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

JIRI HOSEK, LUBOS NAGY, VIT NOVOTNY, PAVEL MASEK, and DOMINIK KOVACBrno 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 Sub-system) network for various intensity of signalling load is the performance analysis. The KPIs (Key PerformanceIndicators) are the most commonly used metrics for measuring and reporting performance from the end-to-endperspective. This article focuses the performance evaluation of the SIP (Session Initiation Protocol) metrics forIMS. The paper is also dealing with the investigation of trend-line functions and their behaviour during executionthe multimedia services such as the Video on Demand, Voice over IP or File transfer or during execution someIMS procedures such as the registration procedure over home IMS network. Thanks to obtained results for se-lected KPIs, the behaviour of a home IMS network can be predicted and evaluated. Finally, the functionality ofLDF (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 analy-sis.

1 IntroductionThe IP based Multimedia Subsystem is considered asa solution for the convergence of fixed, mobile andwireless technologies. Its first concept was definedin UMTS Release 5. Nowadays, this layered archi-tecture is also a part of Evolved Packet Core stan-dard. There are various recommendations ([1]–[4]),research papers ([5]–[13]) or studies that describe theperformance analysis of the whole IMS network usingthe performance test-beds based on the open sourcetools. The overall concept of IMS performance test-beds is defined by ETSI (European Telecommunica-tions Standards Institute) in the technical documents(it is divided into four separated parts) [1] or by 3GPP(3rd Generation Partnership Project) in the technicalstandard [2]. The SIP metrics (Registration RequestDelay, Ineffective Registration Attempts, Session Re-quest Delays, etc.) for analysing the performance ofSIP architectures from the end-to-end point of vieware defined in the standard [3]. The most importantkey performance parameters (related to the registra-tion, session establishment procedures, etc.) of IMSnetwork 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 (theregistration, initial response, initial ringing and de-registration times defined by technical standard [3]) in

simple test. Due to the fact that the SIP signalling wasgenerated only by one client application (OpenIC Liteapplication) into IMS core network, the obtained re-sults 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 tear-down delays, see [3]) for different access networks(GSM, PSTN and WLAN) with respect to distance.Also in this case, the generated signalling is not sig-nificant because the authors made only 10− 15 calls.

The interesting results are presented in the article[7] published by Kulin et col. The authors describedthe performance test-bed of the IP PBX for various in-tensity of generated signalling load with respect to thetransport protocols over which the SIP session is es-tablished. However the results do not offer the charac-teristics of IMS KPI parameters [4]. Therefore in ourprevious work [10], we were mainly focused on theperformance evaluation of maximum load signallingover our laboratory IMS network according to thespecification [1], [2] and the impact of IMS core el-ements on service latency during various IMS proce-dures such as the registration procedures. We discov-ered that the value of the maximum signalling load forthese hardware and software configurations is 500 cpsfor defined IHS threshold 0.025 % and the HSS entitywas the failure point of simulated IMS network. The

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same bottleneck was described in [13] for even lowervalues of load (during execution of registration proce-dures). Also, we investigated that the S-CSCF has thehighest influence on the service latency.

The main motivation of this paper is targeted atthe measurement of key performance parameters andat the finding of their trend-line functions. Thanksto the obtained functions, we will be able to predictthe behaviour of the whole IMS architecture and findout the correlations between these parameters and fail-ure of some IMS core elements during various in-tensity of signalling load generated by subscribers.These derived functions can be used to define theload detection function in the role of load balancingmechanism during registration or re-registration pro-cedures. The architecture and interfaces of Load De-tection Function (LDF) are in more detail describedin [8]. The support of the load balancing for S-CSCFis defined in [9]. Thanks to LDF, the service latencyof 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 theimpact of overloaded S-CSCF servers on the ser-vice latency of whole IMS network. In our previouswork published in [11], we designed the mathematicalmodel of IMS network based on queuing theory and inthe next paper ([12]), we designed and implementedthe load-balancing algorithms for S-CSCF selectionduring the registration and re-registration procedures.In this paper, first we investigate the impact of sig-nalling load on latencies of the IMS procedures (theIMS/SIP KPIs: the registration and the de-registrationprocedures, the call setup and the end call procedures,and the post dial and the pick-up delays) and then wepropose the method of the S-CSCF assignment basedon the LDF and measured KPIs.

