A Network Simulation Tool for User Traffic Modeling and Quality of Experience Analysis in a Hybrid Access Architecture Oscar D. Ramos-Cantor, Technische Universität Darmstadt, oramos@nt.tu-darmstadt.de, Germany Moritz Lossow, Telekom Innovation Laboratories, moritz.lossow@telekom.de, Germany Heinz Droste, Telekom Innovation Laboratories, heinz.droste@telekom.de, Germany Gerhard Kadel, Telekom Innovation Laboratories, gerhard.kadel@telekom.de, Germany Marius Pesavento, Technische Universität Darmstadt, mpesa@nt.tu-darmstadt.de, Germany Abstract A Hybrid Access Architecture, where network users can be simultaneously served by different technologies, has been envisioned in order to increase the achievable data rates and enhance the user experience. This proposed access is promising to users where the costs of replacing existing technology are unmanageable and the complementary technology is underused. In order to understand the implications of a Hybrid Access between Digital Subscriber Line (DSL) and Long Term Evolution (LTE) in Downlink (DL) operation, a Network Simulation Tool has been developed, where the services demanded by the users are defined and modeled. Additionally, the Traffic and QoE Simulator (TQoES) establishes an algorithm to select the kind of access to be used by specific services, and reliably simulates the behavior of users within an LTE network based on 3GPP recommendations. 1 Introduction With the advent of new high bandwidth services, such as HD streaming, cloud computing and IPTV, the global Internet traffic is significantly increasing in recent years [1]. Depending on the employed technology, the users expectation is to have access to such high demanding services at anytime and anywhere. Network users can be described in two types, on the one hand, Fixed Users are connected to the network through a Digital Subscriber Line (DSL). Furthermore, the Fixed Users have a very tight expectation of having access to the services when demanded. On the other hand, Mobile users are connected via wireless technologies, e.g. High Speed Packet Access (HSPA), and their expectation is more flexible because of their higher acceptance of the technology limitations. Thus, in order to satisfy these different expectations, deployment of broadband technologies such as Fiber to the Home (FTTH) or highly dense Long Term Evolution (LTE) network is an option. However, under certain scenarios the deployment costs of such technologies make the solution unfeasible and therefore, additional alternatives need to be explored. The Hybrid Access Architecture solution proposed by Deutsche Telekom AG [2] implies the simultaneous usage of two technologies, DSL and LTE in order to increase the maximum data rates provided to a user. In rural scenarios, deploying FTTH is expensive and the load of the LTE network is low; thus, the Hybrid Access is a promising solution to the growing traffic demand. In this work a novel Network Simulation Tool, the Traffic and QoE Simulator (TQoES), is presented in order to study the implications of implementing the Hybrid Access Architecture in rural environments in the Downlink (DL). Home Gateway Core Network Internet Figure 1: Hybrid Access Architecture with DSL and LTE technologies The TQoES is based on four main modules. a) User generation, b) Hybrid Access Algorithm, c) LTE and d) Quality of Experience (QoE) modules. In the user generation module, a Two State Markov Process is proposed to model traffic services, which are defined in order to simulate typical IP traffic such as Voice over IP (VoIP), streaming and file downloading. Furthermore, these traffic services are associated to the user by following measured distributions of data volumes and active probabilities in the busy hour. The Hybrid Access Algorithm module is in charge of distributing the traffic demand generated by the user into the DSL and LTE accesses by using the criterion of the cheapest line first, i.e. DSL is prioritized. In this way, the user accesses the LTE network only when its demand is higher than the provided DSL capacity. An LTE module is used to simulate the wireless access, i.e. radio channel conditions and scheduling decisions. The LTE module uses the Shannon bound provided by the 3rd Generation Partnership Project (3GPP) [3] in order to estimate the channel condition of the user. Moreover, it applies a modified Round Robin scheduler to reduce complexity while keeping fairness with respect to assigned resources.
