[IEEE 2014 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) - Odessa, Ukraine (2014.5.27-2014.5.30)] 2014 IEEE International Black Sea Conference

Research and Development the Methods of Quality of

Service Provision in Mobile Cloud Systems

Mykhailo Klymash

Department of telecommunications

Lviv Polytechnic National University

UKRAINE, Lviv, 12 Bandery Str.

E-mail: [email protected]

Mykola Beshley




E-mail: [email protected]

Bohdan Strykhalyuk




E-mail:[email protected]

Taras Maksymyuk




E-mail:[email protected]

Abstract—Mobile Cloud Computing (MCC) is an integration

of cloud computing in the mobile environment, which is used to

improve the overall performance of the mobile devices. The rapid

growth of cloud computing and mobile Internet services has

triggered the emergence of mobile cloud services. Among many

challenges, QoS management is one of the crucial issues for

mobile cloud services. Here the survey of the current research

related to QoS provision in mobile cloud computing is presented.

The provision of QoS in mobile cloud computing is a challenging

task. This is because of the dynamic characteristics of mobile

networks and limited resources of mobile devices such as

bandwidth, delay, packet loss ratio, battery life, storage capacity,

computational power, security, etc. Also we present a cloud

aware multimedia, which addresses how multimedia services and

applications, such as storage and sharing, authoring and mashup,

adaptation and delivery, and rendering and retrieval, can

optimally utilize cloud-computing resources to achieve better

quality of experience (QoE). The traffic of Lviv Polytechnic

enterprise network have been analyzed. Simulation model

processing of traffic in queues heterogeneity network on the basis

of services priorities has been developed.

Keywords—mobile cloud computing; quality-of-service;

quality of experience; multimedia; traffic.

I. INTRODUCTION

Nowadays, mobile devices such as cell phones, smartphones, tablets, etc. are rapidly spreads becoming an important part of human life. Thus, new emerging mobile cloud computing (MCC) conception [1] is a promising solution for effective and convenient communication without any restrictions by time and place. The rapid development of MCC becomes a powerful trend in the development of ICT technology as well as commerce and industries. However, there are still a lot of challenges for mobile devices performance such as battery life, storage capacity, processing power and secure communication capability [2]. Quality of service significantly limited by lack of available resources. MCC faces a number of challenges at both users’ and computing side. One

of the most important challenges is Quality-of Service (QoS) provision. Multimedia transmission over cloud infrastructure is a hot worldwide research topic. Multimedia cloud provides video streaming, VoIP and other kinds of multimedia traffic through mobile or fixed networks. This feature allows reliable convergence of telephony, video and audio transmission as well as computing and broadband transmission under single cloud.

II. QOS PROVISION FOR MOBILE CLOUD COMPUTING

A. MCC based on A-IMS architecture

More and more telecommunication service providers have adopted IP Multimedia Subsystems (IMS) due to develop the converged Next Generation all-IP access network (NGN) (Fig.1).

ALL/IP

RAN-GW

2G 3G 4G

INTERNET

QoS control

for MCC

Wi-FiFemtocell

network

IMS

Fig.1. A-IMS architecture for Mobile Cloud Computing

Mobile cloud computing is used in the highly heterogeneous networks [3]. Separate users get access to the cloud service through different access technologies such as General Packet Radio Service (GPRS), Wideband Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMAX), and Wireless Local Area Networks (WLAN), Long Term Evolution (LTE). The next generation of optical transport

2014 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

978-1-4799-4067-7/14/$31.00 ©2014 IEEE 160

networks able to aggregate different data flows and provide the effective QoS management through transparent virtual channels [4]. Therefore, we propose the converged advanced IMS (A-

IMS) network architecture for mobile cloud computing. IMS

network structure logically divided onto 5 layer: access,

transport, control layer, service (application) and cloud

computing layer.

B. QoS provision in Mobile Cloud Computing Systems

The QoS provision task in mobile cloud computing systems is of great interest because of the dynamic characteristics of mobile networks such as bandwidth, delay and packet loss ratio. Moreover, the limited resources of mobile devices such as battery life, storage capacity, computational power, security, etc., set a threshold for achievable QoS.

