IP Flow Mobility Trigger Mechanism in Heterogeneous ... · IP Flow Mobility Trigger Mechanism in...

Journal of Information & Computational Science 10:7 (2013) 2029–2040 May 1, 2013Available at http://www.joics.com

IP Flow Mobility Trigger Mechanism in Heterogeneous

Wireless Networks

Qing Wang∗, Wenjing Li, Peng Yu, Luoming Meng

State Key Laboratory of Networking and Switching Technology, Beijing University of Posts andTelecommunications, Beijing 100876, China

Abstract

With the Multi Access Packet Data Network Connectivity and IP flow mobility introduced by 3GPP,mobile devices with multiple wireless interfaces can connect to multiple access networks simultaneously,meanwhile allowing dynamic allocation of different IP flows to different access systems corresponding totheir requirements. In order to enable IP flow mobility, an effective IP flow mobility trigger mechanismis indispensible. However, among the existing technical standard and current literatures in IP flowmobility, almost no research involves the trigger mechanism and decision algorithm. This paper proposesa mechanism for network-based IP flow mobility, which includes a trigger process and a network selectiondecision algorithm based on each IP flow. Simulation results validate the flexibility and efficiency ofour proposed mechanism, which can achieve a better network load balancing as well as a higher usersatisfaction.

Keywords: IP Flow Mobility; Trigger Mechanism; Load Balancing; User Satisfaction

1 Introduction

In recent years, the popularization of smart-phones and the rapid development of wireless networkshave created increasingly diversified applications. While some of these applications are well suitedto run over 3GPP access system (e.g. VoIP over LTE), some other applications may also be wellsuited to run over non-3GPP access system (e.g. FTP transfer via WiFi). In order to take goodadvantage of these different access networks to meet the various application requirements, peopleare focusing on the interworking and convergence of heterogeneous networks.

3GPP Rel-8 EPS [1] [2] proposed a multi access 3GPP system where different heterogeneousaccess networks are connected to a common core network. The subscriber can establish a singlePDN connection or multiple simultaneous PDN connections, but all the traffic exchanged by thesubscriber, regardless of the PDN connection it belongs to, is routed through the same accesssystem. Namely, the subscriber cannot communicate using multiple accesses simultaneously.

∗Corresponding author.Email address: [email protected] (Qing Wang).

1548–7741 / Copyright © 2013 Binary Information PressDOI: 10.12733/jics20101697

2030 Q. Wang et al. / Journal of Information & Computational Science 10:7 (2013) 2029–2040

In Rel-9 EPS, 3GPP started to study and standardize support for Multi Access Packet DataNetwork Connectivity and IP Flow Mobility. By introducing the IP flow mobility enhancementto the existing EPS mobility architecture, it will be supported that UEs with multiple interfacescould simultaneously connect to 3GPP access and non-3GPP access, allowing dynamic allocationof different IP flows to different access systems as per their requirements. Consequently, the net-work traffic management could be implemented more flexibly and precisely, based on taking eachIP flow as a mobility granularity. However, in the existing technical standard [3] [4] and research[5] [6], they only focus on introducing the architectural requirements, mobility management pro-tocol or policy for IP flow mobility, while are lack of entire IP flow mobility mechanism in detail,such as trigger strategy and IP flow mobility decision algorithm.

In addition, current papers have proposed many handover mechanisms or the interface selectionalgorithms for mobile terminals in the heterogeneous wireless networks environment. They allintend to select the optimal access network for each user [7] [8] [9]. In contrast with IP flowmobility, the granularity of handover is per user or per PDN connection, which means that all IPflows belonging to a UE or a PDN connection should be moved together and so that it is hardto meet the needs of each IP flow. As IP flow mobility makes it possible to allocate individualIP flows generated by the same or different applications to different accesses simultaneously, theuser experience can be enhanced and the available radio resources can be optimally used.

The purpose of this paper is to introduce a trigger mechanism for network-based IP flow mobil-ity. It is based on the solution of IP flow mobility in 3GPP EPS. By collecting and analyzing theinformation related to network condition, user preference and IP flow characters, the network sidewill select the most suitable network interface for each IP flow after running the IP flow mobilitydecision algorithm. Finally, in the ideal situation, it will allocate all IP flows to different accessnetworks. This approach has a significant effect on network load balancing and user satisfaction.More to the point, it further improves the solution for IP flow mobility.