Finally, a QoE module is applied to evaluate the user experience per service type, where specific rules have been defined to assess whether the user is satisfied or not with the achievable data rates. To the best of the authors knowledge, due to the novelty of the Hybrid Access Architecture, simulation tools involving the simultaneous usage of two different technologies to serve the users is not available in the literature. Thus, the proposed TQoES corresponds to the State-ofthe-Art solution for such simulation analysis. The remainder of the paper is organized as follows. Section 2 introduces the Hybrid Access Architecture. Sections 3, 4 and 5 describe the modeling approaches of the user, services and access, respectively. The model validation and results are examined in Sections 6 and 7. Finally, conclusions are given in Section 8. 2 Hybrid Access Architecture A diagram of the Hybrid Access Architecture is illustrated in Figure 1. The user selects services to be provided in the DL, afterwards, a Home Gateway applies the Hybrid Access Algorithm to distribute the traffic between the fixed (DSL) and wireless (LTE) connections. Each technology is connected via the backhaul link to the provider s core network and then to the Internet. The proposed TQoES emulates the behavior of the user per second, by performing the following actions: first, the services are selected according to criteria explained in Section 4.3. Then, a decision step is made, based on the cheapest line first criterion, in order to distribute the traffic between the two accesses. Thus, the LTE network is only used after the maximum DSL capacity is reached. If the user is accessing the LTE network, scheduling decisions are made based on the extended Round Robin scheduler explained in Section 5.2. Finally, transmission is assumed to be held without errors and the traffic from both accesses is added to assess the user experience per service type. 3 User Modeling In order to simulate the Hybrid Access solution, users need to be generated. A user is defined by a traffic demand composed of one or several traffic services, and a description of the accesses conditions, e.g. maximum throughput of the DSL connection and radio link quality, in terms of the Signal to Interference plus Noise Ratio (SINR), of the LTE access. The service selection is based on Cumulative Distribution Functions (CDFs) of the data volume and active time per user in the busy hour. These CDFs are reproduced by the TQoES and can be obtained from different sources such as measurements or models. In this work, the reference CDFs are obtained from exemplary log normal distributions, with parameters µ and σ provided in Table 1. The user demand distribution corresponds to four hour traffic and is given in bits. The log normal distribution s heavy tail is avoided by limiting the user demand to a maximum of 4 GB, which equals a cumulative distribution of 99.7 %. The lowest active probability realized by the simulator is 0.1 %, i.e. 3.6 s in the busy hour. Therefore, active probabilities lower than 0.1 % are not considered, this percentage is equivalent to a cumulative distribution of 8.8 10 12. Mean µ σ User demand 67.8 MB/h 20.7 1.3 Active probability 14.2 % -2.2 0.7 Table 1: Log normal distribution parameters 4 Service Modeling 4.1 Service types and QoE definition Three service types have been defined to be used by the TQoES with their respective QoE criteria. Each service type is thought to cover a group of Internet applications typically demanded by users, where the expectations vary from ensuring connectivity to reduce waiting times. The three services can be summarized as follows: Linear Real Time (LRT) Services, e.g. VoIP, have a low data rate requirement with respect to other kind of services. While videoconferencing or telephoning, the user s expectation is to keep the call ongoing under satisfactory conditions to make the communication possible. In the TQoES, such a requirement is translated into the condition of providing an instantaneous service throughput which is greater or equal to a minimum value. If the instantaneous service throughput is below the threshold, the communication is not possible and the call would be dropped prematurely. Non Linear Real Time (NLRT) Services, e.g. streaming, require higher data rates than the LRT services. During streaming, the user expects an audio/video file to be played smoothly, i.e. without interruptions. For simulation, the QoE criteria used for NLRT services allows variable data rates through time, assuming that buffering is taking place, but requires the average data rate to be greater or equal than a threshold. If the average service throughput is below the minimum value, it is interpreted that the file suffered too many interruptions, i.e. providing a bad user experience. Non Real Time (NRT) Services, e.g. file downloading, have the highest data rates and the most relaxed constraint from all service types. For the simulation it is enough to calculate and compare the download data rates. Hence, the higher it is, the better the user experience.
