Network Working Group P. Kim
Internet-Draft Korea Polytechnic University
Intended status: Experimental
Expires: April 16, 2015 October 17, 2014
Measuring Available Capacity for Mobile Networks
with Multiple Wireless Interfaces
draft-pskim-mif-capacity-nemo-00
Abstract
This draft proposes an estimation scheme of available capacity for
network mobility (NEMO) with the multi-interfaced mobile router
(MMR). In the proposed scheme, mobile nodes (MNs) can get
information on available capacity irrespective of the presence or
absence of estimation functionality. Since the MMR with
heterogeneous wireless network interfaces estimates available
capacity on behalf of the MNs inside the mobile network, the
proposed scheme does not require MNs to be involved in estimating
available capacity. A new algorithm for available capacity
estimation on MMR is developed to improve the estimation accuracy
compared with the existing scheme. The developed algorithm defines
three cases of the difference between the average output gap and the
input gap, and then reflects fully them, which can reduce the
detection error for the turning point and thus provide more accurate
estimation than the existing algorithm. Then, MNs can get
information on estimated available capacity from the MMR using L3
messages. Therefore, the proposed scheme can reduce burden and power
consumption of MNs with limited resource and battery power since MNs
do not estimates directly available capacity. In addition, the
proposed scheme can reduce considerably traffic overhead over
wireless links on multiple estimation paths since signaling messages
and injected testing traffic are reduced.
Status of this Memo
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on April 18, 2015.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Technical Background . . . . . . . . . . . . . . . . . . . . . 4
2.1 End-to-End Path for Mobile Network . . . . . . . . . . . . . . 4
2.2 Existing Available Capacity Estimation : IGI/PTR. . . . . . . 5
3. Available Capacity Estimation Scheme. . . . . . . . . . . . . 5
3.1 Component. . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2 Algorithm for Available Capacity Estimation . . . . . . . . . 6
3.3 Interaction between MMR and MNs. . . . . . . . . . . . . . . . 9
4. IANA Considerations. . . . . . . . . . . . . . . . . . . . . . 9
5. References . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . .10
1. Introduction
The available capacity of an end-to-end network path is its
remaining capacity, that is, the amount of traffic that can be sent
along the path without congesting it[1][2]. This available capacity
between two hosts is an important network parameter for improving
quality of service (QoS) in many distributed applications, such as
the overlay construction of peer to peer system, optimization of
resource utilization, optimization of dynamic server selection,
socket buffer sizing, admission control, and congestion control.
Therefore, recently, the area of end-to-end available capacity
estimation has attracted considerable interest. As a result, several
schemes for the available capacity estimation have been developed
based on active measurements[3]-[5]. In active measurements, the
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available capacity can be estimated by injecting probe traffic into
the network, and then analyzing the observed effects of cross
traffic on the probes. This kind of active measurement only requires
access to the sender and receiver hosts.
Meanwhile, to deal with the mobility support of mobile networks, the
Network Mobility (NEMO) techniques have been researched[6]. In the
NEMO, the mobile router (MR) is capable of changing its point of
attachment to the Internet without disrupting higher layer
connections of attached devices. Therefore, mobile nodes (MNs)
inside a mobile network are unaware of their network's mobility;
however, they are provided with uninterrupted Internet access even
when the network changes its attachment point to the Internet. This
draft considers the mobile network with a multi-interfaced mobile
router (MMR). In addition, to consider the heterogeneous wireless
network environment [7][8], the MMR can be assumed to have multiple
heterogeneous wireless network interfaces. Therefore, the MMR
establishes simultaneously multiple paths to the Internet through
external wireless interfaces such as wireless metropolitan area
network (WMAN) and wireless wide area network (WWAN) with high
mobility and wide coverage. However, due to capacity constraints of
multi-path through external wireless interfaces, the MMR might
require a capacity aggregation to get sufficient capacity for MNs'
demanding inside a mobile network. The capacity aggregation
requires generally several functions such as capacity estimation
and packet distribution, etc. Among them, this draft focuses on the
capacity estimation.