2 The Architecture of IMS Test-bedThe experimental topology of the realized test-bed(see Fig. 1) is consisted of two main parts: the TSentity (Test System [1]) and the SUT (System un-der Test [1]) entity. The high-performance tool - ixiaIxLoad application (the support of Video on Demand,Voice over IP and File transfer) represents the TS en-tity used to generate the SIP signalling load. This ele-ment is also used for the evaluation of measured per-formance metrics (defined in [3, 4]). The SUT repre-sents the whole IMS network (see the IP based Mul-timedia Subsystem and Services in Fig. 1) which isconsisted of six servers unlike the TS node which issituated only on one server.

The intensity of generated load defined with thePoisson distribution (see λ in the Fig. 1) is always

P-CSCF

S-CSCF

I-CSCF

HSS AS+MSS

IP based Multimedia Subsystem

ServicesTS

ixia IxLoad

λ

λ‘

Figure 1: The simplified test-bed topology for evalua-tion of the KPI parameters.

increased to 25 cps (5 cps for each of emulated ap-plications in TS) in each measurement. The intensityof 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 andFile transfer over IMS) consists of three main phases:registration procedures with authentication and sub-scription transactions, session establishment and ses-sion release and de-registration procedures initiatedalways by subscribers.

All emulated services are designed as the statemachine and each of main phases is always synchro-nized. The expiration value per SIP messages is setto 3600 s for SIP REGISTER, 90 s for SIP INVITEand 90 s for SIP UPDATE according to the specifica-tions. The SIP timers, T1 and T2 are set to 500 msand 4000 ms (see [15] for more details about time-outs settings). All interfaces based on DIAMETERprotocol use the TCP as transport protocol.

The hardware and software configuration in ourtest-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 NetworkConnection, OS GNU/Linux (Debian distribution,AMD64 architecture, kernel v3.2), the software im-plementations of CSCF nodes are based on the SER(SIP Express Router) and the HSS based on FHoSS(FOKUS HSS) server created by Fraunhofer FOKUSInstitute.

3 Results and AnalysisIn our case, the tested SUT system is in the role of ahome IMS network (see section 2 or Fig. 1) and thesignalling load goes through each of evaluated IMScore servers only in the case of the (de-)registrationprocedures (see Fig. 2). From the showed graph, itcan be seen that the de-registration procedures (each

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procedure consists of one SIP request and one SIP re-sponse) have the higher delays than the measured reg-istration procedures (each procedure consists of twoSIP requests and responses) for all measured load in-tensities 〈25 cps, 500 cps〉. Also, it can be seenthat the difference between the attempted registrations(see [4]) and de-registrations (see [4]) is significantonly for 500 cps of signalling load when the wholeSUT, especially the HSS node during registration pro-cedures, was very unstable. The characteristic of theinitial registration procedure is defined in [3] as theSIP KPI registration request delay or as the IMS KPIinitial registration set-up time in [4].

Figure 2: The mean values of the registration andde-registration procedures, and the attempted registra-tions and de-registrations vs. the signalling load.

Let the signalling load intensity be x ∈〈25 cps, 500 cps〉. We deal with the registration (seeeq. (1)) and de-registration (see eq. (2)) procedureswhich are defined by the functions using on-linearleast squares with the Trust-region algorithm withhigh 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 char-acteristics of the call setup (the number of SIP re-quests and responses depends on the emulated service,see eq. (3)) and end call (consists of one SIP requestand one SIP response, see Session Disconnect Delayin [3]) times vs. intensity of generated load (see λ inFig. 1). Due to the fact that the values of attemptedcalls (see [4]) and attempted end calls initiated (see[4]) are very similar, the characteristic of end calls ini-tiated is missing in the Fig. 3. The call setup time iscalculated using following equation:

CST = TACK − TINV ITE (3)

where CST is the call setup time calculated as themean value of time difference between received theSIP ACK (TACK) as response to the SIP INVITE(TINV ITE).

Figure 3: The mean values of the call setup and endcall procedures, and the attempted calls vs. signallingload.

From the comparison of Fig. 2 and Fig. 3 can beseen the interesting fact that the measured call setupand end call times are significantly lower than the reg-istration or de-registration times. This may be dueto the fact that the generated signalling goes (duringthe establishment or termination of the session) onlythrough the P-CSCF and S-CSCF nodes (all testednodes have the same hardware and software configu-ration, see section 2) and also that each SIP request orresponse has different response time in each of testedIMS 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)) procedureswhich are defined by the functions using on-linearleast squares with the Trust-region algorithm with thecorrelation 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 (seeSession Request Delay in [3] or see Successful ses-sion establishment time in [4]) and post pick-up (seeeq. (6)) delays are showed. These times are the low-est measured times of all selected delays (see Fig. 2 orFig. 3).