4.2 Service Model The traffic services described in section 4.1 are modeled by Markov Processes [4] as illustrated in Figure 2, where P a and P i are the active and idle probabilities respectively, and t aa, t ai, t ia and t ii are the transition probabilities describing the transition matrix T as [ ] taa t T = ai, (1) t ia t ii where the first subindex indicates the initial state, and the second subindex the final state. The busy probability is obtained from the active time distribution used in the simulator. Thus, the idle probability is obtained as t aa P a P i = 1 P a. (2) t ai t ia Figure 2: Markov Model representation for traffic services P i t ii with a total of N services, the busy probability per service is calculated based on [4], as P Um = j P (S j ) j<k P (S j S k )+ j<k<l P (S j S k S l ) +... + ( 1) N+1 j P (S j ), (4) where P (S j ) is the probability for service j, being active and P (S j S k ) is the probability of both services, S j and S k, being active. Services are assumed to be independent, therefore P (S j S k ) = P (S j )P (S k ). Finally, the resulting data volume for the service mix is calculated as N D Uc = P (S j )R(S j ), (5) j=1 where R(Sj) is the data rate for service j, defined from common codecs such as H.264 among others [6]. The resulting value is compared with the total user data volume assigned from input distributions and if the difference is minimal, the service mix is selected. In the case that there are several alternatives with total data volumes close to the desired value, the one with the lowest difference is selected. Start In the TQoES, at each simulation step, i.e. snapshot, corresponding to 1s, a new state of the Markov Process is calculated by a stochastic process, taking into account the previous state and the transition matrix T. To calculate T, the following set of equations is used based on the Perron Frobenius theorem [5], t ia = 1 λ 1 + P i /P a, (3a) t aa = λ + t ia, t ai = 1 t ia, t ii = 1 t ia, (3b) (3c) (3d) where λ is the second eigenvalue of the matrix T. λ can take values between ( 1 : 1) and is restricted further in order to keep a low number of iterations required to stabilize the Markov Model. Data vol. & active time distributions User s data vol. (D Um ) & active time (P Um ) calculation Service mix calculation (combinatorial) Data volume calculation for service mix (D Uc ) D Um D Uc? No 4.3 Service Selection The last step in the description of the traffic generated by a user is the association of different services to the user. The flowchart presented in Figure 3, illustrates the main process performed to select the most suitable service combination in terms of data volume and active probability in the busy hour. Initially, reference values for the user s total data volume (D Um ) and active probability (P Um ) are assigned based on the input distributions described in Section 3. Afterwards, a combinatorial analysis is performed in order to select the possible services. For such a service mix End Yes Figure 3: Description of service selection algorithm 5 Access Modeling To finish the user generation, a description of the access link qualities is given to the user. For the DSL ac-
cess, typical line speeds for rural scenarios, from 1 Mbps up to 6 Mbps, are selected. For the LTE connection, a lookup table of data throughput per Physical Resource Block (PRB) in terms of the SINR is applied, based on the 3GPP recommendation TR 36.942 [3], where the Shannon bound is used. During the simulation, the number of scheduled resources in the LTE network varies, hence changing the total data rate that a user can obtain from the wireless access. 5.1 Hybrid Access Algorithm After the users have been completely described, i.e. a service mix has been associated and the access specifications have been given, the simulation takes place in a second base manner. At every second, a snapshot of the demand associated to the users is calculated from the current service states and the Hybrid Access Algorithm is executed to specify the way the traffic should be transmitted through the different accesses. The Hybrid Access Algorithm is summarized as follows: 1. Identify the total demand of all service types. 2. Assign the demand to the cheapest line first, i.e. DSL, until the maximum capacity is reached. 