IMS architecture provides service invocation, mobility management, QoS guarantee and security mechanism for mobile cloud services. The real-time telecommunication services requires significantly higher QoS levels and mobility management in the cloud computing environment. These features can be provided by the IMS regardless of the access network. On the other hand, the common telecommunication service providers use cloud computing to realize the elastic resource utilization, especially for services shared by a large amount of users such as Voice over IP (VoIP), location services, video conferences, messaging services, image and text processing services, multimedia search and sensor data applications [5].

III. RESEARCH ON THE IMPACT OF QUEUES CONTROL

ALGORITHMS FOR SERVICE QUALITY MAINTENANCE

Service quality assurance with services differentiation is an important task for multiservice network. It requires a concerted solution of different network resources management problems. It is necessary to implement modern traffic control algorithms for simultaneous assurance of different service quality demands with the effective network resources usage. The balancing of network node resources is related with memory buffering algorithms and services priorities association [6].

A. Analysis of the traffic properties in multiservice network

In this paper, we analyzed the traffic of Lviv Polytechnic enterprise network. Practical use of this method is difficult because of absence the proper mathematical apparatus that provides an assessment of quality parameters of non-stationary load overall appearance. However, information of user traffic characteristics transmitted over the network is a prerequisite for the network design.

Fig.2.The received packets intensity of multiservice device in IMS network

Fig.3.The received packets intensity of info communication service

The distribution of total packet flow intensity is shown in Fig.3. Packets size is limited within recommended values (from 64 to 1500 bytes). Traffic consists of the voice, signaling, and interactive data packets, which characterized by small size. Contrary, IPTV, video on demand, Internet data and videoconference use large-size packets.

Fig.4.Distribution of the received total packet flow intensity

For the analysis and selection of the theoretical distribution, that best characterize experimentally obtained distribution we observe that multiservice traffic describes normal distribution (Fig.5).

Fig.5.Comparison of obtained traffic intensity outcome with normal traffic

distribution

For this distribution of m = Mx, and σ 2

= S2

,2

1)(

2

2)(

2

mx

exf

(1)

2

2)(

22

1)(

mx

exF (2)

B. Estimation method of Hurst parameter value of

multiservice network traffic

There are many methods to assess the Hurst parameter

value of random series [7]. The simplest of these is the RS-

method, which, however, is limited in case of low dispersion

processes. However, for the purposes of the problem

considered in the paper, this method able to be used.

The Hurst index values H = 0.513 – 0.604, that indicates the

self-similarity of the obtained traffic (Table.1).


161

TABLE I. RESEARCH RESULTS

Type of

Traffic

Characteristics of Network Traffic Flow

Intensity

of

incoming

traffic

[λ]

pack/s

Intensity

of out

coming

traffic

[µ]

pack/s

Variance

coefficient of

incoming

traffic

[k]

pack/s

Hurst

index

[H]

-

Device

utilization

[ρ]

-

Video conference

302,6 356,9 60,67 0,513 0,848

IPTV 521,1 541,4 106,1 0,766 0,962

WEB 201,2 205,7 41,7 0,685 0,977

VOIP 87,4 95,9 32,19 0,981 0,739

VOD 410,9 2240,0 79,78 0,839 0,183

Signaling 30,7 34,9 5,66 0,668 0,440

Aggregated

traffic 2023,6 2053,2 312,6 0,604 0,995

IV. QUEUES PROCESSING MODEL OF MULTISERVICE A-IMS

NETWORK

A. The algorithm of simulation model of queues processing

Based on the calculated data and forecasted service estimation the simulation model of queues processing network developed based on the services priorities. This model also realized as a software designed in MatLab.

