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A Performance Analysis for UMTS Packet Switched Network Based on Multivariate KPIS

Mobile data services are penetrating mobile markets rapidly. The mobile industry relies heavily on data service to replace the traditional voice services with the evolution of the wireless technology and market. A reliable packet service network is
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            !"#$ % A   P ERFORMANCE A NALYSIS FOR UMTS   P ACKET S WITCHED N ETWORK B ASED ON M ULTIVARIATE KPI S   Ye Ouyang and M. Hosein Fallah, Ph.D., P.E. Howe School of Technology Management Stevens Institute of Technology, Hoboken, NJ, USA 07030 youyang@stevens.edu hfallah@stevens.edu A BSTRACT    Mobile data services are penetrating mobile markets rapidly. The mobile industry relies heavily on data service to replace the traditional voice services with the evolution of the wireless technology and market. A reliable packet service network is critical to the mobile operators to maintain their core competence in data service market. Furthermore, mobile operators need to develop effective operational models to manage the varying mix of voice, data and video traffic on a single network. Application of statistical models could  prove to be an effective approach. This paper first introduces the architecture of Universal Mobile Telecommunications System (UMTS) packet switched (PS) network and then applies multivariate statistical analysis to Key Performance Indicators (KPI) monitored from network entities in UMTS PS network to guide the long term capacity planning for the network. The approach proposed in this paper could be helpful to mobile operators in operating and maintaining their 3G packet switched networks for the long run. K EYWORDS   UMTS; Packet Switch; Multidimensional Scaling; Network Operations, Correspondence Analysis; GGSN; SGSN; Correlation Analysis. 1.   I NTRODUCTION   Packet switched domain of 3G UMTS network serves all data related services for the mobile subscribers. Nowadays people have a certain expectation for their experience of mobile data services that the mobile wireless environment has not fully met since the speed at which they can access their packet switching services has been limited. Mobile operators realize that if they are to succeed in today’s wireless communications landscape, they must address the quality of service for their packet service users. Simply adding more bandwidth to accommodate increased packet switching traffic is an expensive alternative. Hence, the mobile operators are faced with the issue of how to do more with less? The initial answer is to ensure the network is operating optimally before one considers further capital investment in expanding the network infrastructure. For a network administrator, the traditional network operation and maintenance (O&M) pattern follows a cycle: If a problem is encountered, from hardware or software failures to network congestions, the technician issues a ticket, debugs the network, and fix the problem and operation continues. This mode of operation may be adequate for ensuring timely and quality service of data traffic in a short run. However it does not help mobile operators effectively and actively forecast and prevent potential problems in packet switched network in advance. This paper offers an approach to help mobile operators shape O&M policies for the long run via applying multivariate statistical modeling to the KPIs obtained from UMTS packet switched network. 2.   L ITERATURE R EVIEW   The current literature provides many practical tools and theoretical methods to design, plan and             dimension UMTS PS network. Also no previous literature provides a unified approach to estimate the performance for UMTS packet switched network from the aspect of multivariate analysis. The specifications of 3GPP from [2], [3], [4] and [5] define the architecture, topology, and services of UMTS Packet Switched (PS) network. Figure 1 displays the key network entities and critical links in the UMTS core network. The specifications such as [6], [7], [8], [9] and [10] define the protocol stacks of the interfaces in PS domain and the tunneling encapsulation process between Serving GPRS Support Node (SGSN) and Gateway GPRS Support Node (GGSN). The first ten references are the technical guidelines to construct the trial PS network. Reference [18], [19], and [20] propose some optimized network architecture of UMTS core networks and provide a unified approach to calculate the throughput or traffic for UMTS packet switched network In addition, [11], [12], [13], and [16] introduce the possible methods of multivariate analysis, some of which are adopted in the article to estimate the performance of UMTS PS network and forecast the potential failures exited in the network. Literatures [14], [15], and [17] are focused on those economic and policy issues in US telecommunication industry via empirical studies in which the various methods of multivariate analysis applied. 3.   