Mobile Netw Appl (2013) 18:477–487 DOI 10.1007/s11036-012-0426-7
FFT Traffic Classification-Based Dynamic Selected IP Traffic Offload Mechanism for LTE HeNB Networks Xue Han · Lin Han · Yiqing Zhou · Liang Huang · Manli Qian · Jinlong Hu · Jinglin Shi
Published online: 22 December 2012 © Springer Science+Business Media New York 2012
Abstract Traffic offloading is a promising technique to alleviate the traffic load in LTE core networks. Based on 3GPP “SIPTO” (Selected IP Traffic Offload) architecture, this paper proposes dynamic SIPTO mechanism (D-SIPTO) for traffic offloading in LTE HeNB networks, which combines fast fourier transform (FFT) based IP traffic classification scheme (FFTTCS) with the dynamic traffic offload path selection algorithm (DTOPSA). Simulation results show that FFTTCS can realize on-line traffic classification with
similar precisions but only using less than 10 % of the time needed by existing methods. Combined with DTOPSA, the proposed D-SIPTO can reduce the core network traffic by 60 % while selecting the optimal offload path according to the type of traffic to be offloaded. Keywords LTE HeNB networks · Traffic classification · Traffic offload · FFT 1 Introduction
X. Han () · L. Han · Y. Zhou · L. Huang · M. Qian · J. Hu · J. Shi Institute of Computing Technology, Chinese Academy of Sciences, 100190, Beijing, China e-mail:
[email protected] L. Han e-mail:
[email protected] Y. Zhou e-mail:
[email protected] L. Huang e-mail:
[email protected] M. Qian e-mail:
[email protected] J. Hu e-mail:
[email protected] J. Shi e-mail:
[email protected] X. Han · L. Huang · M. Qian Graduate University of Chinese Academy of Sciences, 100049, Beijing, China X. Han · L. Han · Y. Zhou · L. Huang · M. Qian · J. Hu · J. Shi Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing, China
With the development of high speed wireless communication technologies and the increasing popularity of mobile broadband enabled devices, mobile data is expected to continuously increase with an annual rate of above 100 % [1]. The huge mobile data volume will bring high traffic burden to existing mobile infrastructures. To meet the rapid growth trend of mobile data, operators expand the capacity of core networks which turns out to be of high CAPEX/OPEX (CAPital EXpenditure/OPerating EXpense) costs with limited ARPU (Average Revenue Per User) contribution [2]. A cost-effective method is traffic offloading, which partly transfers the traffic of the core network to other existing low-cost networks at the radio access network. By optimizing network resource utilization, traffic offloading can support high mobile data volume with lowered network congestion and reduced CAPEX investments. To support traffic offloading, 3GPP has introduced an architecture called “SIPTO” (Selected IP Traffic Offload) to offload selective mobile data traffic via H(e)NB (Home Node B or Home eNode B) which is a kind of low-cost fixed access point and the Internet [3]. Important traffic, which carries value added services, is sent to the core network, whereas “dump” traffic, for which the mobile network acts merely as a “bit-pipe”, is offloaded to the Internet. By reducing
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the load of the core network, SIPTO ensures operators with sufficient network capacity for value added services. However, 3GPP only provides the SIPTO architecture and there is no specific method on how to select the traffic to be offloaded. A number of researches have been carried out on traffic offloading, most of which focus on the realization of traffic loading in current mobile networks based on the SIPTO architecture. For example, in [4], Traffic Offload Function (TOF) is proposed to be implemented on both the gateway and H(e)NB to realize the traffic offloading for H(e)NB heterogeneous access networks. The corresponding signaling procedure is also designed. On the other hand, [5] proposes to implement TOF on PGW (Packet Data Network (PDN)-Gateway), where the traffic offload decision is made based on characteristics such as APN (Access Point Name), destination IP address range and port number of the processed bearer. The bearer to be offloaded will be routed to a preconfigured IP address. Since this method only decides whether the bearer should be offloaded or not, when multiple offload paths exist, it cannot select the optimized offload path dynamically to guarantee the QoS (Quality of Services) of the offloaded traffic. In [6], a centralized offloading approach is proposed, where a DNS (Domain Name System) server is accessed to provide offloading related information on per UE (User Equipment) or per APN connection basis. The offloading point that is geographically closest to the core network is selected. However, the QoS requirement of traffic offloading is not concerned and the scheme cannot realize optimized offloading path selection according to the real-time load of the candidate offload point. Another work [7] focuses on the CS (Circuit Switch) domain SIPTO at HNB. FPBX (Femto Private Branch Exchange) is proposed to collect the traffic of the HNBs in a concerned area, such as a campus or an enterprise with central or distributed locations. Through FPBX, normal cellular calls between two HNBs users can be replaced by a low-cost extension call. It is shown by simulation that the FPBX approach can effectively reduce the call setup costs and the voice trunk costs among HNB users by slightly increasing the normal cellular call setup costs. Besides the researches based on the 3GPP SIPTO framework, a generic data offloading scheme focusing on local access networks is described in [8], based on the assumption that the UE can support multi-APN multi-PDN (Packet Data Network) links. According to the load condition, the UE will set up a new optimized PDN link for the new IP session service for offloading without affecting the existing PDN links on this APN. But service flows that the existing PDN link has borne cannot be transferred to the new established optimized PDN link and therefore cannot be offloaded. It can be seen that this scheme makes offload decision based on PDN connections, which is not flexible in terms of QoS control.
