Application of ELECTRE to Network Selection in A ... - IEEE Xplore

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2007 proceedings.

Application of ELECTRE to Network Selection in A Hetereogeneous Wireless Network Environment Farooq Bari ([email protected], [email protected]) Victor Leung ([email protected]) Dept. of Electrical & Computer Engineering, The University of British Columbia Vancouver, BC, Canada V6T 1Z4 Abstract — Inter-working of existing packet switched wireless access technologies can help make services ubiquitously available. However this means that the services will have to be delivered over a heterogeneous mix of access technologies. There are several technical challenges that have to be overcome in such an environment, with selection of an optimal service delivery network being one of the most important issues. Choosing a nonoptimal network can result in problems such as the use of expensive access types or poor service experience. Multi Attribute Decision Making (MADM) algorithms have been considered in the past to rank the candidate networks in a preference order. While many types of MADM algorithms exist, the decision maker may choose to use a particular type of algorithm to solve a decision problem based on an assessment of the suitability of the algorithm to the problem space. This paper adapts ELECTRE, a type of MADM algorithm that performs pair-wise comparisons amongst the alternatives, to solve the problem of network selection. The algorithm has been modified so that it is able to provide complete ranking of networks even in scenarios where the utility of some attributes is non-monotonic. The algorithm has been evaluated by applying it to a network selection scenario in a heterogeneous wireless network environment.

I. INTRODUCTION Mobile users expect communication services to be available everywhere and all the time. The homogeneous networks of today are unable to provide such ubiquitous service availability in an optimal manner, e.g., from a cost, quality-of-service (QoS), or coverage perspective. Inter-working of existing packet switched wireless access technologies using the Internet Protocol (IP) as the transport can help achieve such universal service availability. However services will have to be delivered over a heterogeneous mix of access technologies. Users with multimode devices can find themselves in situations where they have to choose a service delivery network from amongst multiple network operators with different access technologies serving the same geographic area. Example of this scenario is that of a dual mode device supporting wireless local area network (WLAN) and Universal Mobile Telecommunication Service (UMTS) being used in a public space where networks with both technologies are available for use. There are several technical challenges that have to be overcome in order to provide a good service experience in such an environment, with selection of an optimal service delivery network being one of the most important issues. It is an area of active research and a topic of discussion in several standardization forums [1]. Apart from the issues arising out of architectural differences

for different access technologies (e.g., roaming, charging, multiple authentication mechanisms, and credentials), the problem of network selection mainly relates to the variability of QoS while delivering services over IP transport using a variety of access technologies. Unlike circuit switched systems, packet switched access systems provide a varying degree of QoS depending upon the underlying access system capabilities and network congestion levels. Depending upon a number of factors such as the use of licensed or unlicensed spectrum, appropriate roaming agreements, etc., the transport costs for the system can also vary widely. Therefore the ability to provide good and consistent customer experience in QoS demanding applications such as Voice over IP (VoIP) or streaming media would depend upon the ability to select the most optimal delivery network. The decision of network selection is influenced by the optimization objectives of the decision maker. Selection of a non-optimal network can result in problems such as the unnecessary use of expensive access types or poor service experience. II.

EVALUATING MADM ALGORITHMS FOR USE IN NETWORK SELECTION The decision process for the selection of a service delivery network takes into consideration several factors related to, e.g., access network capabilities, the current network conditions and transportation costs. Researchers have considered the use of Multi Attribute Decision Making (MADM) algorithms [2][3] to rank the candidate networks in a preference order. There are numerous types of MADM algorithms with [2] documenting thirteen of them. Several alternate MADM algorithms can be suitable for solving a decision problem and the decision maker in this situation can be faced with the task of selecting the most appropriate method from amongst a number of feasible methods. Classification of MADM algorithms into categories [2] can help to eliminate the algorithms in categories that are not well suited to the problem space, but this process does not provide the most suited algorithm. It is conceivable that a suitable MADM algorithm may be selected for a particular decision problem based on one or both of the following criteria. A. Accuracy of the results obtained from an algorithm For a variety of reasons different algorithms, when applied to the same problem under the same assumptions, can result in different rankings of the alternatives. In such scenarios it is not possible to objectively rank the MADM algorithms for their ranking accuracy as it would require the use of another MADM