3. If there is remaining data to be transmitted, activate the second access, LTE, to schedule the required resources. In the second step, different approaches can be used in order to assign the demanded data to the DSL link. For instance, a prioritization of the different service types can be performed to provide a better experience to the user. In the simulation, the LRT services have been prioritized to be only transmitted through DSL, thus, the possibility of satisfying the user expectations is high since their required data rates are below the DSL s capacity. For the NLRT and NRT services, the remaining resources are distributed fairly. 5.2 Extended Round Robin Scheduler At the Base Station (BS) side, there are several algorithms to schedule the existing resources to the users. The TQoES uses an extended version of a Round Robin scheduler which takes also the user demand into account. In the first step, the BS schedules the radio resources in a round robin fashion, i.e. equal number of resources for each active user. Then, each demanding user can unlock the unused resources. The scheduler assigns the remaining resources also in a round robin fashion to users demanding additional resources. The scheduling process ends either if there are no more resources to be scheduled, or each user has enough resources to meet their demand. In order to avoid any harm to the existing mobile user, a prioritization in the scheduling process has been implemented. The mobile users have the highest priority and the BS assigns only the remaining resources to the Hybrid Access users. 6 Model Validation In this section, the correctness of the user modeling is verified. Table 2 provides the simulation parameters used for the evaluation. Parameter Value No. BS 57 (3 sectors per site) BS scheduler Extended Round Robin LTE Bandwidth 10 MHz BS Inter-site distance 5000 m Simulated time / granularity 1 hour / 1 s Environment Rural No. HA-user per radio cell 30 SINR distribution 3GPP 36.942 DSL capacity 1 Mbps, 3 Mbps, 6 Mbps Table 2: Configuration parameters of TQoES 6.1 User Traffic Generation The initial step is to ensure that the user traffic generation was performed correctly. Therefore, input and output distributions of the user demand and active probability were compared, where the DSL capacity was assumed as unlimited in order to avoid any bandwidth constraining effects on the Markov Model. CDF CDF 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 Input distribution Output distribution 10 7 10 8 10 9 10 10 Demand [MB] Input distribution Output distribution 0 10 3 10 2 10 1 10 0 Active probability Figure 4: Input and output distributions of the user demand (top) and active probability (bottom) In Figure 4, the input and output distributions of the user demand and active probability are shown, where the in-
put distributions correspond to the log normal references explained in Section 3, and the output distributions are associated to the resulting behavior of the Markov Model in the simulation. The simulated results agree well with the reference distributions, presenting a small deviation in the lower part of the CDFs because of the limitation of the lowest active probability, i.e. 0.1 %. Therefore, results validate the model to generate user traffic based on given distributions and hence, it is used for further evaluations. 7 Quality of Experience Evaluation Results and Access Employment The aim of the simulator is to evaluate the QoE enhancements introduced by the Hybrid Access Architecture under realistic user traffic conditions. In the following, results are focused on NLRT and NRT services. In the implementation, LRT services are not influenced by the Hybrid Access Architecture due to their high priority on the DSL and their low data rates. Comparison between Fixed and Hybrid Access is presented, where Fixed Access represents simulations with only DSL connections, and Hybrid Access illustrates the simulations with both active connections, DSL and LTE. 7.1 Non Linear Real Time Services The Quality of Experience for NLRT services is described by the ratio between good transmissions, i.e. successful streaming with average data rate over the defined threshold, and the total number of streamings. In Table 3 the percentages of good QoE for different DSL capacities and NLRT services with data rates up to 4 Mbps, are presented. By increasing the capacity of the line, the percentage of good QoE increases to 99.3 % with a 6 Mbps DSL. If the Hybrid Access is simultaneously