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In summary, for traffic offloading in LTE HeNB networks, the following problems remain unsolved: (1) In the existing mechanisms, offloaded traffic is selected basing on static characteristics such as APN, destination IP address and location. Therefore, the QoS of the offloaded traffic cannot be guaranteed and more flexible schemes such as on-line traffic classification should be employed in SIPTO to meet various QoS requirements. (2) In the existing offloading methods, the traffic is usually proposed to be offloaded to a specific local gateway according to a fixed static routing configuration. However, the provision ability of each path changes as the traffic load in real network changes. That means, an offload path that can ensure QoS now may not be able to guarantee the QoS at a later time. Therefore, a fixed offload path is not desirable for QoS assurance. The offload path should be selected dynamically and optimally according to the real-time QoS provision ability of each path. In our previous work [9], a scheme METCS (Maximum Entropy based Traffic Classification Scheme) is proposed for on-line IP-traffic classification, which extracts the application layer payload pattern using the data flow. Simulation results show that METCS can achieve better accuracy with reduced time than existing methods for the whole group of data. However, it should be noted that the processing data unit in METCS is flow, e.g., 20 bytes out of 5 packets. Extra time is needed to collect enough number of data from different packets in a flow. A classification method that can work with smaller number of data is more desirable to realize real time traffic offloading. Moreover, the optimal offload path selection is also not concerned in METCS. Therefore, aiming to offload traffic via the optimal path to guarantee QoS, this paper proposes a dynamic SIPTO traffic offloading mechanism (D-SIPTO) for LTE HeNB networks, which includes FFT (Fast Fourier Transform) based traffic classification scheme (FFTTCS) and the dynamic traffic offload path selection algorithm (DTOPSA). FFTTCS can achieve fast on-line traffic classification in packet grain (e.g., 6 bytes out of one packet so there is no need to wait for the arrival of multiple packets as in METCS) which ensures more flexible offload decision with QoS assurance than METCS. Meanwhile, DTOPSA ensures the offloaded traffic to select the optimal offload path even during transmission according to the real-time QoS provision ability. In this way, the QoS of the offloaded traffic is better guaranteed. Simulation results show that FFTTCS can realize on-line traffic classification with similar precisions but only using less than 10 % of the time needed by existing methods. Moreover, DTOPSA enables the system to transmit offloaded traffic via the optimal path
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that can always guarantee the QoS. As a whole, the proposed D-SIPTO can reduce the core network traffic by 60 % while QoSs are still guaranteed. The rest of the paper is organized as follows. Section 2 describes the system model. Then the proposed D-SIPTO procedure is introduced in Section 3 with detailed descriptions of FFTTCS and DTOPSA. Simulation results are shown in Section 4. Finally, conclusions are drawn in Section 5.
2 System model LTE networks with HeNBs are considered. As shown in Fig. 1, several HeNBs are deployed in a local enterprise network. Without loss of generality, it is assumed that HeNBs are connected through IP links. The HeNBs access the operator’s EPC (Evolved Packet Core) through the WAN (Wide Area Network). Different local enterprise networks and the EPCs belonging to different operators (e.g. operator A and operator B in Fig. 1) are interconnected through WANs. EPC is comprised of P-GW (PDN gateway), S-GW (serving gateway) and MME (mobility management entity). P-GW will allocate IP addresses to UEs while S-GW is responsible for data transmission in the user plane. Signals in control plane from HeNB is processed in MME. Without SIPTO, for UEs located in the same local enterprise network (see UE1 and UE2 in Fig. 1), traffic flows from the originating UE (UE1) is transmitted upward to EPC-A (EPC of operator
Fig. 1 Architecture of LTE HeNB networks
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A) via HeNB and WAN. EPC-A will then transmit the traffic further to the terminating UE (UE2) (see path 1 in Fig. 1). It can be seen that the traffic passes through EPC-A and WAN twice, which wastes network bandwidth and transmission resource enormously. SIPTO will move part of the EPC function (such as MME and S-GW) to the local enterprise network, so that the traffic flows will be offloaded to the local Femtocell gateway (see path 2 in Fig. 1) when the originating UE (UE6) and terminating UE (UE7) are in the same local enterprise network. Similarly, for UEs belonging to different operators which previously follow path 3 in Fig. 1, SIPTO will allow it routing through path 4 as shown in Fig. 1. It can be seen that SIPTO enables traffic offloading which can alleviate the load of core network effectively. Aiming to realize the offloading without degradation to the QoS of the offloaded traffic, a proper traffic classification method is firstly needed, which should have the capability to distinguish different services with different QoS requirements using a small amount of traffic data in a short time. This is one target of this paper. Meanwhile, it can also be seen in Fig. 1 that there are two optional offload paths in the local enterprise networkA: Gateway-1 and Gateway-2. It is desirable to choose an optimal path to offload the traffic adaptively according to the changing network conditions, so that the QoS of the offloaded traffic can always be guaranteed. Yet SIPTO itself cannot provide such a function. Thus the optimal path selection becomes another target of this paper.