1525-3511/07/$25.00 ©2007 IEEE

3813


algorithm to get such a ranking. For this reason it has been found difficult to use accuracy of results as a criterion in selecting a specific type of MADM algorithm. B. Appropriateness of applying the algorithm to the problem Because of differences in the approaches used by different MADM algorithms, a direct comparison amongst them is difficult. It has been proposed in the past that a method which is capable of solving the decision problem and whose decision making philosophy reflects the values of the decision maker can be considered to be the best suited. Decision makers in general prefer deterministic algorithms that provide reliable results based on a simple and easy to understand philosophy. This paper describes the use of ELECTRE [2][8], a type of MADM algorithm, to the problem of network selection. ELECTRE algorithms perform a pair-wise comparison amongst the alternatives using each of the attributes under consideration, an approach that is very popular with decision makers because of its deterministic nature and a simple philosophy. Other MADM algorithms such as TOPSIS [2][7] and SAW [2][7] have different decision making strategies but share the trait of simplicity in their philosophy with ELECTRE. Compared with these MADM algorithms, GRA algorithms [4][5][6] are more recent and the philosophy behind them are less intuitive and more complex. It is based on Grey Systems Theory, which can best be compared to fuzzy mathematics and probabilistic decision making approaches. GRA provides a measure of similarity of a set of values to a set of reference values. The concept of reference values, as will be discussed in a later section, is very useful in network selection. Other MADM algorithms, such as ELECTRE, TOPSIS and SAW, do not have this capability as their comparison processes assume a monotonically increasing or decreasing level of importance (utility) associated with the attribute values. Therefore despite a much more abstract decision philosophy of GRA, it has been applied to the problem of network selection. The standard ELECTRE algorithm as indicated above has some shortcomings that if properly addressed would make it very attractive for application to the problem of network selection because of its simple decision making philosophy. ELECTRE assumes a monotonic utility and does not provide a complete ranking of all the alternatives either, which would be needed to find the top ranking candidate network. In this paper an alternative approach to apply the ELECTRE algorithm has been developed so that it now provides a complete ranking of the networks under consideration. The algorithm has also been modified to make it suitable for application to scenarios where the utility of some attributes is non-monotonic. This change allows the application of the algorithm in scenarios where the decision maker would like to optimize the network selection to select the alternative that has attributes closest to a reference set of attribute values. For example, it may be desired by the decision maker to select for web browsing the network that is not the best alternative from a QoS or cost perspective, but has attributes closest to the reference values for a desired network for web browsing as perceived by the decision maker. The modifications to the ELECTRE algorithm described in the next section would allow such selections and make it very well suited for ranking candidate networks for network selection.

III.

APPLICATION OF MODIFIED ELECTRE TO NETWORK SELECTION ELECTRE was developed by Bernard Roy [8] in the 1960s as a practical decision making tool and has found vast applications in engineering decision making problems. The method performs pair-wise comparisons among alternatives for each one of the attributes separately to establish outranking relationships between the altenratives [2][9]. In order to formulate network selection as a MADM problem the factors impacting the decision process have to be determined. Table I provides the attributes used in the network selection decision process in this paper, along with their brief description. TABLE I.

ATTRIBUTES USED IN NETWORK SELECTION

Attribute Abbrev Brief Explanation Cost per Byte CB Data transport cost on a particular access system Total Bandwidth TB Overall bandwidth of the wireless access link Allowed AB bandwidth allowed by the access system on a per Bandwidth user basis Utilization U current utilization of the wireless link Packet delay D average packet delay within the access system Packet Jitter J Average packet delay variations within the access system Packet Loss L average packet loss rate within the access system

Using these attributes, from a decision making perspective attributes of the i-th candidate network can be represented by a vector as follows, NW i =  CB i

TB i

AB i

Ui

Di

Ji

L i 

For N alternative networks to be considered in the selection process, a matrix can be formulated as follows,  CB1  CB  2  . NW =   .  .  CBN

TB1 TB2

AB1 AB2

U1 U2

D1 D2

J1 J2

. .

