Internet DRAFT - draft-dong-qms-fag
draft-dong-qms-fag
Datagram Congestion Control Protocol Y. Dong, Ed.
Internet-Draft C. Liu, Ed.
Intended status: Informational Nanjing Univ. of Posts and Telecom.
Expires: August 19, 2016 February 16, 2016
A Dynamic Service Class Mapping Scheme for Different QoS Domains Using
Flow Aggregation
draft-dong-qms-fag-00
Abstract
This document addresses the issue of provisioning end-to-end Quality
of Service (QoS) for multimedia services over heterogeneous networks
and introduces a parametric model by using network calculus theory
for QoS class mapping between different QoS domains. Then a QoS
Mapping Scheme based on Flow Aggregation (QMS-FAG) is proposed in
this document to mitigate the information loss problem due to mapping
between QoS domains with different granularity of QoS class and to
provide efficient network resources utilization by considering user's
Quality of Experience (QoE). In QMS-FAG, the QoS requirements of
service flows are indicated by a unique FAG identifier which is
described in a service flow map of QoS parameters. With FAG
identifier and mapping executors sitting at the border of different
QoS domains, QMS-FAG allows smooth QoS class mapping between networks
with different granularity of QoS class. Both numerical analysis and
simulation studies are given to demonstrate the efficiency of the
proposed method.
Status of This Memo
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This Internet-Draft will expire on August 19, 2016.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 3
2. QMS-FAG Development . . . . . . . . . . . . . . . . . . . . . 3
2.1. QoS and QoS Class Models . . . . . . . . . . . . . . . . 4
2.2. Flow Aggregation Concept . . . . . . . . . . . . . . . . 5
2.3. An Overlay Network Paradigm . . . . . . . . . . . . . . . 5
2.4. A Typical Scenario of QoS Class Mapping over
Heterogeneous Networks . . . . . . . . . . . . . . . . . 6
2.5. MOS Value for Video Service . . . . . . . . . . . . . . . 7
3. QMS-FAG Description . . . . . . . . . . . . . . . . . . . . . 7
3.1. Parametric Model . . . . . . . . . . . . . . . . . . . . 7
3.2. Procedure of QMS-FAG . . . . . . . . . . . . . . . . . . 8
4. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 9
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
6. Security Considerations . . . . . . . . . . . . . . . . . . . 9
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 9
7.1. Normative References . . . . . . . . . . . . . . . . . . 9
7.2. Informative References . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
1. Introduction
This document proposes a unified QoS Mapping Scheme based on Flow
AGgregation (QMS-FAG) to provide better end-to-end QoS over
heterogeneous networks. Different from previous efforts, the aim of
the proposed method is to provide better flow services over
heterogeneous networks. We aim to contribute to the ongoing research
by proposing a QoS mapping scheme, based on network QoS requirements
and users' QoE. The proposed method has several advantages: (1) it
considers the asymmetrical problem between fine and coarse grained
QoS domains (Normally the fine grained QoS domain has more/finer QoS
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classes than the coarse grained QoS domain); (2) it considers QoE and
can improve users' experience by maximizing the utilization of
network resources with flexible QoS class mapping; (3) it does not
need a mapping table.
1.1. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [1].
2. QMS-FAG Development
Previous studies on QoS class mapping between different networking
technologies can be roughly classified into two categories: the
function based methods [2][3] and mapping-table based ones [4][5].
The first category that translates between the QoS parameters of
heterogeneous networks is complex, in which the effective design of
functions can highly affect the end-to-end QoS. The second category
that established mapping tables consisting of many QoS class pairs
can cause the i nformation loss due to mapping between QoS domains
with different granularity of QoS class. One shortcoming of current
approaches is incapable of utilizing network resources efficiently
because of not considering users' QoE in the QoS mapping process.
For ease of analysis, let us define the following variables:
o RN = N-dimensional real number Euclidian space;
o Pvi = overall N QoS requirements of i-th service;
o pvin = value of N QoS requirement for i-th service;
o PWhl = lower boundary of QoS class h in network W;
o PWhu = upper boundary of QoS class h in network W;
o HW = number of QoS classes in network W;
o P = a conjunction of a set of F QoS parameters;
o pf = the f-th element of P;
o Sk = the k-th Mapping Evaluator(ME);
o X = set of FAG;
o FWm = QoS description of xm in network W.
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2.1. QoS and QoS Class Models
Based on [6] and our finding, each of QoS parameters can be
parameterized by a real number (Please see Appendix A for details).