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3 D-SIPTO scheme 3.1 D-SIPTO framework As explained in previous sections, existing methods usually offload the traffic to a specific local gateway according to a fixed static routing configuration. To realize flexible dynamic traffic offload, the QoS of different traffic types should be considered when selecting the traffic to be offloaded. In addition, according to the changing QoS provision ability during offloaded transmission, the optimal path should be adaptively chosen for the offloaded traffic. To facilitate the realization of flexible and dynamic traffic offload with QoS assurance, the framework of D-SIPTO is firstly designed, which is shown in Fig. 2, and the three main function modules are illustrated as follows. (1) Traffic Classification Module (TCM) TCM monitors and reserves traffic flows passing through HeNBs. Traffic classification algorithms are implemented in this module to identify the traffic type which will be the input for the traffic offload decision engine. (2) Traffic Offload Rule Base (TORB) TORB is used to store the information needed to make traffic offload decisions, including priorities and QoS requirements for different traffic types, user preferences. In addition, special requirements can also be stored in TORB. For example, traffic with LI (Lawful Interception) requirement must pass through operator’s core network and cannot be offloaded. (3) Traffic offload decision engine (TODE) TODE is the core function of D-SIPTO. It firstly collects the traffic classification results at the output of TCM. Then dynamic traffic offload path selection is carried out according to the offload rules stored in TORB. Finally, the offload decision is made. It can be seen that the traffic classification algorithm in TCM and dynamic traffic offload algorithm in TODE are critical to the performance of D-SIPTO. With the proposed framework, D-SIPTO could select the optimal offload path Fig. 2 D-SIPTO framework
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according to the QoS requirement of each traffic type and the QoS provision capability of each offload path. For traffic which is previously decided to be offloaded, if there is no offload path that can guarantee its QoS at a later time, it will not be offloaded and routed to core network. In this way, the limited resources of core network are efficiently utilized and the operator’s throughput is improved without degrading the QoS of the offloaded traffic. 3.2 FFT based traffic classification algorithm Although traffic classification has been widely investigated in wired IP networks, these methods are not applicable in mobile environments where high bit error rates (BER) and temporary disconnections are observed due to hostile wireless channel conditions [10]. Therefore, to provide accurate and rapid traffic classification in mobile networks, a FFT based traffic classification scheme (FFTTCS) is proposed for SIPTO at HeNB, which can provide the traffic patterns through FFT operation over each packet unit (e.g. 6 bytes out of one packet) of the application layer payload, so that the traffic can be classified. Since the data from only one packet is needed in FFTTCS, the processing time is much faster compared to that of METCS [9], where data flow is needed (e.g. 20 bytes out of 5 packets). As shown in Fig. 3, FFTTCS includes four main functions, i.e., extract, FFT, classify and add flags. Detailed explanations are as follows. (1) Extract: As shown in Fig. 4, each IP packet is composed of an IP header, a TCP/UDP header and the application payload. The function of “Extract” module is to firstly remove the IP and TCP/UDP header, then take the first N Bytes of the application payload as the input to the FFT module. N is a small number to reduce the processing complexity of future operations. (2) FFT: It is well known that FFT is a fast algorithm for the discrete fourier transform. It is introduced in traffic offloading because it can transform a time domain signal into frequency domain so that the features of signals are more distinguishable. For example, Fig. 5 shows the traffic expressions in time and
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Fig. 3 FFTTCS
frequency domains for five different services, i.e., HTTP, RTP, RTSP, FTP and P2P. Five hundred packets are used for experiments with six bytes extracted out of each packet according to the “Extract” module. These extracted bytes are treated as signal sequences in the time domain and input into FFT. In the time domain, the x axis stands for the 500 packets adopted. The y axis denotes the decimal value of each byte. The values of six bytes are distinguished by different colors. For each packet, the six values are processed by FFT and the obtained complex sequences are shown as frequency domain expressions, where the x and y axis stand for the real and imaginary parts, respectively. It can be seen that in the time domain, it is difficult to identify the six sequences from each other, while in the frequency domain, the six sequences are clearly distinguishable, which makes the traffic classification easier. FFT also has the advantage of fast calculating and good DSP (Digital Signal Processing) hardware support so that it can be easily adopted in practice [11]. (3) Classify: Machine learning algorithms are employed to build models off-line and do classifications on-line. As shown in FFT function, the application payload patterns in the frequency domain are distinguishable. Such patterns can be obtained from off-line training basing on a large number of packets (e.g. 50,000) and stored as classification models. Then for each packet to be classified, the frequency domain of the six bytes extracted out of the packet is compared with the classification models, so that different applications can be identified.
