Small Cell Offloading Algorithm Based on the Social ...

Small Cell Offloading Algorithm Based on the Social Context Hye-Rim Cheon

Seung-Que Lee

Jae-Hyun Kim

Department of Electrical and Computer Engineering Ajou University Suwon, Korea [email protected]

Electronics and Telecommunication Research Institute Daejeon, Korea [email protected]

Department of Electrical and Computer Engineering Ajou University Suwon, Korea [email protected]

of using small cell backhaul such as the Internet.

Abstract—Traffic demand increases explosively due to the developed services and it becomes the mobile network’s load. One of these services, social networking, generates a large amount of traffic. To solve it, we propose a small cell offloading algorithm using social context. In the proposed algorithm, to satisfy the effective data rate maximally, we estimate the application selection probability using social context and determine the offloading ratio according to this. By simulation, the effective data rate achievement level of the proposed algorithm is higher than the conventional one with similar offloading ratio.

Thus, we propose the small cell offloading algorithm based on the social context. The proposed algorithm determines the offloading ratio to maximize the QoS for small cell users in terms of effective data rate using the probability of application selection. The rest part of this paper is organized as follows. In Section II, we propose the small cell offloading algorithm based on the social context. We then evaluate the performance of the proposed algorithm in Section III. Finally, we conclude this paper in Section IV.

Keywords—Small cell, offloading, LIPA/SIPTO, social context, LTE, LTE/A

I.

II.

PROPOSED ALGORITHM

We propose the small cell offloading algorithm that maximizes the effective data achievement level and determines the offloading ratio of each application according to the application selection probability, which is calculated by the social context. The application selection probability is estimated using the application’s popularity in small cell users and overall network users. The former is considered as a target social context to meet the small cell user’s QoS requirement, while the latter is considered as a generalized social context. The total offloading ratio is determined by each application’s offloading ratio but it cannot exceed the predefined optimal offloading ratio while maximize the achievement level of effective data rate.

INTRODUCTION

The improvement of mobile communication technologies has resulted in the explosive increase of mobile traffic and this can be the load on the mobile core network. According to Cisco, mobile data traffic grew 74 percent in 2015 [1]. In addition, the social networking services account for a large percentage of mobile data traffic [2]. Therefore, it is needs to offload the traffic via other routes using social context such as content popularity. Many researchers have studied offloading schemes according to the offloading route, such as device-to-device (D2D), WiFi, and small cell offloading. In D2D offloading, the service providers may deliver the information only to target users and the target users can further disseminate the information to subscribed users [3]. In WiFi offloading, it transfers data on the WiFi hot spot using spontaneous WiFi connectivity, or restarts the data transfer whenever entering the WiFi coverage until the transfer is finished [4]. In small cell offloading, it can directly route the traffic to same residential/enterprise network or the Internet through the small cell [5]. 3GPP proposed the local IP access (LIPA) and selected IP traffic offload (SIPTO) for traffic offloading [6].

Fig. 1 presents the proposed algorithm framework. It consists of 4 stages. In stage 1, we estimate the application selection probability Pi, which means that the user uses the application with probability Pi. The application selection probability Pi is determined by the application’s popularity in small cell users and overall network users and it can be defined by Indian Buffet Process (IBP) model [7]. Pi is defined as

Pi  Pi _ sc  Pi _ net

(1)

where Pi_sc is application i selection probability for small cell users and Pi_net is application i selection probability for overall network users. Pi_sc can be estimated by

In D2D and WiFi offloading, the quality of service (QoS) of the non-delay-tolerant application might be degraded when offloading because of their short coverage area. However, in small cell offloading, non-delay-tolerant applications can offload the data without the performance degradation because

Pi _ sc 

1 li m j  , j  Ii li j 1 n

(2)