. .

. .

. .

. .

. TBN

. ABN

. UN

. DN

. JN

L1  L2   .   .  .   LN 

In order to best match the optimization objectives described in the previous section, we modify the basic ELECTRE method by utilizing a reference network, which can be considered to be an access network that has a desired set of attribute values given in the reference attribute vector. This reference attribute vector is used to adjust the raw attribute values for the alternative networks before they are compared. The source of reference attribute values can be different depending upon the decision process used. For example, it is possible that the information is provided by the user terminal via indication of the service it wants to initiate. In another scenario the information can be provided by the user home operator via its knowledge about the subscribed QoS in the user profile. We can represent this reference access network as, N W re f =

( C B re f

T B re f

AB

re f

U re f

D re f

J re f

L re f

)

In the proposed modification to the algorithm, the value of each of the attributes in matrix NW is compared with a corresponding reference attribute value. An absolute difference between the two values is taken to calculate a new matrix as

3814


follows. In standard ELECTRE algorithm this step would be skipped with the assumption of a monotonically increasing or decreasing utility for the raw attribute value. |CB1 -CBref | |TB1 -TBref | |AB1 -ABref | |CB -CB | |TB -TB | |AB -AB | 2 ref 2 ref  2 ref  . . . NWAdjusted = . . .   . . .  |CBN -CBref | |TBN -TBref | |ABN-ABref |

|U1 -Uref |

|D1-Dref |

|J1-Jref |

|U2 -Uref | .

|D2 -Dref | .

|J2 -Jref | .

.

.

.

. |UN-Uref |

. |DN-Dref |

. |JN-Jref |

|L1-Lref |  |L2 -Lref |   .  .   .  |LN-Lref | 

In order to remove the impact of use of different measurement units (e.g., dollars/byte for transportation cost vs. milliseconds for latency or jitter) the attributes represented in the matrix have to be normalized. Since in the proposed modification to the ELECTRE algorithm, the raw attribute values have been adjusted with respect to the reference attribute values, it can now be assumed that for the adjusted values, the larger the attribute value, the farther it is from the desired or the reference value. In other words, all attribute values can now be considered to have a monotonically decreasing utility. Since a lower value for an adjusted attribute is considered an indication of a better network in the selection process, each attribute Xi in row i of a specific column of the matrix can be normalized as follows, Xi =

max {X j } − X i j =1…N

max {X j } − min {X j } j =1…N

j =1…N

A normalized matrix with these normalized values as its elements is created as follows.  CB1   CB 2  . NW =   .   .   CB N

TB 1

AB1

U1

D1

J1

TB 2

AB 2

U2

D2

J2

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

TB N

AB N

UN

DN

JN

L1   L2   .  .   .   L N 

During the process of overall comparison of alternatives, the impact of pair-wise comparison of different attributes is summed up. This summation should take into consideration the relative importance of each of the attributes involved in the decision about network selection. The information about the relative importance of the attributes can have similar sources as described earlier in generating a reference attribute vector. For example, a user may request the use of VoIP service whereby the relative importance of transportation cost, total bandwidth is considered low because of VoIP being a low bit rate application. However factors such as latency, jitter are quite important for the VoIP type service. On the other hand the weight related information can also come from the user profile that can, e.g., indicate the user to be a Bronze user and hence assign a higher weight to the cost attribute and lower weights for the latency and jitter attributes. Therefore, depending upon the information about the service to be used or the user QoS profile, the j-th attribute is assigned a weight wj, such that

W = w CB + w TB + w AB + w U + w D + w J + w L = 1 Using the assigned weights, an updated matrix is calculated as follows. wCB *CB1 wTB *TB1 wAB *AB1  wCB *CB2 wTB *TB2 wAB *AB2  . . . NWwt =   . . .   . . .  w *CB w *TB w *AB  CB N TB N AB N

wU *U1

wD *D1

wJ *J1

wU*U2

wD *D2

wJ *J2

.

.

.

.

.

.

.

.