Assuming that the QoS value is ranked in order of importance in this
paper, the most important one has the minimal value and the least
important has the maximal value. Each of QoS requirements can then
be represented by a real number and the overall QoS requirements can
be represented by a vector consisting of corresponding QoS
parameters. Formally, we specify the overall N QoS requirements of
i-th service by vector Pvi, as follows:
o Pvi = [pvi1, pvi2, pvi3,..., pviN], i=1,2,3,... (1)
where i is an integer that represents index of service,pvi is in RN
represents the value of n-th QoS requirement located in RN space for
i-th service. RN denotes an N-dimensional real number Euclidian
space which is consisted of QoS parameters.
Because each QoS class has a sub-space in N-dimensional space, we use
a pair value (PWhu,PWhl) specification in our paper, which will allow
us to define range representation with acceptable QoS regions
(PWhl<=Pvi<=PWhu) and unacceptable QoS regions (Pvi<PWhl) of QoS
class h in network W with proper normalization of QoS parameters (see
Appendix A for details).Pvi<PWhl indicates the level of QoS is below
the acceptable lower boundary, with which the traffic should be
arranged for the lower class level or refused to transmit. For the
case of Pvi>PWhu, it indicates that the traffic with Pvi should be
arranged for a higher class level.PWhl and PWhu are the lower and
upper boundaries of QoS class sub-space in N-dimensional space,
respectively, whose definitions are similar to Pvi, where
h=1,...,HW;HW is the number of QoS classes in network W.
QoS influences user's QoE, which is vital for the success of
multimedia services. Furthermore, QoE is also influenced by the
human factors that often are independent of the service type [7]. As
a result, different users of the multimedia service have different
tolerance for adjusting QoS level. For some users, when enjoying a
live TV program via web (such as a football match), they probably
prefer to degrade their QoS level rather than to be denied access
directly. Therefore, users' QoE should be considered in QoS class
mapping schemes to increase the number of satisfied users in heavy
traffic load.
Depending on the individual human perception, it is somewhat
difficult to give a precise objective metric and objective estimation
method for QoE [8]. This paper will not concentrate on how to
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estimate QoE or map between QoE and QoS, which has been a hot
research topic of many other works [8][9].
We use the QoE model proposed in [9] to obtain a mean opinion score
(MOS) to rate QoE level and modify the model by substituting sender
bitrate (SBR) with bandwidth and block error rate (BLER) with packet
loss rate caused by delay and link errors. In the modified model
mean burst length (MBL) and content type (CT) have constant values of
2.5 and 0.1, respectively, which are typical values in [9] (For
details see Appendix B). The values of the coefficients of the
modified model are the same as the values of the model proposed in
[9]. In this paper, by dynamically adjusting the QoS parameter
values within threshold, we present an empirical QoS class mapping
method with QoE to demonstrate the feasibility of the proposed
method.
2.2. Flow Aggregation Concept
A flow aggregation (FAG) is defined in this work as a set of flows
with similar QoS requirements represented by a conjunction of a set
of F QoS parameters P=[p1,p2,...,pF], each associated with a QoS
constraint, that can be specified by a range representation with
acceptable and unacceptable QoS regions. We assume that the QoS
requirements of a service flow can be expressed by a vector in a
multi-dimensional space of relevant QoS parameters, and then define
this multidimensional space as a service flow map. Each FAG has a
unique identifier that can be described by the QoS information on a
service flow map.
The FAG is different from QoS class defined by global standardization
organizations in the following aspects: 1) its granularity can be
established on the fly according to QoS requirements of services and
reflects natural muster in QoS characteristic space, and is not
connected with any of the predefined QoS classes; 2) it provides a
bridge with a flexible granularity for consistent mapping between
fine and coarse grained QoS classes in order to mitigate the
information loss problem, whose efficacy will be demonstrated by
numerical analysis in Section V.
2.3. An Overlay Network Paradigm
In this section, we describe an overlay network paradigm based on the
scenario illustrated in Fig. 1. From the viewpoint of providing end-
to-end QoS guarantees, the process of QoS mapping can be imagined as
a virtual plane of QoS mapping above the traditional layers. This
plane of QoS mapping is a collection of virtual nodes connected
together by a set of virtual links to form a large virtual domain,
which is essentially a subset of the underlying network topology.
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Each virtual node is a logical abstraction of a particular physical
node that processes QoS mapping. A virtual link spans over a path in
the physical network and includes a portion of the networking
resources. By allowing multiple networks to have different QoS
domains to map QoS in the plane of QoS mapping, users in two ends
construct a virtual end-to-end path and are provided end-to-end QoS
guarantees across different QoS domains, as illustrated in Fig. 1.