PPD = TRTP − T200 (6)

where PPD is calculated the post pick-up delay asthe mean value of time difference between first re-ceived RTP packet (TRTP ) and the SIP 200 sent forINVITE (T200).

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Figure 4: The mean values of the post dial and postpick-up delays vs. signalling load.

Let the intensity of signalling load be x ∈〈25 cps, 500 cps〉. We deal with the selected pro-cedures (see PDD in eq. (7) and PPD in eq. (8))which are defined by the functions using on-linearleast squares with the Trust-region algorithm withhigh correlation indexes (R-square=0.98 and AdjustedR-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 LDFIn technical report 3GPP TR 23.812 (see [8]), fourvariants of LS are described in more details. Our pro-posal of the LDF architecture based on variant no. 1([8]) is showed in Fig. 5.

In our proposal, we designed the new functional-ity of P-CSCF node - the AMB (the Advanced Mea-suring Block, Fig. 5), for support of S-CSCF assign-ment executed by I-CSCF. The main task of this func-tionality is to measure and evaluate the selected KPIindicators (the registration and de-registration delays

LDF

EMS/NMS

Ln

n1S-CSCFP-CSCFs

Mw MwMw

IP-CAN

I-CSCFs

CxCx

S-CSCF

HSS

Priority of S-CSCF (PID)

List of KPIs and NoSfor each S-CSCF

PIDData from HSSAMB

1 n

Figure 5: The load detection function based on [8].

and the session setup and session release delays) sep-arately for traffic through each of S-CSCF server. Thenext function of AMB is the counter of the actualnumber of subscribers (see NoS in Fig. 5). The num-ber of subscribers is one of possible parameters re-lated to the load balancing that is defined in 3GPP TR23.812 [8].

In our previous works published in [11, 12], usingthe mathematical model, we investigated the influenceof NoS parameter (as one parameter for load balanc-ing of S-CSCF servers) on the optimized latency ofthe whole IMS network. The obtained results showthat the influence of this parameter is lower than inthe method based on prediction of time (PT) and thanthe signalling time spent in the S-CSCF node. The re-sults of this method showed that the average improve-ment of the service latency of the whole IMS networkcompared to the round robin as the best-fit function is3.769 %. Therefore, this predicted time (calculated byLDF element) has in our proposal the higher weightthan the NoS parameter (see Tab. 1).

Table 1: The weight of performance parametersParameter 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 ofKPIs and NoSs from AMB and the value of PT param-eter from each of available S-CSCF servers throughLn interface. First, the LDF evaluates (see eq. (9)) thereceived information related to the load balancing ofS-CSCF servers (see tab. 1) and then the LDF sendsthe list of PID (priority of ID S-CSCF servers) to I-CSCF (see Fig. 5).

W (i) =∑

1<i<m1<j<n

p(i, j) ∗ w(i, j) (9)

where, the W (i) is the value for each of available S-CSCF servers (1...n) that is calculated as a sum ofperformance parameters p(i, j) (1...m) with definedweight w(i, j) according to Tab. 1.

The highest value of PID has the S-CSCF withthe lowest W value (see eq. (9)). Finally, the I-CSCFreceives the list of PIDs and the S-CSCF address ischosen by I-CSCF node.

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4 ConclusionThis paper deals with the evaluation of SIP/IMSKPIs (see Fig.2, Fig.3, Fig.4) for various intensitiesof the SIP signalling load generated by the high-performance generator through the laboratory IMSnetwork with the support of some advanced telecom-munication services such as the Voice over IP, Videoon Demand, etc. Also, we have found out the trend-lines with the high correlation that are described bythe help of the exponential or logarithmic functions.

These trend-line functions are used for the de-sign of new S-CSCF assignment using the LDF archi-tecture during the initial registration or re-registrationprocedures. The selection of S-CSCF server is basedon the PID parameter evaluated by LDF architecture(see eq. (9) and tab. 1).

Acknowledgements: This research work issupported by projects CZ.1.07/2.3.00/30.0005,SIX CZ.1.05/2.1.00/03.0072 and OPVKCZ.1.07/2.2.00/28.0062, Czech Republic.

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[12] Nagy, L., Novotny, V., Uramova, J., Makhlouf,N., Performance Analysis of IMS Network: TheProposal of new Algorithm for S-CSCF Assign-ment. In: Advances in Electrical and ElectronicEngineering, 2013. pp. 1–8. It will be publishedin 2013.

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