.

wU *UN

wD *DN

wJ *JN

wL *L1   wL *L2   .  .   .   wL *LN 

In order to compare the network alternatives, the concept of concordance and discordance has been introduced in ELECTRE, which are measures of satisfaction and dissatisfaction of the decision maker when one alternative is compared with another. Thus concordance and discordance sets are calculated, where a concordance set (CSet) provides a list of attributes for which an alternative network under consideration is better than the other alternative network it is being compared with, and a discordance set (DSet) on the other hand provides a list of attributes where the alternative network under consideration is worse than the compared alternative. For example when network 1 is being compared with network 2, the concordance set CSet12 is the subset of all attributes that indicate that network 1 should be preferred over network 2, and the discordance set DSet12 is the subset of all attributes that indicate a preference of network 2 over network 1. Mathematically this can be represented as follows,

CSet12 = {j : (NWnorm )1,j >= (NWnorm )2,j } DSet 12 = { j : (NWnorm )1,j < (NWnorm )2,j } Using the concordance and discordance sets, corresponding matrices are constructed. ELECTRE calculates the elements of concordance matrix C as follows,

∑

ckl =

wj

j∈CSetkl

The Concordance matrix C can be represented as,

− C  21 C= .   . CN1

C12

.

.

− . .

. . .

. . .

CN2

.

.

C1N  C2N  .   .  − 

The entries for the concordance matrix are not defined for the diagonal. Similarly, ELECTRE defines the elements of Discordance matrix as follows, dkl =

∑

(NWnorm )kj − (NWnorm )lj

j∈DSetkl

∑ (NW

) − (NWnorm )lj

norm kj

j

Similarly, the discordance matrix D can be represented as,

3815


− D  21 D= .   . DN1

D12 − . . DN2

. . . . .

D1N  D2N  .   .  − 

. . . . .

The entries for the discordance matrix are also not defined for the diagonal. Two possible approaches can be considered to proceed further in decision making based on the ELECTRE algorithm. A. Approach 1 In the standard ELECTRE method, the outranking calculations are performed as follows. Concordance and discordance dominance matrices are determined. The concordance dominance matrix is calculated using a threshold value for the concordance index. A way to determine threshold value, Cthreshold, is to use the average concordance index as follows,

C threshold =

N

N

k =1

l=1

∑ ∑c

kl

N * (N − 1)

Using the Cthreshold value, elements of concordance dominance matrix, Cdom, are calculated as follows,

(C dom )kl = 1 : c kl >= C threshold

(Cdom )kl = 0 : c kl < C threshold The discordance dominance matrix is calculated using a similar threshold value, Dthreshold. This value can be calculated using a similar formula as follows, N

N

k =1

l=1

∑ ∑d

kl

Dthreshold =

N * (N − 1)

Using the Dthreshold value, elements of the discordance dominance matrix, Ddom, are calculated as follows,

(D dom ) kl = 1 : dkl >= D threshold (Ddom )kl = 0 : dkl < D threshold The aggregate dominance matrix, Adom, is calculated as follows,

(A dom )kl = (Cdom )kl * (Ddom )kl The aggregate dominance matrix is able to provide partial preference ordering of the access networks under consideration. For example if (Adom)12 = 1, then this would imply that network 1 is preferred over network 2 when both concordance and discordance criteria are used. A problem with this approach in formulating the ELECTRE algorithm is the arbitrary selection of threshold values. These

threshold values can significantly impact the outcome of the algorithm. In addition the results of this ELECTRE method do not provide a complete ranking for all the alternatives. B. Approach 2 The complimentary analysis in [2] tries to address the shortcomings of approach 1. A new parameter Ci, called the net concordance index is calculated. Ci is a measure of dominance of an alternative i over other alternatives when compared with a measure of dominance of other alternatives over the alternative i. It can be calculated as follows, N

N

j =1 j ≠i

j =1 j ≠i

Ci = ∑ Cij − ∑ C ji Similarly, the term net discordance index Di, is defined as a measure of relative weakness of alternative i over other alternatives when compared with a measure of weakness of other alternatives from the alternative i. N

N

j =1 j ≠i

j =1 j≠i

Di = ∑ Dij − ∑ D ji An alternative with the highest value of net concordance index C and lowest value of net discordance index D would be preferred. It is possible that the alternative with the highest value of concordance index is not the same as that with the lowest value of discordance index. In order to address this issue, the alternatives are ranked based on the concordance and discordance indices and each alternative is ranked by taking the average of these two rankings. The alternative with the highest average ranking is considered to be the best alternative. Alternatives with same average ranking would be considered equally suited. For the case of network selection, in order to find the top candidate network, it is required to have a complete ranking for all the networks under consideration. Therefore approach 2 has been applied as it provides a clearer ranking of the alternatives. IV.