This work was supported by the IT R&D programs of MSIP (Ministry of Science, ICT and Future Planning), Korea [15ZI1110, Research on Advanced Technologies of Access Network for Traffic Capacity Enhancement] and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2014R1A2A2A01002321)

978-1-4673-9991-3/16/$31.00 ©2016 IEEE

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ICUFN 2016

Application i selection probability  for overall network  users

��_��� �

�� ��� �� � �

Estimate the  application i selection  probability

�� � ��_�� � ��_��� Calculate  application i offloading ratio

���_� �

Total offloading ratio  constraint

��� � ��������

Application i selection probability  for small cell users

��_�� �

��

� ���

Ri is the effective data rate of application i, which means the data rate of required to satisfy the QoS requirements of target application [9]. rOL is total offloading ratio and it can be given by

rOL 

n

III.

(3)

In stage 2, we calculate the application i offloading ratio rOL_i and it is given by

rOL _ i 

k

TLtotal

(7)

PERFORMANCE ANALYSIS

TABLE I. Parameter Web Music/Audio Data Rate SD Video HD Video Web Traffic Music/Audio Generation SD Video Ratio HD Video Initial Offloading Network Supportable Data Rate Initial Core Network Supportable Data Rate Scenarios for QoS requirement algorithm. (w1 , w2 , w3) Number of Iterations

(4)

P i 1

TLi 

We perform the simulation to analyze the performance of the proposed algorithm using MATLAB. We assume that traffic data rate is divided into 4 classes [10]. The QoS requirement parameters refer to Standardized QoS Class Identifier of QCI characteristics in 3GPP technical specification [11].

where αi is the Poisson parameter and it can be determined by application i’s popularity in overall network.

wOL _ i  Pi

OL _ i

In stage 4, stop the iteration of stage 3 and determine the offloading ratio for each application.

where i is application index, j is content index, li is number of contents which can be classified as application i, mj is number of use for content j, n is number of small cell users and Ii means the set of content which can be categorized as application i. In addition, Pi_net can be estimated by

n

i 1

By these equations, we repeat the adjustment of weighting factor and the calculation of offloading ratio for each application until find the maximum total effective data rate achievement level with the constraint.

Figure 1. Proposed algorithm framework



r

where TLi is traffic volume of application i and TLtotal is total traffic volume in networks.

���_�

e

(6)

where SRLN and SRCN are the supportable data rate of offloading network and core network and it means that the network can really support data rate in the current condition [8].

���_� � �

k

i

(5)

Ri _ N  rOL _ i  SRLN  (1  rOL _ i ) SRCN

��_� ��

i

Ri

Ri_N is the expected data rate of application i according to the offloading and it is defined as

Determine  application i offloading ratio

Pi _ net 

i 1

where roptimal is the predefined optimal offloading ratio.

���

Maximize  total effective data  rate achievement  level �

Ri _ N

subject to rOL  roptimal

�� 1 � � ��

Application i offloading ratio  weighting

���_� � �� ∑���� ��

k

maximize 

i

where wOL_i is the offloading weighting factor for application i and its default value is predefined but it have to be adjusted to maximize the effective data rate achievement level in the next stage. In stage 3, we adjust the offloading weighting factor to maximize the effective data rate achievement level while not exceed the optimal offloading ratio. It can be presented as:

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SIMULATION PARAMETERS Value Uniform(150, 449) kbps Uniform(450, 749) kbps Uniform(750, 1149) kbps Uniform(1150, 2300) kbps 14.6 % 13.4 % 27.7 % 44.3 % 800 kbps 400 kbps Scenario 1: (0.8, 0.1, 0.1) Scenario 2: (0.1, 0.8, 0.1) Scenario 3: (0.1, 0.1, 0.8) 300

context for small cell users and decides the offloading ratio of each application in terms of small cell users. As a result, it can achieve the goal effectively.