The rest of paper is organized as follows. Section 2 presents the related work to IP flow mobilityissue; the ANDSF solution is presented as contrast and MADM algorithms are introduced. Section3 proposes an IP flow mobility trigger mechanism and mobility decision algorithm for IP flows.In Section 4 the simulation scenario and parameters are given. Section V presents the simulationresults. Finally, in Section 5 the major conclusions are presented.

2 Related Work

IP flow mobility makes it possible for a multi-radio UE that is connected to the EPS via differentaccesses simultaneously, sending and receiving different IP flows through different accesses. Be-sides, it allows to switching over a single IP flow seamlessly and selectively to a different access,meanwhile keeping the other ongoing IP flows on their original access.

Let us consider a typical use case as Fig. 1 which typifies IP flow mobility.

The UE is reachable through both 3GPP access and non-3GPP access and has simultaneouslyseveral active sessions through both accesses. It has a VoIP session and ftp file synchronizationwith a backup server via 3GPP access, and starting a web browsing via non-3GPP access. At somepoint, the 3GPP access becomes congested and some traffic control must be carried out. Assumethat due to updated IP flow routing rule, the ftp synchronization flow (best effort) should bemoved from 3GPP access to non-3GPP access. Only the VoIP flow with higher QoS requirement

Q. Wang et al. / Journal of Information & Computational Science 10:7 (2013) 2029–2040 2031

VoIP

FTP

Web

Internet

Intranet

Internet

Intranet

EPC

EPC

3GPP access

3GPP access

Non-3GPP

access

Non-3GPP

access

Fig. 1: IP flow mobility sketch map

is left to 3GPP access.

Consequently, it should be possible to select one access when a flow is started and re-distributethe flows to/from a UE between available accesses based on the characteristics of the flows and thecapabilities of the available accesses, subjected to the user’s preferences and operator’s policies [3].So that IP flow mobility is a “decision making” problem with multiple alternatives (networks) andattributes. Multiple Attribute Decision Making (MADM) is one of the most promising approach-es for decision making. It includes many methods such as SAW (Simple Additive Weighting),WP (Weighting Product), and TOPSIS (Technique for Order Preference by Similarity to IdealSolution) [7]. As we know, there are many factors influencing the network selection decision orthe handover decision for an UE, such as user preferences, mobile terminals’ capacities, networkconditions, operators’ policies, etc. As to IP flow mobility, besides the above factors, the decisionalgorithm could be in the view of IP flow granularity, taking the IP flow types and flow-basedquality of service requirements into consideration.

Besides, Access Network Discovery and Selection Function (ANDSF) can provide network dis-covery and selection assistance data as per operators’ policy to the UE. The UE with IFOMcapability may use the inter-system routing policies provided by ANDSF to selecting the mostpreferable access networks. All the policies are provisioned in the UE and may be updated bythe ANDSF based on network triggers or after receiving a UE request for network discovery andselection information. Moreover, the inter-system routing policies provided to the UE by theANDSF take precedence on the inter-system routing policies pre-configured on the UE. In thispaper, we proposed an improved algorithm for selecting the best access network for each IP flowin order to supplement the policies of ANDSF.

3 IP Flow Mobility Trigger Mechanism

Considering network selection decision made by UE, the network conditions are not easy toachieve due to operators’ security contain, so that the UE could not have a global vision aboutthe network resources management like load balancing. Moreover, running too many algorithmsand a lot of modification at UE will increase complexity. Our proposed mechanism, supposesthat network side is responsible for the network selection for each IP flow and the trigger of IP


flow mobility. The UE assists the decision making by informing some necessary information tonetwork, such as its available interfaces status, radio signal strength and the application QoSrequirement.