P ACKET S WITCHED D OMAIN I N 3G   UMTS Packet Switched (PS) domain and Circuit Switched Domain compose the Core Network (CN) of a 2G Global Systems for Mobile Communications (GSM) or a 3G UMTS network. Whether in 2G or 3G phase, the CN plays an essential role in the mobile network system to provide such important capabilities as mobility management, call and session control, switching and routing, charging and billing, and security protection. In R99 version, the first version of 3G UMTS network, the CN domain still consists of the same network entities (NE) and the same network architecture as that in GSM phase. However, there is a change in the circuit switched domain of R4, the second version of UMTS, which supports a networking mode where bearer is separated from control. Meanwhile multiple bearer modes such as ATM/IP/TDM are supported by CN. Consequently the Mobile Switching Center (MSC) in GSM/UMTS R99 is split into two NEs: MSC Server (MSS) and Media Gateway (MGW). We should note that no changes happen in packet switched domain from R99 to R4 except for a new Iu-PS interface which is used to connect PS domain with 3G radio access network (RAN). Figure 1. Topology of UMTS CN: CS+PS domain             The CN in UMTS is logically classified into the circuit switched domain (CS) and packet switched domain (PS). The CS domain includes such logical NEs as MSC Server, MGW, Visitor Location Register (VLR) integrated in MSC Server physically, Home Location Register (HLR), Authentication Center (AUC), and Equipment Identity Register (EIR). The packet switched domain (PS) includes Serving GPRS Support Node (SGSN) and Gateway GPRS Support Node (GGSN). More specifically, PS domain consists of data service NEs: SGSN and GGSN as well as auxiliary NEs like Charging Gateway (CG), Border Gateway (BG) and Domain Name System Server (DNS), and different service platforms attached to PS domain. Figure 1 displays the topology of UMTS CN with the logical NEs mentioned above. Packet Switched domain physically consists of SGSN, GGSN, and Charging Gateway. Below is a short description of these NEs. SGSN is responsible for the delivery of data packets from and to MSs within its serving area. Its tasks include packet routing and transfer, mobility management (attach/detach and location management), logical link management, and authentication and charging functions. Its interfaces include Iu-Ps interface connecting to RNC, Gn/Gp interface to GGSN, Gr interface to HLR, Gs interface to MSC Server or MSC, Gd interface to Short Message Center (SMC), and Ga interface to Charging Gateway. GGSN is a gateway between UMTS PS/GPRS network and external data networks (e.g. Internet). It performs such functions as routing and data encapsulation between a MS and external data network, security control, network access control and network management. From UMTS PS/GPRS aspect, a MS selects a GGSN as its routing device between itself and external network in the activation process of PDP context in which Access Point Name (APN) defines the access point to destination data network. From external data network aspect, GGSN is a router that can address all MS IPs in UMTS PS/GPRS network. GGSN provides Gc interface to connect with HLR, Gn/Gp interface with SGSN, Gi interface with external data networks, and Ga interface with CG. Charging Gateway is the billing unit for PS domain. Sometimes coupled together with SGSN, it collects, merges, filters and stores the srcinal Call Detail Record (CDR) from SGSN and communicates with billing center, and then transfers sorted CDR to billing center. 4.   S ERVICE M ONITORING M ODEL F OR UMTS   PS   N ETWORK   UMTS Packet Switched (PS) network is a typical data network in which data traffic, particularly with streaming media services, is live, extremely time sensitive to delay, latency and  jitter, non-tolerant of congestion. For example, a small minority of packet service subscribers running FTP, streaming video or peer-to-peer (P2P) file sharing applications can generate enough traffic to congest UMTS PS networks and impact the majority of subscribers using interactive Web browsing and E-mail applications. In the past network operation and maintenance was focused more on monitoring the entire throughput. The UMTS PS model for service monitoring shall be capable of monitoring and capturing the necessary KPI data at the service level in addition to the network level. In the model, various types of service packet enter PS core domain via Iu-PS interface, the entry port of SGSN. After the encapsulated tunneling transport between SGSN and GGSN, the packets are delivered out to external network via the exit: Gi interface in GGSN. Hence the data monitoring starts from interface Iu-PS, the entry port of SGSN, and ends in interface Gi which is the exit of GGSN. The monitored KPIs for the model include two types of parameters: QoS/performance parameters and service parameters, the former of which includes delay, jitter, packet loss, throughput, and utilization; while the latter includes the throughput of all types of services going through SGSN and GGSN. Figure 2 below depicts the service model of UMTS PS network for performance monitoring. Different from traditional instant network monitoring, the UMTS PS model for service monitoring shall achieve:                A long run view of the PS service the user is experiencing;    Service-level quality and performance metrics which are affected by the traffic as well as vendors equipment (SGSN and GGSN);    Correlation of fault and performance data captured over a long period to identify the potential service affecting outages;    Consolidated utilization and performance data that can be applied for future network expansion planning. Cell phonesSmart phonesLaptopsMP3 players IP Core Messaging Packets VoiceVideoMusicWeb pagePeer-to-peer Devices KPI Data CollectionKPI Data Collection SGSNGGSN   Figure 2. UMTS PS Model of Service Monitoring 5.   