(4) Add flag: A flag is added to the packet before it goes to HeNB to identify the classification result generated by “Classify” function. This flag is used for registering the classification result for each packet. There is no strict requirement for the place where the flag is added. It can be anywhere in the packet as long as the HeNB can identify. 3.3 Dynamic traffic offload path selection algorithm As illustrated before, besides the traffic classification algorithm, the dynamic traffic offload path selection algorithm (DTOPSA) is also important to the performance of D-SIPTO (core function of TODE, see Fig. 2). The offload path selection problem can be defined as follows. Let F = {f1 , f2 , ..., fn }, n ∈ φ (φ is the set of positive integer numbers) denotes the set of traffic flows from different services classified by FFTTCS, where fi represents traffic flow of service i. R ∗ = {r0 , r1 , r2 , ..., rm }, m ∈ φ represents the set of all candidate offload paths. Note that r0 stands for the path via the core network which is taken as the default transmission path. Moreover, the QoS vector for each service is expressed as Q = (q1 , q2 ..., qk ), k ∈ φ, where qk represents the kth QoS attribute for a service, such as delay. rj,ql stands for the provision capacity of the jth path for the ql −th QoS attribute. Finally, fi,ql is the ith traffic flow demand for the ql −th QoS attribute. Thus, the dynamic offload path selection problem can be modeled as finding a mapping between F and R ∗ as follows: roptimal offload = arg
max
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(i)∗
Cj
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(1)
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Fig. 4 Structure of the IP packet
fi,bw ≤ rj,bw
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fi,delay ≥ rj,delay , fi,jitter ≥ rj,jitter , fi,loss ≥ rj,loss
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Fig. 5 Traffic expressions in time and frequency domains
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where the object function Eq. 1 is to find an optimal transmission path roptimal offload from R ∗ for each traffic fi in set F . Cj(i)∗ is the closeness coefficient of each alternative
Fig. 6 Simulation test bed system
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which can be calculated according to the classical multicriteria ideal-point-distance method TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) [12]
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(see Appendix A). Constraint (2) represents the bandwidth demand of the traffic i (fi,bw ) that the offload path must satisfy, where ri,bw is the bandwidth provision ability of the path j . Similarly, Constraint (3) represents the QoS demands (delay, jitter and packet loss) that the selected offload path must meet according to [13].
4 Simulation 4.1 System configurations In order to evaluate the performance of D-SIPTO, a HNB based private network is set up to collect traffic data, whose architecture is shown in Fig. 6. In the test system, Femtocell AP is a HNB which can support 8 cell phones. A Core Gateway software is developed to realize the UMTS Core Network functions as an approximation to LTE EPC since there is no commercial LTE networks available now. Thus, cell phones connected to this private network can enjoy IP traffic services and data flows can be obtained from switches to which all Femtocell APs are connected. Five services are concerned in this paper, i.e., Web Browsing/HTTP, Video Streaming/RTSP, VoIP/RTP, FTP and P2P, according to 3GPP traffic model definition [14]. The amount of traffic data of each service used in simulations is shown in Table 1. Next, consider the scenario with four candidate offload paths. The initial QoS provision capabilities of these four paths are shown in Table 2, where PATH0 is the path via LTE EPC and the other three paths (PATH1,PATH2 and PATH3) are WLAN. According to the QoS definition in ITU [13], in addition to the four mandatory QoS (delay, packet loss rate, jitter, bandwidth) parameters, two more optional but important parameters, cost and security, are selected in this paper to describe the ability of offload path and the requirements of service traffic flows. With D-SIPTO, the traffic can be offloaded to the optimal PATH before and during transmission dynamically. Moreover, the traffic ratio and QoS requirements are shown in Table 3 for the five applications.
Table 2 Initial QoS provision capability of offload paths Parameters
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PATH3
Bandwidth (Kbps) Delay(ms) Jitter(ms) Packets loss rate(%) Costs (cent/KB) Security
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96
1,000
37–50 3