3.1 Mechanism Description

As illustrated in Fig. 2, our proposed trigger mechanism for IP flow mobility can be depicted asfollowing procedure, which is based on several modules located in the network side:

Start

Monitor and collect the network

condition information

Update the IP flow information

cache for each UE

Estimate related parameters

Judging if the trigger conditions

are fulfilled

IP flow mobility procedure is

triggered by the network side

End

No

Trigger the IP flow mobilitydecision algorithm

Judging if the IP flow routing

rule has changed

No

Resource collection module

Resource estimation module

Algorithm running module

Trigger decision module

Yes

Fig. 2: Flowchart of IP flow mobility trigger mechanism

- Resource collection module is responsible for collecting information of network capabili-ties (e.g. total bandwidth, bit error rate, etc.) and network conditions (e.g. traffic load, networkutilization) periodically from the related network management systems. Moreover, the resource


collection module can tackle the information informed from UE and generate an IP flow infor-mation cache for each UE where stores the information of UE’s available access, signal strength,ongoing IP flows, etc.

The IP flow information cache is exemplified as shown in Table 1. Note that the Flow ID isonly unique for a given UE.

Table 1: IP flow information cache

UE IDAvailable

network

Signal

strengthIP flow Flow description

UE’s IP address

assigned by

core network

Network #1 RSS1Flow ID 1 IP flow type, flow-based QoS parameters . . .

Flow ID 2 IP flow type, flow-based QoS parameters. . .

Network #2 RSS2 Flow ID 3 IP flow type, flow-based QoS parameters. . .

- Resource estimation module, which is an important component of proposed mechanism,is designed to calculate related parameters, estimate if the trigger conditions are fulfilled anddecide whether to trigger the selection decision algorithm or not. That is to say, the resourceestimation module is responsible for calculating available bandwidth of access networks (networkutilization rate) and the affected attributes’ values of algorithm, allocating different attributes’weights to different types of IP flows according to their required QoS parameters.

We conclude that the following cases will be regarded as our IP flow mobility trigger condition:

• Network interface status change.

• IP flows have been created or deleted.

• Flow-based QoS parameters values have been modified.

• User preferences or operator constraints change.

• Network performances modification.

- Algorithm running module: The operating principle of this module is described in sectionB.

- Trigger decision module: After running IP flow mobility decision algorithm, a new IP flowrouting rule will be generated. If the new IP flow routing rule is the same with the former rule,IP flow mobility will not be triggered. Otherwise, the affected IP flows will be moved accordingto the new routing rule.

3.2 IP Flow Mobility Algorithm

In heterogeneous networks environments, a decision for IP flow mobility, which is aiming at choos-ing the best interface for each IP flow, should depend on several parameters as a single factorwhich can provide a clear approach to make selection does not exist. The basic idea of our pro-posed algorithm is designed to make the decision satisfying both user/application expectationsand load balancing between the different networks at the same time. So that we use multiple


attribute decision algorithm to solve the “IP flow mobility decision making” problem with multi-ple alternatives (networks) and attributes (network characteristics, user preferences, applicationrequirements, IP flow types, costs, etc.)

1) Model description

Firstly, we model the IP flow mobility decision algorithm as follows:

N = {Ni, i = 1, 2, · · · , n} is a set of access networks available for UE.

A = {Aj, j = 1, 2, · · · ,m} is a set of attributes which influence the decision such as the networkcharacteristics and IP flow characteristics, etc.

Based on historical and current data, each attribute Aj of network Ni has a parameter valuexij.

f = {fk, k = 1, 2, · · · , l} is a set of ongoing and new IP flows for which access networks have tobe determined.

Then we define a set of weight values for each IP flow to represent the relative importance ofthe attributes respectively, as follows:

ω (fk) = {ωkj, k = 1, 2, · · · , l, j = 1, 2, · · · ,m}

There ism∑j=1

ωkj = 1 for each IP flow fk.

For different types of IP flows, different attributes are corresponding to different importancedegrees, reflecting the user/application preferences. For instance, conversation service flow has ahigher requirement in packet delay but lower requirement in packet loss rate, while ftp synchro-nization service is just the reverse.