Total Effective Data Rate Achievement Level

9 8 7

IV. CONCLUSION In this paper, we proposed the small cell offloading algorithm by the social context. We estimate the application selection probability using the social context for small cell users and overall network users and then, calculate the offloading ratio for each application according to the application selection probability. To maximize the total effective data rate achievement level with the constraint, we iterate the weighting factor adjustment and the calculation of offloading ratio. By performance analysis, we show that the proposed algorithm can make the effective data rate achievement level higher than conventional one, while its offloading ratio is similar with the conventional one.

6 5 4 3 2 1 0

No Offloading

QoS(Scenario 1) QoS(Scenario 2) QoS(Scenario 3) Social Context

Figure 2. Total Effective data rate achivement leval

ACKNOWLEDGMENT This work was supported by the IT R&D programs of MSIP (Ministry of Science, ICT and Future Planning), Korea [15ZI1110, Research on Advanced Technologies of Access Network for Traffic Capacity Enhancement] and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2014R1A2A2A01002321)

0.25

Offloading Ratio

0.2

0.15

0.1

REFERENCES 0.05

0

[1]

QoS

Cisco, “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2015–2020,” February 2016. [2] “Mobile Data Traffic Surpasses Voice,” http://www.cellularnews.com/ story/42543.php, 2010. [3] A. Aijaz, H. Aghvami and M. Amani, “A Survey on Mobile Data Offloading: Technical and Business Perspectives,” IEEE Wireless Communications, vol.20, no.2, pp. 104- 112, April 2013 [4] K. Lee, J. Lee and Y. Yi, “Mobile Data Offloading: How Much Can WiFi Deliver?,” IEEE/ACM Transactions on Networking, Vol. 21, No. 2, April 2013 [5] L. Ma, W. Li, “Traffic Offload Mechanism in EPC Based on Bearer Type,” in Proc. WiCOM 2011, September 2011. [6] 3GPP TR 23.829 V10.0.1, “3GPP technical specification group services and system aspects; local IP access and selected IP traffic offload,” October 2011. [7] T. L. Griffiths and Z. Ghahramani, “The Indian buffet process: An introduction and review,” J. Mach. Learn. Res., vol. 12, no. 4, pp. 1185– 1224, April 2011. [8] H. R. Cheon, S. Q. Lee, and J. H. Kim, “New LIPA/SIPTO Offloading Algorithm by Network Condition and Application QoS Requirement,” in Proc. ICTC 2015, Oct. 2015 [9] S. H. Kang and J. H. Kim, “QoS-aware path selection for multi-homed mobile terminals in heterogeneous wireless networks,” in Proc. CCNC 2010, Jan. 2010 [10] Cisco, “Global Internet Speed Test (GIST) for iPhone, BlackBerry and Android,” http://gistdata.ciscovni.com/ [11] 3GPP TS 23.203 V12.6.0, “3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Policy and charging control architecture(Release 12),” September 2014

Social Context

Figure 3. Offloading ratio

We compare the proposed offloading algorithm with the conventional algorithm based on the QoS requirement of application and assume three scenarios for conventional algorithm [8]. Each scenarios assume that high data rate, high cost and delay-sensitive application have the offloading priority. The details of simulation parameters are presented in Table I. Fig. 2 shows the total effective data rate achievement level. The effective data rate achievement level means that how much satisfy the effective data rate. As shown in Fig. 2, the total effective data rate achievement level of proposed algorithm is about 1.7 times larger than the conventional one. Because the goal of proposed algorithm is to maximize the effective data rate achievement level, it makes the offloading ratio of the app lication, which has relatively low effective data rate and high traffic generation ratio, high. Fig. 3 compares the proposed algorithm with the conventional algorithm in offloading ratio. The offloading ratio of conventional algorithm is average value for 3 scenarios. The offloading ratio is 0.221 and 0.26 in each conventional and proposed algorithm. It means that the proposed algorithm can efficiently satisfy the effective data rate of application with similar offloading ratio. In proposed algorithm, it uses social

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