EVALUATION OF MODIFIED ELECTRE

In order to evaluate the use of ELECTRE as well as the impact of the proposed changes to the algorithm, we consider a network selection situation with five network types to choose from. The attribute values for these networks, determined at the time of network selection, are provided in Table II. TABLE II.

Ntw #1 e.g. UMTS Ntwk#2 e.g. 802.11b Ntwk#3 e.g. 802.11a Ntwk#4 e.g. 802.11n Ntwk#5 e.g. 4G

ATTRIBUTE VALUES FOR SCENARIOS UNDER CONSIDERATION CB % 100

TB Mbps 2

AB mbps 0.2

U % 10

D Msecs 400

J msecs 50

L per 106 100

20

11

1

20

200

25

20

10

54

2

20

100

15

15

5

100

5

40

150

30

20

30

100

5

20

100

20

15

3816


Figure 1. Weights associated with attributes for different services

We consider the scenario where network selection is influenced by the requested service indicated by the user. Three services, namely VoIP (low bit rate, real-time), streaming (high bit rate, soft real-time), and web browsing (varying bit rate, bursty, non real-time) are considered. The service type is used to assign attribute weights. For example in the case of VoIP, since it is a low bandwidth application, the total bandwidth and available bandwidth are not considered important and therefore assigned a weight of zero. Also transport cost is not considered significant because of the higher revenue generating nature of VoIP applications but attributes such as low latency and jitter are quite significant for good customer experience and therefore assigned higher weights. The values of assigned weights for different services considered in simulations are provided in Figure 1. For the case of the modified algorithm, different reference values for the attributes are used for each of the service type. These reference values indicate the preferred attribute values for the service type. Table III provides these values for VoIP, streaming and web browsing applications. TABLE III.

REFERENCE ATTRIBUTE VALUES FOR VOICE OVER IP, STREAMING AND WEB BROWSING SERVICES CB

TB

AB

U

D

J

(%) (mbps) (mbps) (%) (ms) (ms)

L (per 106)

VoIP

5

100

0.02

10

100

15

15

Streaming

5

100

1

10

400

50

50

Web Browsing

5

100

0.1

10 1000 100

100

The results of Matlab simulations are documented in Tables IV and V. As described in alternative 2 of Section II, the rankings shown in these tables are averages of two rankings obtained using concordance and discordance indices. So the highest rankings in these tables may not actually be 1 unless there is a network which is the best both from the perspective of concordance and discordance indices. Table IV shows the network rankings when a standard version of ELECTRE as described in Section II of the paper was used with approach 2. The results show that the same network, i.e., #3 is being selected for all the services although the rankings for the rest of the networks are different for the services; e.g., the second ranked network for VoIP service is different than that for streaming or web browsing service. The rankings for networks using the modified version of the algorihtm that uses reference attribute values (as shown in Table III) is shown in Table V. The selected network rankings in this case are different for VoIP and streaming. For web browsing, three networks are ranked at the same level. To further explain the reason for the

selection of different networks by the algorthm Tables VII, VII and VII are provided. These tables show how the input attribute values are changed by the use of reference values, normalization and then use of attribute weights. It can be seen from these tables that the adjusted, normalized and weighed attribute values that form the input to the algorithm are quite different in the case of VoIP, streaming and web browsing services. This reflects the effect of data manipulation performed to meet the optimization objectives of the decision maker. As a result, different networks can get selected for different service types. TABLE IV.

RANKING FOR NETWORKS USING STANDARD ELECTRE METHOFD WITH ALTERNATIVE 2

Ntwk#1 e.g. UMTS Ntwk#2 e.g. 802.11b Ntwk#3 e.g. 802.11a Ntwk#4 e.g.802.11n Ntwk#5 e.g 4G TABLE V.