2) Algorithm description

To select the most suitable access network for each IP flow, we propose to improve the DiAalgorithm in [7] to solve our problem. The approach is based upon the concept that the chosenalternative should have the relative shortest distance to the ideal solution, including the followingseveral steps:

Step 1: Construct the normalized decision matrix. Each element rij of the Euclidean normalizeddecision matrix R can be calculated as follows:

rij=xij√n∑

i=1

x2ij

(1)

Step 2: Construct the weighted normalized decision matrix for the IP flows belonging to an UEwhich needed to be assigned. As an example, we only choose an IP flow for analysis. This matrixV is calculated by multiplying each column of the matrix R with its associated weight ωkj.

V=

v11 · · · · · · v1m

· · · · · · vij · · ·· · · · · · · · · · · ·vn1 · · · · · · vnm

=

r11 ∗ ωk1 · · · · · · r1m ∗ ωkm

· · · · · · rij ∗ ωkj · · ·· · · · · · · · · · · ·

rn1 ∗ ωk1 · · · · · · rnm ∗ ωkm

(2)


Step 3: Determine the positive and negative ideal alternative attribute values of each attribute.These are the maximum and minimum values of attribute in each column of the matrix V .

a+j = maxj

[vij] (3)

a−j = minj[vij] (4)

Step 4: The distance between the attribute values and the positive and negative ideal valuesare measured using the Manhattan distance.

D+i =

m∑j=1

∣∣vij − a+j∣∣ (5)

D−i =

m∑j=1

∣∣vij − a−j∣∣ (6)

Step 5: We consider the minimum value of D+and maximum value of D−.

minD+ = minD+i = min

i

m∑j=1

∣∣vij − a+j∣∣ (7)

maxD− = maxD−i = max

i

m∑j=1

∣∣vij − a−j∣∣ (8)

If we consider the (D+, D−) plane, the point(minD+

i maxD−i

)is defined as the “positive

ideal alternative” (PIA). The best alternative has the shortest distance to the PIA. This absolutedistance is calculated as follow.

Rj =

√(D+

i −minD+)2

+(D−

i −maxD−)2

(9)

Step 6: The alternative having the smallest Rj value has the shortest distance to the PIA. Atlast, this corresponding access network is chosen to the specific flow.

3) Evaluation index

After using above algorithm, each IP flow could be routing due to the optimal access network.To evaluate the performance of the IP flow mobility mechanism, we intend to use two evaluationindexes respectively from two aspects: the access network load balanced degree and the usersatisfaction.

We first give some definitions:

ANAB (Access Network’s Available Bandwidth): an ANAB could be calculated as formula(10) (11).

Boccpi =

l∑k=1

Bassgik (10)

Bavaii = Bi −Boccp

i (11)


Bassgik : denotes the assigned bandwidth by network Ni for IP flow fk.

Boccpi : denotes the occupied bandwidth of network Ni.

Bi : is the total bandwidth capacity of network Ni.

Bavaii : denotes the available bandwidth of network Ni.

VANAB (Variance of Access Networks’ Available Bandwidths): the VANAB could reflect theaccess networks’ load balanced degree. By calculating VANAB in a long period of time, we canlearn whether load balancing of entire networks is achieved according to the variation of VANAB.

US (User Satisfaction): is denoted by a score utility function as follows:

S =l∑

k=1

(ωk1 · ln

Ri

Rref

+ ωk2 · lnBavai

i − bkBref

+ωk3 · lndk −Di

Dref

+ ωk4 · lnlk − Li

Lref

+ ωk5 · lnCi

Cref

)(12)

Within the equation: R is the radio signal strength of the selected network, bk is the requiredbandwidth of IP flow fk, D is the average delay, d is the maximum supported delay, L is the packetloss rate, l is the acceptable packet loss rate, C is the monetary cost, and ωk is the associatedweight of each attribute.

4 Simulation Scenario and Parameters

To study the flexibility and feasibility of IP flow mobility, we consider a UE with multiple interfacesmoving in a heterogeneous network environment formed by four available access networks (UMTS,WiFi, WiMax, and 4G) and starting four IP flows belonging to four service types every minute.

We consider five attributes associated to access networks: radio signal strength informed byUE (R), available bandwidths of network (B), packet delay (D), packet loss rate (L), and cost pertype for each network (C). The initial parameter values of the attributes are shown in Table 2.