In Fig. 1, the proposed Mapping Evaluator (ME) entity sits on a
gateway/router at the edge of two different QoS domains, aiming to
classify each service according to QoS requirements. Whenever ME
receives a service, it generates a corresponding FAG with P according
to QoS requirements of the service by a clustering algorithm, such as
evolutionary algorithm, and labels the FAG with a unique FAG
identifier. Then ME puts the FAG into the corresponding queue with
the same priority value. According to available network resources,
ME determines appropriate QoS class mapping between current and new
networks for the FAG by the proposed QMS-FAG.
2.4. A Typical Scenario of QoS Class Mapping over Heterogeneous
Networks
In this section, we depict a typical scenario of QoS class mapping
over heterogeneous networks.
A typical scenario of QoS class mapping over heterogeneous networks
is shown below.
Nwk A -----R1----- Nwk B -----R2----- Nwk C
Figure 1
As illustrated in figure above, we consider a scenario of three
interconnected networks (Nwk A, Nwk B and Nwk C) connected by two
gateways/routers (R1 and R2). Assume Nwk A and Nwk C are 3G UMTS
networks and Nwk B is a wireline IP-based Diffserv network. Since
audio conferencing is a typical multimedia service requiring strict
QoS requirements to set priorities at flow\packet level, here we
assume that the audio conferencing service is implemented between
user X and user Y. In source network (Nwk A), an appropriate QoS
class queue is assigned to audio according to QoS requirements. For
traditional QoS mapping, the QoS class mapping table is preset in the
gateway/router that sits at the boundary of two different QoS domains
and the audio conferencing service belongs to a certain QoS class of
current QoS domain. Whenever the gateway/router receives an audio
conferencing service, it determines an appropriate QoS class mapping
between current and new network according to the mapping table for
this audio conferencing service.
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2.5. MOS Value for Video Service
The MOS value for video service is computed as follows [10]:
o MOS =
(a1+a2*ln(SBR)+CT*(a3+a4*ln(SBR)))/(1+(a5*BLER+a6**BLER*BLER)*MBL)
(2)
where, SBR is sender bitrate, BLER is block error rate in 3G/UMTS
networks, MBL is mean burst length, CT is content type of the video
service.
Coefficients in (2) are:
+--------+--------+---------+--------+--------+--------+-----+------+
| a1 | a2 | a3 | a4 | a5 | a6 | CT | MBL |
+--------+--------+---------+--------+--------+--------+-----+------+
| 3.9560 | 0.0919 | -5.8497 | 0.9844 | 0.1028 | -0.236 | 0.1 | 0.25 |
+--------+--------+---------+--------+--------+--------+-----+------+
Table 1
3. QMS-FAG Description
3.1. Parametric Model
The proposed scheme can automatically map the FAG to the appropriate
class that has QoS resource by adjusting QoS requirements. An
attractive feature of the dynamic QoS class mapping is that the
method considers the QoE of end users by which the ME adjusts the QoS
requirements of FAG under the condition of available QoS resources.
Consider a network session being set up over the heterogeneous
networks consists of MEs S1,S2,...,Sk, the set of FAGs that will be
transmitted into the next network can be described as
o X = {x1,...,xm,...,xM}, m=1,2,...,M (3)
where xm represents the m-th FAGs, M is the number of FAG in an ME.
By a similar description to QoS class, xm can be described as
o FWm = [Pm1,...PmN], m=1,2,...,M (4)
where FWm denotes QoS description of xm in network W,
Pmn(n=1,2,...,N) represents the n-th QoS requirement of xm.
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In mapping process, the ME will map xm to class y described as below
by the function Phi(for RN->F, then x->Phi(x)). This function can be
derived as [2]
o ch=W*(||FWm-PWh||), for all h=1,2,...,HW (5)
where ch is the order of QoS class mapped according to PWm,
W=[w1,w2,...,wHW] is a weighting array which is used to describe the
characteristics of multimedia service, satisfied with w1+w2+...+wHW=1
and often gained based on the experience. But the computation is
different in the two following cases: lower and higher traffic load
cases.
3.2. Procedure of QMS-FAG
Computation is different in the two following cases: lower and higher
traffic load cases.At lower traffic load, QoS class y can be derived
as
o y = {k|ck=minh{ch}}, for all h=1,2,...,HW (6)
where k is the order of QoS class and ck is the minimum value among
all QoS classes ch, y is the order of QoS class adjusted according to
available network resources. Here, if one QoS class has a smaller
order value, the class's FAG has a better chance to transmit earlier.