VoIP Streaming Web Browsing 5 3 5 3 2 2 1 1 1 4 5 2 2 4 4

RANKING FOR NETWORKS WHEN USING MODIFIED ELECTRE METHOD WITH ALTERNATIVE 2

Ntwk#1 e.g. UMTS Ntwk#2 e.g. 802.11b Ntwk#3 e.g. 802.11a Ntwk#4 e.g.802.11n Ntwk#5 e.g 4G

V.

VoIP Streaming Web Browsing 5 2 4 3 1 2 1 3 2 4 4 2 2 4.5 5

CONCLUSIONS

The paper has highlighted the important problem of network selection that exists today while delivering services on a heterogeneous mix of wireless access technologies. It has described adaptation of ELECTRE, an MADM algorithm, for ranking network alternatives during the network selection process. The use of a particular MADM algorithm for a specific problem is based on an assessment about the appropriateness of the algorithm for application to the problem space. The paper has suggested use of modified algorithm that provides a complete ranking of alternative networks. The modifications also allow usage of ELECTRE with attributes exhibiting a non-monotonic utility. These modifications make the algorithm more adept to application in network selection as it expands its applicability to wider range of optimization objectives. Such network selection scenarios are of special importance in a heterogeneous wireless network environment being used to deliver a variety of service types. With these modifications and a simple decision making philosophy, ELECTRE is an ideal algorithm for use in network selection. In order to evaluate the proposed use of the algorithm, an example has been described where depending upon the QoS requirements of the service being requested by the user device, a different delivery network maybe chosen. The results have been compared with and without implementing the modifications to support non-monotonic utility of attributes. The proposed algorithm can be used with the network selection architecture described in [10].

3817


VI.

TABLE VII.

REFERENCES

J. Arkko, B. Aboba, J. Korhonen, F. Bari, Network Selection and Discovery Ppoblem, http://www.ietf.org/internet-drafts/draft-ietf-eapnetsel-problem-05.txt [2] C.L. Hwang, K. Yoon, “Multiple Attribute Decision Making: An Introduction”, Sage Publications, 1995. [3] E. Triantaphyllou, Multi-Criteria Decision Making Methods: A Comparative Study, Kluwer Academic Publishers, 2002. [4] Q. Song and A. Jamalipour, “Quality of Service Provisioning in Wireless LAN/UMTS Integrated Systems using Analytic Hierarchy Process and Grey Relational Analysis”, Proceedings of IEEE GlobeCom, Dallas, TX, Nov./Dec. 2004. [5] Q. Song and A. Jamalipour, “Network selection in an integrated wireless LAN and UMTS environment using mathematical modeling and computing techniques”, IEEE Wireless Communications, Volume 12, Issue 3, June 2005. [6] Q. Song and A. Jamalipour, “A network selection mechanism for next generation networks” Proceedings of IEEE International Conference on Communications (ICC), May 2005. [7] E. Stevens-Navarro and V.W.S. Wong, "Comparison between Vertical Handoff Decision Algorithms for Heterogeneous Wireless Networks," Proceedings of IEEE Vehicular Technology Conference (VTC-Spring), Melbourne, Australia, May 2006. [8] B. Benayoun, B. Roy, and B. Sussmann, , “Manu al de reference du programme electre, Note de Sythese et Formation,” Direction Scietifique SEMA, N. 25, 1966. [9] M. Rogers, M. Bruen and L.-Y. Maystre, ELECTRE and Decision Support – Methods and Applications in Engineering and Infrastructure Investment, Kluwer Academic Publishers, 2000. [10] F. Bari and V.C.M. Leung, “Service delivery over heterogeneous wireless networks: network selecton sspects”, Proceedings of ACM IWCMC, Vancouver, Canada, July 2006.

[1]

TABLE VI.