Table 2: The attribute parameters

Network R B(Mbps) D(ms) L(per 106) C(cent/byte)

UMTS 3 1 400 100 100

WiFi 2 8 200 20 10

WiMax 3 30 150 20 5

LTE 2 80 100 15 30

For each service class of IP flows, we have assigned adequate weights values to the differentattributes as shown in Table 3. The characteristics of different IP flows are shown in Table 4.The parameters values above are taken the technical standard [10] and papers [11] together intoconsideration


Table 3: Weights assigned to different class of IP flows

Service class R B D L C

Conversational Voice 0.05 0.25 0.5 0.05 0.15

Video (Buffered Streaming) 0.05 0.6 0.2 0.05 0.1

Interactive Gaming 0.05 0.15 0.15 0.35 0.2

TCP-based 0.05 0.1 0.05 0.4 0.4

Table 4: Simulated flows characteristics

Service class Bit Rate (Kbps) Delay (ms) Loss Rate

Conversational Voice 20 150 10−3

Video (Buffered Streaming) 80 300 10−2

Interactive Gaming 5 1000 10−6

TCP-based 5 3000 10−6

5 Performance Evaluation

To evaluate the performance of our proposed algorithm, we present the simulation results andperformance comparison with the Ubique Algorithm (UA) [12]. The simulations are carried outusing MATLAB.

The simulation is performed based on the scenario and parameters described above. For sim-plicity and convenience, we assume that only four IP flows belonging to different service class arebegan every minute and the other IP flows are out of the scope of our study. When the new IPflows are started, the IP flow mobility decision algorithm will be triggered and a network interfacewill be allocated to each new and ongoing IP flow. The total simulation time is 1000 minutes.

During the simulation, we assume that the available network bandwidth is reducing as the IPflows increased. It is also assumed that when the system is underutilized, all arriving new andongoing IP flows are admitted with the highest required bandwidth b.

A. Network Load Balancing

As a first study, we have tried to run Ubique Algorithm and our proposed algorithm respectivelyto select the access network for IP flows and observe each network’s bandwidth use condition fora long period of time.

Fig. 3 shows the behavior of both algorithms in ANAB as time passed. As labels shown, the solidlines stand for the simulation result of Ubique Algorithm and the dotted lines stand for that ofDiA Algorithm. For Ubique Algorithm, we could find that the network WiMax offering the lowestcost and higher network performance is quickly occupied and overloaded at the 700th minute whilethe other networks are kept under-utilized. Moreover, the ANAB of WiFi is remaining unchangeduntil finally the better networks LTE and WiMax are overloaded, and suddenly a sharp decreaseis occurred. Indeed, the Ubique Algorithm does not adopt the concept of weights in its interfaceselection decision. It choose the best network simply by taking all parameters of the networkinto consideration, such as bandwidth, delay, cost, etc, in order to guarantee to the user to bealways best connected. Therefore, it leads generally to select the same interface for all IP flowsregardless of different application requirements.


0Time/minute

ANAB/Mbps

80

70

60

50

40

30

20

10

0100 200 300 400 500 600 700 800 900 1000

UMTS-UA

WiFi-UA

WiMax-UA

LTE-UA

UMTS-DiA

WiFi-DiA

WiMax-DiA

LTE-DiA

Fig. 3: Network bandwidth condition when using two algorithms

Then let us see the simulation results of our proposed algorithm. Due to the obvious advantagesin performance, LTE is selected as the first access network for many IP flows. As can be seen, theANAB of LTE tends to be close to that of WiMax. Besides, the moment of networks overloadoccurs much later and all the networks keep available for a longer time. With the introduction ofconcept of weights, our approach can share traffics between different access networks accordingto applications requirements and networks performances.

In addition, we could see that the lines standing for WiFi and UMTS of both algorithms arealmost overlapping. Moreover, they are nearly unchanged except that the ANAB of WiFi is re-duced slightly after the 800th minute. The reason why the ANAB of UMTS remains unchangedis that the UMTS network has an obvious disadvantage compared with other networks in thefactors we consider, especially in bandwidth. In fact, the UMTS network also has many advan-tages. For example, it has wider coverage area, higher security, etc, which has not been takeninto consideration in this paper and will be further studied in our later work.