M ULTIVARIATE S TATISTICAL M ETHODS A PPLIED T O K PIS The following methods can be applied to the collected UMTS PS performance (QoS) parameters and service parameters. Below is a short introduction to the methods we will be using. For the detail algorithms please see the Appendix A.  A.   Correlation Correlation measures the strength and direction of the relationship between two or more variables. Correlation is a standardized measurement that generates an easily interpretable value (Correlation Coefficients: r) ranging from -1.00 to +1.00. Correlation as applied in the next section will analyze the relationship between QoS performance parameters and service parameters. The correlation results may identify the service-level quality and performance metrics and reveal the impact of different service types on the UMTS PS network performance.  B.   Factor Analysis The core purpose of factor analysis is to describe the covariance relationships among many variables in terms of a few underlying, but unobservable, random quantities called factors. F actor analysis identifies the key variables and detects the structure in the relationships between variables, that is, to classify variables. Factor analysis can be considered an extension of principle component analysis, both attempting to approximate the covariance matrix represented by  . However, the approximation based on the factor analysis model is more elaborate. C.    Multidimensional Scaling Multidimensional scaling displays multivariate data in low-dimensional space. Its primary target is to fit the srcinal data into a low dimensional coordinate system such that any distortion caused by a reduction in dimensionality is minimized. Multidimensional scaling in our case deal with the following problem: For a set of observed similarities or differences between pairs of observations, we can find a representation of the samples in two dimensions such that their proximities in the new space nearly match the srcinal similarities.  D.   Correspondence Analysis             Correspondence analysis is a graphical procedure for representing associations in a table of frequencies or counts. To illustrate this assume is the matrix of m observable values of network parameters in n samples. Based on the factor analysis, one can obtain the eigen values for the m parameters. Then if we select p common factors () to calculate their corresponding eigen vectors {eig 1 , eig 2 ,…eig p}, the loading matrix last obtained is the common factor scores for the m parameters. Similarly the common factor scores for sample time spots can also be obtained. The last step is to reflect the factor scores for UMTS PS network parameters and sample times into a two dimension coordinates Using this approach we are able to investigate the UMTS PS network QoS in different time intervals to develop operation policies to optimize the performance of the current network.  E.   Cluster Analysis Cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise. Cluster analysis simply discovers structures in data without explaining why they exist. Cluster analysis can be used to discover structures in observed in KPI samples without providing an explanation/interpretation. Cluster Analysis and Multidimensional Scaling used together can help to investigate the similarities of the observed samples and organize them into several clusters. Multidimensional scaling is the visualization of Cluster Analysis in a two dimensional coordinate system. 6.   C ASE S TUDY   In this section, we illustrate how the statistical models described above can be used in modeling and developing operation policies for the UMTS network. Let’s assume a network environment as shown in Figure 3. We use Figure 3 as a trial UMTS PS network. The system as highlighted is composed of a SGSN which connects with radio network via Iu-PS interface and a GGSN which accesses Internet and Intranet of Enterprise 1. Firewall and Network Address Translation (NAT) are built between UMTS PS network and external networks. The radio network domain consists of a Node-B (Base station) and a Radio Network controller (RNC). The network administrator monitors the network traffic through management station with authorities to access the network entities (NE) of UMTS PS network. The objective of this experiment is to monitor the throughput in interface Gi (Eth1:100) as it leaves GGSN.   Figure 3. Network topology of trail UMTS PS network
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