Loading the data array :

Array=load(‘Traffic_Intesity_full_NULP.dat’);

Initialization of variables:i=1; N=3600; Lser=[];

BufCurrent=0; Buff=Buffer(λmean, H)

Nvtrat=[]; Tser=[]; Jitter=[];

Classification of network streams:M_Aggregate=(Mass(:,2));

M_VoIP=(Mass(:,3));M_IPTV=(Mass(:,4));M_Web=(Mass(:,5);

M_Videoconference=(Mass(:,6));M_VoD=(Mass(:,7));

M_signal=(Mass(:,8));

Jitter(i)=abs(mean(Tser(i))-Tser(i));

Pvtr(i)=Nvtrat/(sum(Mass)+Nvtrat(i));

Tser=mean(Tser(i));Jitter=mean(Jitter(i));

i<N

BufCurrent(i)=Mass(i)

BufCurrent>BuffNvtrat+=BufCurrent-Buff;

BuffCurrent=Buff;

Tser(i)=(1+BufCurrent/2)/(Mu*Lser(i));BufCurrent=BufCurrent-(Mu*Lser(i));

BufCurrent<0 BufCurrent=0;

Begin

The coefficient of variation II order: Kv

II > 1%

Initialization of arrays:Masλ=[];Pak=[][];ΔT=[];Δt=[];

Buffer1[];Buffer2[];Buffe3[];Buffer4[];Buffer5[];Buffer6[];Buffer7[];

krok=0;

τ ≥ 1мсτ=τ+ΔTkrok+Δtkrok;

λ=λ+1;krok=krok+1;

λ=0;τ=0;

The accumulation values λ

Masλ=λ;Intensity of out coming traffic µ

Determination the coefficient of variation I order:Kv

I (Masλ);Determination the coefficient of variation II order:

Kv II (Kv

I роду);

Generation of the array Pak [] [] and determine the size of packets

ΔT;Generation of the array between packet pause

Δt;

No

Determine the amount of packets entering the buffer:

µ-λ≥0

Determining the the sequence number of the package that comes with a

buffer: i=krok-λ+µ+1

Reading and checking of DSCP code

package:Pak[2][i]=001000

Pak[2][i]=010000

Pak[2][i]=011000

Pak[2][i]=100000

Pak[2][i]=101000

Pak[2][i]=110000

Buffer1[z1]=Pak[2][i]z1=z1+1



Buffer1[z4]=Pak[2[i]z4=z4+1




i=i+1krok-i<0

НіYes

Determination of the Hurst

parameter:H((Mas(:,2));

Determining the size of buffer:

Buffer(λmean, H)

End

No

No

No

No

No

No

No

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Fig.6. The queues processing algorithm based on developed simulation model

The overall structure of the code consists of the following key steps:

1. Loading the data array of the received packets

intensity.

2. Division of total observation time (in this case one

hour) at 1-second intervals.

3. Analysis of the packet bursts received within the

corresponding interval and its classification

according to certain network flow.

4. Determination of packet processing opportunity,

getting the decision of packet buffering or dropping

in a given time interval.

5. Packets servicing according to the selected algorithm.

6. Separate quality parameters calculation for each

stream and for the total flow.

B. Evaluation of multimedia quality of service parameters

Due to determine the best mechanism for prioritized

multimedia traffic service selected five algorithms service:

a) Queues processing algorithm.

b) Queues prioritization mechanism PQ.

c) An ordered multicast CQ.

d) Uniform queuing algorithm FQ.

e) Weighted fair service algorithm WFQ.

The criterion for each algorithm assessing is the ability to

best quality of service provision. These parameters defined as

follows:

1. Packet loss probability – defined as the percent

number of lost packets to the total number of packets:

1,k

k

P nNprob

(3)

where Pprob – the probability of packet loss; N – total number

of packets, nk – number of lost packets by the k-th period.

2. Delay – is the length of service package, defined as

the amount of time processing and batch mode service in the

buffer:

2 ,packR

T t tdelay buffer processV

(4)

where V – rate internal bus of servicing device (accepted that

the rate of change of the input and output level); Tbuffer –

waiting time for packet buffer; Rpack – length of the packet;

Tprocess – duration of packet processing in the processor of

servicing device; Tdelay – duration of the service packet.

3. Jitter – defined as the difference between the mean

value of the delay and concrete delay:

,1

iiT

averageTdt

N

(5)

where Ti - delay of i-th packet; Taverage - average value of

delay.