After calculating the VANAB during the simulation time, we can conclude that our proposedalgorithm leads to network load balance more rapid and steady compared with the Ubique Algo-rithm as shown in Fig. 4.

B. User Satisfaction

In this section, we study the ability of our approach to satisfy the user’s preferences based on theformula (12). For this purpose, we select some moments to calculating the score utility functionevery 100 minutes. Furthermore, the reference values of the involved attributes are considered asfollows: Rref is 1, Bref is 1Mbps, Dref is 100 ms, Lref is 10−2, Cref is 1 cent.

Fig. 5 presents the user satisfaction by taking the score utility function as representative whenusing Ubique Algorithm and our proposed algorithm respectively. It’s obvious that the user satis-faction gained by our algorithm is much higher than that gained by Ubique Algorithm throughoutthe process. In addition, after a long period of time, the user satisfaction is close to zero whenusing Ubique Algorithm. By contrast, there is a less rapid decline of the user satisfaction whenusing Ubique Algorithm.


VANAB-UA

VANAB-DiA

0

1400

1200

1000

800

600

400

200

0100

VANAB

200 300 400 500Time/minute

600 700 800 900 1000

Fig. 4: Comparison chart for variance of available bandwidths

US-Ubique algorithm

US-DiA algorithm

Time/minute

7

6

5

4

3

2

1

00

Use

r sa

tisf

acti

on

100 200 300 400 500 600 700 800 900 1000

Fig. 5: Comparison chart for user satisfaction

6 Conclusion

The article proposes an architecture of IP flow mobility triggering mechanism in multi-accesswireless networks, in which individual IP flows, possibly carried on different access network, canbe treated separated for the mobility decision. A procedure for such triggering decision andan algorithm for selecting an access network for each IP flow are presented. The simulation isperformed to evaluate the performance of our proposed algorithm as well as the Ubique Algorithm.The simulation results show that our proposed algorithm achieves a better performance in networkload balancing due to making full use of network resources. At the same time, it has a prominentadvantage in improving the user satisfaction as a result of taking flow characters and user’spreferences into consideration.

Acknowledgements

This research is supported by the Funds for Creative Research Groups of China (61121061),


National S&TMajor Projects (2011ZX03003-002-01), Natural Science Foundation of China (61271187),NCET-10-0240, and Chinese Universities Scientific Fund (BUPT2012RC0608).

References

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[2] 3GPP TS 23.402 Architecture Enhancements for Non-3GPP Accesses

[3] 3GPP TR 23.861 v1.3.0 Multi Access PDN Connectivity and IP Flow Mobility

[4] 3GPP TS 23.261 v10.1.0 IP Flow Mobility and Seamless Wireless Local Area Network (WLAN)Offload

[5] I. T. Ahmed, S. Antoine, S. Dong, D. Barankanira, Multi access data network connectivity andIP flow mobility in Evolved Packet System (EPS), in WCNC 2010

[6] P. Loureiro, M. Liebsch, S. Schmid, Policy routing architecture for IP flow mobility in 3GPP’sevolved packet core, in IEEE Globecom, 2010

[7] P. N. Tran, N. Boukhatem, The distance to the ideal alternative (DiA) algorithm for interfaceselection in heterogeneous wireless networks, in ACM, 2008

[8] S. K. Lee et al., Vertical Handoff decision algorithms for providing optimized performance inheterogeneous wireless networks, in IEEE GLOBECOM, 2007

[9] F. Bari, V. C. M. Leung, Automated network selection in a heterogeneous wireless network envi-ronment, in IEEE Network, 2007

[10] 3GPP TS 23.203 v8.12.0 Policy and Charging Control Architecture

[11] F. Zarai, I. Smaoui, J.-M. Bonnin, L. Kamoun, Seamless mobility in heterogeneous wireless net-works, in IJNGN, 2010

[12] L. Suciu, K. Guillouard, J.-M. Bonnin, A methodology for assessing the vertical handover algo-rithms in heterogeneous wireless networks, in ACM, 2006

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