Generally, there can be often many MNs inside the mobile network
with NEMO in heterogeneous wireless network environment. The MMR
enables the multi-path communication outside the mobile network.
Thus, MNs inside the mobile network can select the most appropriate
communication path depending on the network environment and then
communicate with corresponding hosts, such as the IPTV server, media
streaming server, web server, FTP server, etc, via the MMR. If MNs
want to understand the condition of multiple communication paths,
they will estimates directly available capacity for each path.
Therefore, all MNs inside the mobile network are required to be
involved in estimating available capacity and thus have to
implement estimation functionality, which can be somewhat burdensome
and power consumptive for MNs with limited resource and battery
power. In addition, there can be the number of estimation signaling
messages and injected testing traffic as shown in active measurement
approaches[3], which can cause considerable traffic overhead over
wireless links on estimation paths.
Therefore, this draft proposes an available capacity estimation
mobile networks. In the proposed scheme, when MNs inside the mobile
network want to understand the condition of multiple communication
paths outside the mobile network, they can get available capacity
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irrespective of the presence or absence of estimation functionality.
That is, the proposed scheme does not require the MN to be involved
in estimating available capacity. Instead, the MMR estimates
available capacity on behalf of the MNs inside the mobile network.
The proposed available capacity estimation scheme requires an
estimation algorithm. Thus, in this draft, a new algorithm is
proposed based on the IGI/PTR scheme[3][4] to reduce the detection
error of the turning point and enhance the accuracy of the available
capacity estimation. The proposed algorithm reflects fully three
cases, while the existing IGI/PTR algorithm reflected only two cases.
Since three cases are handled respectively by appropriate
corresponding manners, the proposed algorithm can be expected to
reduce the detection error for the turning point. Therefore, the
end-to-end available capacity can be estimated more accurate than
existing algorithm.
2. Technical Background
2.1 End-to-End Path for Mobile Network
This draft considers the mobile network in heterogeneous wireless
networks. The MR is capable of changing its point of attachment to
the mobile network, moving from one link to another link. To
consider heterogeneous wireless networks, the MR is assumed to be
multi-homing and thus called the multi-interfaced mobile router
(MMR). The MMR has heterogeneous multiple network interfaces which
are categorized by internal and external wireless interfaces. With
the consideration of coverage and capacity, internal wireless
interfaces attached to MNs inside the mobile network would be WLAN
and external wireless interfaces attached to external base stations
would be WMAN and WWAN. Therefore, the MMR enables the multi-path
communication outside the mobile network through these heterogeneous
wireless interfaces. Meanwhile, MNs inside the mobile network are
assumed to have single wireless interface or heterogeneous multiple
wireless interfaces. Corresponding hosts (CHs) can be the IPTV
server, media streaming server, web server, FTP server, etc. MNs
inside the mobile network can communicate with CHs on multiple paths
via the MMR.
The end-to-end multi-path from MNs inside the mobile network to CHs
outside the mobile network via the MMR consists of following three
links. Inside the mobile network, there is a link between MN and
MMR. The WLAN will be generally adopted as an air technology due to
high transmission speed and moderate coverage. Thus, MNs with WLAN
interface can communicate via the MMR with internal WLAN interface
inside the mobile network. Outside the mobile network, there is a
link between MMR and external BSs. The WMAN and WWAN will be
generally adopted as an air technology due to wide coverage. Thus,
the MMR with external WMAN and WWAN interfaces can communicate via
corresponding base stations (BSs). However, in this wireless link,
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it is difficult to expect higher transmission speed than that of
the wireless link between MNs and MMR using WLAN. The link between
external BSs and CHs consists generally of routers with high
processing speed and wired networks with high transmission speed.