In [6] authors proposed to use the simulation for

multiservice traffic generating. The classifier analyses IP

header to detect the content of DSCP field. After reading the

DSCP the classifier have to associate an appropriate priority

with the current packet based on criterion. This procedure

continues for all packets in the arrival workflow. Then the

classifier will direct the packets into the special buffer zones


162

with fixed different lengths [8]. In current case, router will

contain seven buffer zones for every service to assure high

service quality. The essence of the proposed approach is

shown in Fig. 7.

Buffer

λn λ2 λ1

Imcoming traffic

Δr

ΔT

Δt

Classifier

IP packetIP header

DSCP code

Prioritet 1Prioritet 2Prioritet 3

Prioritet 5Prioritet 4

Prioritet 6

DSCP code

11100000

11000000

10100000

10000000

01100000

01000000

µ µ µ

Outcoming traffic

11100000

11100001

11100010

11100011

- The counter of delay package - Tdop * 70% = ti1




11100011 Packet transferring

without buffering in knots at the all way to the destination

ti1

ti2

ti3

ti4

Selector class

Prioritet 7 00100000

(001000) (010000) (011000) (100000) (101000) (110000) (110000)

Fig.7. Model of services priority association for multiservice traffic in the

node with common buffer

According to the proposed model, packets in more preferred

zones are processed firstly. Thereby, the required service

quality is assured, but this may cause for a high risk of less

preferred packets suppression by high-preferred packet flows.

This phenomenon affects significantly the service quality for

less preferred services. To solve this problem the paper

proposes to set allowable packet delay counter for each

preferred buffer zone. This counter allows establishing four

delay levels t1, t2, t3, t4 and mark packets by setting the two

reserved lowers bits in the DSCP field (t1 = tdop∙70% –

xxxxxx00, t2 = tdop∙80% – xxxxxx01, t3 = tdop∙90% –

xxxxxx10, t4 = tdop∙100% – xxxxxx11). Therefore, the packets

with critical level of buffering delay (t4 = tdop∙100%), get the

highest service priority through the all path to be delivered

without buffering at the next nodes.

1-node

2-node

3-node

Ρ=0.5

Ρ=0.55

Ρ=0.6 4-node

5-node

6-node

Ρ=0.65

Ρ=0.7

Ρ=0.75

Tdelay = K*X [ms] <100 [ms]

7-node

8-node

9-node

Ρ=0.8

Ρ=0.85

Ρ=0.9

10-node

Ρ=0.95

MCC

UsersMCC

Users

A B

Fig.8. The structure of the investigated network for forecasting of packet delay

As can be seen in figure 8 the structure of the model

constructed in order to reflect different cases of factor loading

coefficients of nodes in which there are different priority

queues with different buffer zones. The forecasting was

carried out for the traffic generated by a group of users who

use different services in varying degrees.

The formula for determining i-th service priority packets

delay through the transmission path has been proposed and is

given below (6):

( )

( )1

0 5

1 1

.

1 1 0

8і m j

Hі m j

H

H ,

M N і Havg

і ik use

k j m j

lt р

С

, (6)

where: M – number of channels between two subscribers of

the service; τik – propagation delay for i-th service priority

packet in the k – channel; N – number of switching nodes

located between two end subscribers of the service; C –

bandwidth of the j-th channel; puse.(i-m) j – probability of using

the i-th service priority in the j-th node; lavg.(i-m) j – the average

length of i-th service priority packet in j-th node; ρ –device

utilization.

Fig.9. Forecasting the delay duration for i-th services priority packets

Fig.10. Packet loss probability for each stream using an appropriate algorithm

service

Fig.11. Delay for each stream while using an appropriate service algorithm

Fig.12. Jitter of each stream while using an appropriate algorithm service

As shown from Figs.10, 11 and 12, the best algorithm for

low packet loss are FQ and WFQ, and for the lowest delay and

jitter – WFQ.

We propose the evaluation parameter due to improve the

service quality:

1,i

x i

i i

AoK k

m Ax (7)


163

where Kx – improving the quality parameter estimation for x –

th algorithm; A0i – initial value of the i-th parameter of quality;

Axi – current value and quality parameter for the x-th

algorithm; ki – coefficient importance of i-th parameter of

quality; m – the number of parameters to be compared. This

parameter shows how many times service quality may be

improved by using the proposed algorithm.

