<i<m 1<j<n where, the W (i) is the value for each of available S- CSCF servers (1...n) that is calculated as a sum of performance parameters p(i, j) (1...m) with defined weight w(i, j) according to Tab. 1. The highest value of PID has the S-CSCF with the lowest W value (see eq. (9)). Finally, the I-CSCF receives the list of PIDs and the S-CSCF address is chosen by I-CSCF node. ISBN: 978-960-474-341-4 140

4 Conclusion This paper deals with the evaluation of SIP/IMS KPIs (see Fig.2, Fig.3, Fig.4) for various intensities of the SIP signalling load generated by the highperformance generator through the laboratory IMS network with the support of some advanced telecommunication services such as the Voice over IP, Video on Demand, etc. Also, we have found out the trendlines with the high correlation that are described by the help of the exponential or logarithmic functions. These trend-line functions are used for the design of new S-CSCF assignment using the LDF architecture during the initial registration or re-registration procedures. The selection of S-CSCF server is based on the PID parameter evaluated by LDF architecture (see eq. (9) and tab. 1). Acknowledgements: This research work is supported by projects CZ.1.07/2.3.00/30.0005, SIX CZ.1.05/2.1.00/03.0072 and OPVK CZ.1.07/2.2.00/28.0062, Czech Republic. References: [1] European Telecommunications Standards Institute, IMS Network Testing (INT); IMS/NGN Performance Benchmark. ETSI TS 186 008. November 2012. [2] 3 rd Generation Partnership Project; Technical Specification Group Services and System Aspects, Performance Management (PM); Performance measurements; IP Multimedia Subsystem (IMS) (Release 11). 3GPP TS 32.409 (v. 11.4.0). September 2012. [3] D. Malas and A. Morton, Basic Telephony SIP End-to-End Performance Metrics. Internet Engineering Task Force (IETF), RFC 6076. January 2011. [4] 3 rd Generation Partnership Project; Technical Specification Group Services and System Aspects, Telecommunication management; Key Performance Indicators (KPI) for the IP Multimedia Subsystem (IMS); Definitions (Release 11). 3GPP TS 32.454 (v.11.0.0). December 2011. [5] J. Tang, C. Davids, Y. Cheng, A study of an open source IP multimedia subsystem test bed. In: Proceedings of the 5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine. 2008, pp. 45:1 45:7. [6] R. Mahmood and M. Azad, SIP messages delay analysis in heterogeneous network. In: Wireless Communication and Sensor Computing - ICWCSC 2010. 2010, pp. 1 5. [7] M. Kulin, T. Kazaz, S. Mrdovic, SIP Server Security with TLS: Relative Performance Evaluation. In: IX International Symposium on Telecommunications (BIHTEL - 2012). 2012, pp. 1 6. [8] 3 rd Generation Partnership Project; Technical Specification Group Services and System Aspects. Feasibility study on IP Multimedia Subsystem (IMS) evolution (Release 11). 3GPP TR 23.812 (v11.0.0). Dec. 2011. [9] 3 rd Generation Partnership Project; Technical Specification Group Core Network and Terminals, IP Multimedia Subsystem (IMS); Stage 2 (Release 12). 3GPP TS 23.228 (v12.1.0). June 2013. [10] L. Nagy, J. Hosek, P. Vajsar, V. Novotny, Impact of Signalling Load on Response Times for Signalling over IMS Core. In: IEEE Federated Conference on Computer Science and Information Systems - FedCSIS 2013. 2013. pp. 1 4. [11] Nagy, L., Tombal, J., Novotny, V., Proposal of a Queueing Model for Simulation of Advanced Telecommunication Services over IMS Architecture. In: IEEE International Conference on Telecommunications and Signal Processing - 2013, 2013. pp. 1 6. [12] Nagy, L., Novotny, V., Uramova, J., Makhlouf, N., Performance Analysis of IMS Network: The Proposal of new Algorithm for S-CSCF Assignment. In: Advances in Electrical and Electronic Engineering, 2013. pp. 1 8. It will be published in 2013. [13] R. Herpertz and J.M.E. Carlin, A Performance Benchmark of a Multimedia Service Delivery Framework. In: IEEE Mexican International Conference on Computer Science - ENC 2009. 2009, pp. 137 147. [14] 3 rd Generation Partnership Project; Technical Specification Group Core Network and Terminals, Signalling flows for the IP multimedia call control based on Session Initiation Protocol (SIP) and Session Description Protocol (SDP); Stage 3 (Release 5). 3GPP TS 24.228 (v5.15.0). September 2006. [15] J. Rosenberg, H. Schulzrinne, G. Camarillo, A. Johnston, J. Peterson, R. Sparks, M. Handley, E. Schooler, SIP: Session Initiation Protocol. RFC 3261. June 2002. [16] 3 rd Generation Partnership Project, IP Multimedia (IM) Subsystem Cx and Dx interfaces; Signalling flows and message contents (Release 11). 3GPP TS 29.228 (v11.6.0). December 2012. ISBN: 978-960-474-341-4 141