2.2 Existing Available Capacity Estimation : IGI/PTR
The IGI(Initial Gap Increasing)/PTR(Packet Transmission Rate)
algorithm [3][4] was proposed for the available capacity estimation
and shown to be much faster than existing algorithms with similar
estimation accuracy but with shorter estimation latency. This
algorithm is based on a single-hop gap model that captures the
relationship between the competing traffic and the probing packet
train. As a sequence of probing packet trains from the source travel
through the network, packets belonging to the competing traffic may
be inserted between them, thus increasing the gap at the
destination. As a result, the average output gap value at the
destination may be a function of the competing traffic rate, making
it possible to estimate the amount of competing traffic. That is,
the average output gap can be used to determine the competing
traffic capacity and hence the available capacity on the
end-to-end path assuming that the bottleneck link capacity along the
end-to-end path is known. At some point, the average output gap
equals the average input gap as gaps in a probing packet train
increase. This point is called the "turning point". At the turning
point, the input gap value for which the average output gap is equal
to the input gap is the right value to use for estimating the
available capacity. However, there are some issues in the existing
IGI/PTR algorithm. After performing the estimation, three cases are
defined according to the difference between the average output gap
and the average input gap. These three cases mean that the average
output gap at the destination is (a) larger than, (b) equal to, (c)
less than the average input gap at the source. These three cases
have respectively different relationship between the average rate of
the probing packet train and the available capacity. However, the
existing algorithm did not reflect fully these three cases in order
to reduce the estimation latency. That is, both (b) and (c) cases
are handled in the same way, which can introduce the detection error
for the turning point since (b) and (c) cases are absolutely
different. Therefore, the available capacity can be estimated
inaccurately although the estimation latency can be reduced.
3. Available Capacity Estimation Scheme
If MNs inside the mobile network measure directly IP performance
metrics, they are required to be involved in the measurement
procedure and thus have to implement measurement functionality,
which can be somewhat burdensome and power consumptive for MNs with
limited resource and battery power. In addition, there can be the
number of measurement signaling messages and injected testing
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traffic, which can cause considerable traffic overhead over the
wireless links, such as link between MN and MMR, and link between
MMR and external BS, on measurement paths. In addition, as
mentioned previous section, the wireless link between MMR and
external BS is likely to be overloaded network link, that is,
"bottleneck link". Moreover, if there are many mobile networks
connected to external BS, this link is likely to be "tight link".
This means that IP performance metrics of the end-to-end
multi-path might be mostly influenced by the wireless link between
MMR and external BS.
With the consideration of these problems, a measurement scheme of IP
performance metrics is proposed for the mobile network in
heterogeneous wireless networks. In the proposed scheme, when MNs
inside the mobile network want to understand the condition of
multiple communication paths outside the mobile network, they can
get IP performance metrics irrespective of the presence or absence
of measurement functionality. Since the MMR with heterogeneous
wireless interfaces measures IP performance metrics on behalf of the
MNs inside the mobile network, the proposed scheme does not require
MNs to be involved in measuring IP performance metrics.
3.1 Component
Main components on the end-to-end measurement path consist of MNs,
MMR, and measurement server. MNs inside the mobile network are
assumed to have a single wireless interface or heterogeneous
multiple wireless interfaces. When MNs want to get IP performance
metrics to understand the condition of multiple communication paths,
they can request to the MMR using the L3 message. Also, MNs can get
IP performance metrics that the MMR provides periodically. The MMR
measures IP performance metrics on behalf of the MNs inside the
mobile network. Since the MMR have heterogeneous external wireless
interfaces such as WMAN and WWAN, the MMR enables the multi-path
communication outside the mobile network and thus can measure IP
performance metrics for all paths through these heterogeneous
external wireless interfaces. The measurement server is a host that
receives testing traffic, calculates performance statistics, and
response results of IP performance metrics to the MMR.