Fig.13. Packet loss probability for aggregate an appropriate algorithm service

Fig.14. Packets delay for aggregate an appropriate algorithm service

Fig.15. Jitter for aggregate an appropriate algorithm service

TABLE II. COMPARISON OF IMPROVED QUALITY PARAMETERS COEFFICIENT

FOR EACH ALGORITHM

QoS parameters KPQ KCQ KFQ KWFQ

Packet loss 0,12 2,63 1045,91 6,79

Delay 1,46 1,47 1,42 4,62

Jitter 5,85 6,15 1,46 5,68

Overall 0,33 2,45 2,16 5,56

Thus, the use of CQ or FQ algorithms allows improving the

quality of service more than twice. The WFQ algorithm gain is

more than five times.

Fig.16. Maximum number of network nodes, which assure the demanded QoS

We propose the formula for determining the maximum

number of nodes due to assure demanded QoS:

1

0 5

1 13

.

0

810і m

Hі m

H

H ,

і Hser

nodes dop use

m

lX t р

С

(8)

where: tdop. – an allowable services delay as recommended by

ITU-T. Figures 9, 16 show the forecasting of i-th service

priority packets delay and the number of network nodes, which

assure the demanded QoS for different values of the average

utilization.

V. CONCLUSION

In this article, the converged heterogeneous A-IMS network architecture for mobile cloud computing has been proposed. A-IMS provides an IP-based control plan for the mobile cloud computing environment, especially QoS guarantee and mobility management. Service quality assurance with services differentiation is an important task for MCC. It is necessary to develop and implement new algorithms for traffic control, which provides simultaneous assurance of different service quality demands with the effective network resources usage. The balancing of network node resources provided with memory buffering algorithms and services priorities association. The traffic of enterprise network have been analyzed and simulation model for traffic processing based on services priorities has been developed. Evaluation of multimedia quality of service parameters has been conducted. The packet delays in network node and the maximum number of nodes calculated due to assure the demanded service quality.

REFERENCES

[1] A. Sheik ali, Dr.K.Baskaran. “A Survey on Quality-of-Service Provision in Mobile Cloud Computing”, International Journal of Societal Applications of Computer Science, pp.428-433, July 2013.

[2] B. Ramesh, N.Savitha, A.E.Manjunath. “Mobile applucacations in multimedia cloud computing” Int.J.Computer Technology & Applications, vol. 4, No.197-103, Jan-Feb 2013

[3] N. Fernando, S. Loke, and W. Rahayu, “Mobile cloud computing: A survey,” Future Generation Computer Systems, vol. 29, No. 1, pp. 84–106, 2012.

[4] S. Dumych, P. Guskov, T. Maksymyuk and M. Klymash, “Simulation of characteristics of optical burst switched networks”, In Proc. IEEE International Conference on Microwave & Telecommunication technology (CriMiCo’2013), Sevastopol, pp. 492-493, Sep. 2013.

[5] Q. Qi, Y. Cao, “Cloud service-aware location update in mobile cloud” EBUPT Information Technology Co., Ltd., Beijing, P.R. China, 2013.

[6] M. Klymash, M. Beshley, O. Lavriv. “Model of network resources management on the basis of services priorities association”, In Proc. of IEEE International conference (CADSM’2013). Polyana-Svalyava, pp.172-173, 2013.

[7] M. Klymash, O. Lavriv, B. Buhyl, Y. Danik, “Service Quality Oriented Method of Multiservice Telecommunication Networks Design”, In Proc. of 11th International Conference Modern Problems of Radio Engineering, Telecommunications and Computer Science TCSET’2012. February 21-24, Lviv, pp. 235-236, 2012.

[8] M. Klymash, M. Beshley, V. Koval. “The model оf prioritization of service for efficient usage of resource multiservice network”, In Proc. of 11th International Conference Modern Problems of Radio Engineering, Telecommunications and Computer Science TCSET’2012, Lviv, pp. 320-321, Feb. 2012.


164

Documents

[IEEE 2014 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) - Odessa, Ukraine (2014.5.27-2014.5.30)] 2014 IEEE International Black Sea Conference