At higher traffic load, the process is as follows:
The QMS-FAG scheme at higher traffic load is described in Algorithm
1.
/*Algorithm 1: The QMS-FAG scheme */
-------------------------------------------------------------------
1. The QoS class level is decreased by one
2. y is recomputed according to equations (6) based on the QoS requirements adjusted
3. If network resources for the decreased QoS class are still not enough to transmit
this FAG, go back to step 1)
4. If the MOS value is still OK (above a preset threshold MOSth) for end users,
based on Equ. (2), then, this FAG is transmitted; otherwise,
the FAG is rejected.
6. The process is stopped.
-------------------------------------------------------------------
On the whole, the algorithmic steps of QoS class mapping are as
follows:
1)If the network resource allows, ck is mapped to QoS class y based
on equations (2)
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2)If the network resource is not allowed, with the process in the
case of higher traffic load, the ME gradually reduces the order of
QoS class for xm until xm is transmitted with a lower order of QoS
class, or is rejected if no appropriate mapping is available
(assuming the lower the order of QoS class, the lower its priority).
4. Acknowledgements
The authors would like to acknowledge feedback and discussions on
service class mapping scheme for QoS with a wide range of people,
including members of the Wireless Communication Research Group and
the End-to-End Research Group. Thanks are given to the National
Natural Science Foundation of China (No.61271233, No.60972038), the
Ministry of Education (China) Ph.D. Programs Foundation
(No.20103223110001), the Research Culture Funds of Anhui Normal
University (No.2013xmpy10) and Jiangsu Province Postgraduate
Innovative Research Plan (No.CXZZ11_0396) for their financial
support.
5. IANA Considerations
There are no IANA actions required for this document.
6. Security Considerations
All drafts are required to have a security considerations section.
7. References
7.1. Normative References
[1] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<http://www.rfc-editor.org/info/rfc2119>.
7.2. Informative References
[2] Misun Ryu, Youngmin Kim, Hongshik Park, , "Systematic QoS
Class Mapping Framework over Multiple Heterogeneous
Networks", September 2008, <Proc. NEW2AN>.
[3] Wang Zhenhua, Sun Qiong, Huang Xiaohong, Ma Yan, , "IPv6
end-to-end QoS provision for heterogeneous networks using
flow label", 2010, <Proc. IEEE IC-BNMT>.
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[4] Lin Fu, Fei Pei, Zhang Dengyi, Li Wenhai, , "Quality of
service support for event detection in wireless sensor
networks", 2011, <Proc. WiCOM>.
[5] Ben Hamza Nejd, Rekhis Slim, Boudriga Noureddine, ,
"Cooperative architecture for QoS management in wireless
4G networks", 2011, <Proc. IEEE Symposium on Computers and
Informatics>.
[6] Klara Nahrstedt, Jonathan Smith, , "The QoS Broker", 1995,
<IEEE Multimedia>.
[7] Wanmin Wu, Md Arefin, Raoul Rivas, Klara Nahrstedt, ,
"Quality of Experience in Distributed Multimedia
Environments: Towards a Theoretical Framework", October
2009, <Proc. ACM Multimedia>.
[8] Kye-Hwan Lee, Son Tran Trong, Bong-Gyun Lee, , "QoS-
guaranteed IPTV service provisioning in IEEE 802.11e WLAN-
based home network", 2008, <Network Operations and
Management Symposium Workshops>.
[9] Asiya Khan, Lingfen Sun, Emmanuel Ifeachor, , "QoE
Prediction Model and its Application in Video Quality
Adaptation Over UMTS Networks", 2012, <IEEE Trans.
Multimedia>.
[10] Cibin R, Sudheer K P, Chaubey I, , "Sensitivity and
identifiability of stream flow generation parameters of
the SWAT model", 2010, <Hydrological Processes>.
Authors' Addresses
Yu-ning Dong (editor)
Nanjing Univ. of Posts and Telecom.
66 New Mo-fan-ma-lu Road
Nanjing, Gulou 210003
China
Phone: +86 15077858011
Email: dongyn@njupt.edu.cn
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Chun Liu (editor)
Nanjing Univ. of Posts and Telecom.
66 New Mo-fan-ma-lu Road
Nanjing, Gulou 210003
China
Phone: +86 18362930657
Email: 132189@163.com
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