3.2 Algorithm for Available Capacity Estimation
A new scheme for available capacity estimation is
proposed to improve the estimation accuracy compared with the
existing scheme. As mentioned before, since (b) and (c) cases
handled in the same way are absolutely different, they should be
handled by respectively.
In this section, a new algorithm for available capacity estimation
is developed to improve the estimation accuracy compared with the
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existing algorithm. As mentioned in Section 2, since cases (b) and
(c) handled in the same way are absolutely different, they should be
handled by respectively. Following parameters are defined:
- A_bw : Available capacity.
- B_bw : Bottleneck link capacity.
- C_bw : Competing traffic capacity.
- Gap_out : Average output gap.
- Gap_in : Average Input gap.
- Delta : Equality boundary.
- R_pkt: Average rate of the packet train.
The end-to-end available capacity is defined as the difference
between the bottleneck link capacity along an end-to-end path and
the competing traffic. The bottleneck link capacity in the path
determines the end-to-end capacity which is the maximum IP layer
rate that the path can transfer from source to destination. In
other words, the capacity of a path establishes an upper bound on
the IP layer throughput that a user can expect to get from that
path. There are diverse measurement schemes for the bottleneck
link capacity. Therefore, the bottleneck link capacity can measured
from one of existing schemes.
There are several important probing parameters such as probing
packet size, number of probing packet in packet train, and input
gap to get correct measurement. Among them, input gap in a probing
packet train is the most important parameter to control for
accurate available capacity estimation. The source sends a
sequence of probing packet trains with adjusting input gap. The
difference between the average output gap and the input gap is
observed for each train. Then, the turning point is detected for
estimating the available capacity.
After performing a measurement, three cases are defined according to
the difference between the average output gap Gap_out$ and the input
gap Gap_in. Three cases are called 'Larger Than (LT)', 'Equal To
(EQ)', 'Smaller Than (ST)' cases which have respectively different
relationship between the average rate of the probing packet train
and the available capacity. These three cases are handled
respectively. As shown in three cases, the proposed algorithm
handles 'EQ' and 'ST' cases respectively while the existing
algorithm handles them in the same way.
Cases Condition Meanging
----------------------------------------------------
LT Gap_out > Gap_in + Delta/2 R_pkt > A_bw
EQ |Gap_out - Gap_in| < Delta R_pkt = A_bw
ST Gap_out < Gap_in + Delta/2 R_pkt < A_bw
----------------------------------------------------
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(A) 'LT' Operation
The estimation is repeated with the increased input gap. After then,
three cases observed once again. For each case, the estimation is
repeated with adjusting input gap as follows:
- LT : increased input gap
- EQ : same input gap as previous estimation
- ST : decreased input gap
In the existing algorithm, the estimation is repeated with the same
input gap as previous estimation for 'ST' case.
(B) 'EQ' Operation
The estimation is repeated with the same input gap as previous
estimation. After then, three cases are observed once again and then
handled respectively as follows:
- LT : estimation with increased input gap
- EQ : estimation finished (turning point detected)
- ST : estimation with decreased input gap
In the existing algorithm, the estimation is finished for 'ST' case.
(C) 'ST' Operation
The estimation is repeated with the decreased input gap. In the
existing algorithm, the estimation is repeated with the same input
gap in this case. After then, three cases are observed once again
and then handled respectively as follows:
- LT : estimation with increased input gap
- EQ : estimation finished (turning point detected)
- ST : estimation with decreased input gap
In the existing algorithm, the estimation is finished for 'ST' case.
When the turning point is detected, the measurement is finished and
then the end-to-end available capacity can be estimated as follows.
The end-to-end available capacity is obtained by subtracting the
competing traffic capacity from the bottleneck link capacity as
follows:
A_bw = B_bw - C_bw
As mentioned before, the bottleneck link capacity can be measured
from one of existing schemes. Then, the competing traffic
capacity can be computed using the average output gap and the input
gap at the turning point, and the bottleneck link capacity.