#1 #2 #3 #4 #5

CB % 95.00 15.00 5.00 0.00 25.00

#1 #2 #3 #4 #5

CB % 0.000 0.842 0.947 1.000 0.737

#1 #2 #3 #4 #5

CB % 0.000 0.168 0.190 0.200 0.147

VOICE OVER IP SERVICE

TB Mbps 98.00 89.00 46.00 0.00 0.00

J msecs 0.00 25.00 35.00 20.00 30.00

L per 106 50.00 30.00 35.00 30.00 35.00

#1 #2 #3 #4 #5

TB mbps 98.00 89.00 46.00 0.00 0.00

U % 0.00 10.00 10.00 30.00 10.00

D Msecs 300.00 100.00 0.00 50.00 0.00

J msecs 35.00 10.00 0.00 15.00 5.00

L per 106 85.00 5.00 0.00 5.00 0.00

#1 #2 #3 #4 #5

CB % 0.000 0.842 0.947 1.000 0.737

NORMALIZED ATTRIBUTE VALUES TB AB U D Mbps Mbps % Msecs 0.000 1.000 1.000 0.000 0.092 0.833 0.667 0.667 0.531 0.625 0.667 1.000 1.000 0.000 0.000 0.833 1.000 0.000 0.667 1.000

J msecs 0.000 0.714 1.000 0.571 0.857

L per 106 0.000 0.941 1.000 0.941 1.000

#1 #2 #3 #4 #5

CB % 0.000 0.042 0.047 0.050 0.037

NORMALIZED AND WEIGHED ATTRIBUTE VALUES TB AB U D J mbps mbps % Msecs msecs 0.000 0.000 0.200 0.000 0.000 0.000 0.000 0.133 0.200 0.214 0.000 0.000 0.133 0.300 0.300 0.000 0.000 0.000 0.250 0.171 0.000 0.000 0.133 0.300 0.257

L per 106 0.000 0.141 0.150 0.141 0.150

#1 #2 #3 #4 #5

CB % 95.00 15.00 5.00 0.00 25.00

#1 #2 #3 #4 #5

CB % 0.000 0.842 0.947 1.000 0.737

#1 #2 #3 #4 #5

CB % 0.000 0.421 0.474 0.500 0.368

U % 0.00 10.00 10.00 30.00 10.00

D Msecs 0.00 200.00 300.00 250.00 300.00

J msecs 1.000 0.286 0.000 0.429 0.143

L per 106 0.000 1.000 0.750 1.000 0.750

NORMALIZED AND WEIGHED ATTRIBUTE VALUES TB AB U D J Mbps Mbps % Msecs msecs 0.000 0.160 0.200 0.100 0.100 0.014 0.200 0.133 0.033 0.029 0.080 0.150 0.133 0.000 0.000 0.150 0.000 0.000 0.017 0.043 0.150 0.000 0.133 0.000 0.014

L per 106 0.000 0.050 0.038 0.050 0.038


ADJUSTED ATTRIBUTE VALUES

AB Mbps 0.18 0.98 1.98 4.98 4.98

AB mbps 0.80 0.00 1.00 4.00 4.00

TABLE VIII.

CB % 95.00 15.00 5.00 0.00 25.00

STREAMING SERVICE

ADJUSTED ATTRIBUTE VALUES

TB Mbps 98.00 89.00 46.00 0.00 0.00

WEB BROWSING SERVICE

ADJUSTED ATTRIBUTE VALUES AB U D mbps % Msecs 0.10 0.00 600.00 0.90 10.00 800.00 1.90 10.00 900.00 4.90 30.00 850.00 4.90 10.00 900.00

J msecs 50.00 75.00 85.00 70.00 80.00

L per 106 0.00 80.00 85.00 80.00 85.00

J msecs 1.000 0.286 0.000 0.429 0.143

L per 106 1.000 0.059 0.000 0.059 0.000

NORMALIZED AND WEIGHED ATTRIBUTE VALUES TB AB U D J Mbps Mbps % Msecs msecs 0.000 0.150 0.100 0.050 0.050 0.005 0.125 0.067 0.017 0.014 0.027 0.094 0.067 0.000 0.000 0.050 0.000 0.000 0.008 0.021 0.050 0.000 0.067 0.000 0.007

L per 106 0.100 0.006 0.000 0.006 0.000


3818