used, an important improvement of the QoE is achieved for DSL capacities of 1 Mbps and 3 Mbps, with gains of 16.5 % and 18.7 %, respectively. DSL capacity [Mbps] 1 3 6 Good QoE Fixed [%] 79.2 80.8 99.3 Good QoE Hybrid [%] 95.7 99.5 100 Table 3: Percentage of good QoE for NLRT services 7.2 Non Real Time Services In the case of NRT services, the QoE is described by the download rate. In Figure 5 the distributions of the NRT data rates are shown for DSL capacities of 1, 3 and 6 Mbps. For the evaluation, files of 5 MB are selected because they are transmitted very often and therefore, their results provide a reliable statistic. Fixed Access users (FA) receive most of the time with data rates corresponding to the full capacity of the DSL. In some cases, less than 10 %, the data rates are slightly lower due to the presence of other concurrently active services. Activating the Hybrid Access (HA) enhances the data rates experienced by the user. In average, the gains provided by the Hybrid Access to 6 Mbps to 1 Mbps DSL capacities, represent factors of 2.3 to 7.5, respectively. This improvement represents an average user experience of 7 Mbps download rate even when the DSL capacity is significantly lower. CDF 1 0.8 0.6 x7.5 x3.4 x2.3 FA; 1 Mbps 0.4 HA; 1 Mbps FA; 3 Mbps 0.2 HA; 3 Mbps FA; 6 Mbps HA; 6 Mbps 0 0 10 20 30 40 Data rate [Mbps] Figure 5: NRT service data rate distributions for 5 MB file size 7.3 LTE Access Employment In order to obtain the previously described improvements, the Hybrid Access requires the availability of radio resources in the wireless network. From the TQoES, such a description of utilized radio resources and total wireless traffic is obtained and presented in Table 4. As a consequence of the Hybrid Access Algorithm, where the cheapest line criterion is used as explained in Section 5.1, the LTE traffic share for the overall Hybrid Access depends on the DSL capacity. Therefore, if the DSL capacity is low, more resources are demanded from the LTE network. Hence, for a 1 Mbps DSL connection, the traffic transmitted through the LTE network corresponds to 66.5 % of the total traffic, whereas it is only 29.8 % for a 6 Mbps DSL connection. Similar results are obtained for the total data volume transmitted per BS and the amount of scheduled radio resources. In the first case, the data volume decreases from 1.2 GB at 1 Mbps DSL to 0.5 GB at 6 Mbps DSL; in the latter, 36.6 % of the radio resources where used for a 1 Mbps DSL Hybrid Access, while it was 13.4 % of the resources for the 6 Mbps DSL case.
DSL capacity [Mbps] 1 3 6 LTE Traffic share [%] 66.5 44.4 29.8 Wireless data volume [GB/h] 1.2 0.8 0.5 Utilized radio resources [%] 36.6 23 13.4 Table 4: Data volume and utilized radio resources per cell 8 Conclusions In this work, a simulator to generate reliable user traffic based on given user demand and active probability distributions is presented. In this way, analysis of the Quality of Experience per service can be performed to assess the user performance enhancement introduced by different network accesses. It has been demonstrated that the Markov Model, used to simulate different service types, and the service selection algorithm, employed for describing the user demand, are accurate and agree well with reference input distributions. Furthermore, such an approach can be adopted in other types of simulations where representing the user behavior during a given period of time is relevant. The proposed definitions of service types and their QoE criteria, allow the objective evaluation of users expectations. Under that context, the Hybrid Access Architecture represents an attractive alternative to ensure the growing users requirements, permitting the usage of current and upcoming high demanding services. References [1] Cisco Visual Networking Index: Forecast and Methodology, 2012 2017, available at www.cisco.com [2] Deutsche Telekom AG: Capital Market Day, 2012, available at www.telekom.com [3] 3GPP: TR 36.942 v11.0.0, Radio Frequency (RF) system scenarios, 2012, available at www.3gpp.org [4] Sheldon M. Ross: Introduction to Probability Models, Tenth Edition, Elsevier, 2010. [5] Eugene Seneta: Non negative Matrices and Markov Chains, Second Edition, Springer Series in Statistics, 2006. [6] Kari Järvinen et. al.: Media coding for the next generation mobile system LTE, Elsevier Computer Communications, Vol. 33, pp.1916-1927, 2010.