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3.3 Interaction between MMR and MNs
When MNs want to get IP performance metrics from the MMR to
understand the condition of multiple communication paths, following
two methods can be available:
- Unsolicited Reqeust and Response : Irrespective of the request of
MNs, the MMR broadcasts periodically measured IP performances
metrics to MNs inside the mobile network.
- Solicited Request and Response : A specific MN requests and then
the MMR unicasts measured IP performance metrics to the
corresponding MN.
Request and Response messages can be defined by the Internet Control
Message Protocol (ICMP) message format in [10]. For example, for
unsolicited request and response, the unsolicited router
advertisement (RA) message format in [10] can be reused by the
modification of type field. For solicited request and response,
route solicitation (RS) and router advertisement (RA) message
formats in [10] can be reused by the modification of type field.
Using obtained IP performance metrics, MNs can understand the
condition of multiple communication paths for heterogeneous multiple
wireless interfaces. Then, MNs may want to select the most
appropriate path per communication type. If the condition of all
communication paths is unfavorable, MNs with heterogeneous multiple
wireless interfaces can connect to the corresponding BS directly,
not via the MMR.
4. IANA Considerations
This document has no IANA actions.
5. References
[1] V. Paxson, G. Alimes, J. Mahdavi and M. Mathis, "Framework for
IP Performance Metrics," IETF RFC 2330, May 1998.
[2] P. Chimento, J. Ishac, "Defining Network Capacity," IETF RFC
5136, Feb 2008.
[3] N. Hu and P. Steenkiste, "Evaluation and characterization of
available bandwidth probing techniques," IEEE JSAC Special Issue
in Internet and WWW Measurement, Mapping, and Modeling, vol. 21,
no. 6, pp. 879~894, 2003.
[4] R. Prasad, C. Dovrolis, M. Murray, and K. Claffy, "Bandwidth
estimation: metrics, measurement techniques, and tools," IEEE
Network, vol. 17, pp. 27~35, 2003.
[5] E. Bergfeldt, S. Ekelin, and J. M. Karlsson, "Real-time
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available-bandwidth estimation using filtering and change
detection," Computer Networks, vol. 53, no. 15, pp. 2617~2645,
2009.
[6] Thubert, P., A. Petrescu, R. Wakikawa and V. Devarapalli,
"Network Mobility (NEMO) Basic Support Protocol," RFC 3963, Jan
2005.
[7] M. Blanchet, P. Seite, "Multiple Interfaces and Provisioning
Domains Problem Statement," IETF RFC 6418, November 2011.
[8] M. Wasserman, P. Seite, "Current Practices for
Multiple-Interface Hosts," IETF RFC 6419, November 2011.
[9] A. Conta, S. Deering, M. Gupta, "Internet Control Message
Protocol(ICMPv6) for the Internet Protocol Version 6 (IPv6)
Specification," IETF RFC 4443, March 2006.
[10] T. Narten, E. Nordmark, W. Simpson, "Neighbor Discovery for IP
Version 6 (IPv6)," IETF RFC 2461, December 1998.
[11] L. Suciu, J-M. Bonnin, K. Guillouard, and T. Ernst, "Multiple
network interfaces management for mobile routers," in Proc. of
5th International Conference on ITS Telecommunications (ITST),
2005, pp. 347~351.
[12] X. Chen, H. Zhou, Y. Qin, and H. Zhang, "Multi-interfaced mobile
router scheme and enhanced path selection algorithm," in Proc.
of the International Conference on Telecommunications (ICT),
2008, pp. 1~8.
Author's Address
Pyungsoo Kim
Department of Electronics Engineering,
Korea Polytechnic University,
2121 Jungwang-Dong, Shiheung City,
Gyeonggi-Do 429-793,
KOREA
Phone: +82 31 8041 0489
EMail: pskim@kpu.ac.kr
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