Internet DRAFT - draft-yang-rtgwg-oma-impl-report
draft-yang-rtgwg-oma-impl-report
Network Working Group S. Yang
Internet-Draft H. Yu
Intended status: Informational UESTC
Expires: May 28, 2016 X. Zhang
N. Wu
Huawei
November 25, 2015
Ordered Metric Adjustment Implementation Report
draft-yang-rtgwg-oma-impl-report-00
Abstract
Ordered Metric Adjustment (OMA) as specified in
[I-D.zxd-rtgwg-ordered-metric-adjustment] has provided a mechanism
whereby global transient forwarding loops can be avoided through
graceful link metric adjustment. This document provides an
implementation report for OMA algorithm and mechanism on different
network topologies. What's more, it gives an analysis on efficiency
and performance of OMA, comparing with other well-known loop-
preventing algorithm.
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 [RFC2119].
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at http://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on May 28, 2016.
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Copyright Notice
Copyright (c) 2015 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(http://trustee.ietf.org/license-info) in effect on the date of
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the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Overview of Algorithm . . . . . . . . . . . . . . . . . . . . 3
3.1. Calculating the adjustment range of link metric for each
node . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.2. Merge different Seq(Yj) into a global sequence . . . . . 5
4. Innovation point of algorithm . . . . . . . . . . . . . . . . 5
5. Simulation of Algorithm . . . . . . . . . . . . . . . . . . . 6
5.1. Environment Setup . . . . . . . . . . . . . . . . . . . . 6
5.2. Measurement and Methods . . . . . . . . . . . . . . . . . 7
5.3. Simulation results . . . . . . . . . . . . . . . . . . . 8
5.3.1. Micro-loop risk . . . . . . . . . . . . . . . . . . . 8
5.3.2. Sequence length and computing time . . . . . . . . . 8
6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 10
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
8. Security Considerations . . . . . . . . . . . . . . . . . . . 11
9. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 11
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 11
10.1. Normative References . . . . . . . . . . . . . . . . . . 11
10.2. Informative References . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
1. Introduction
This document provides an implementation report for OMA algorithm and
mechanism on different network topologies. What's more, it gives an
analysis on efficiency and performance of OMA, comparing with other
well-known loop-preventing algorithm.
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2. Terminology
o Reverse Shortest Path First Tree (RSPF Tree): A directed acyclic
graph containing all the shortest path from the node of the
network toward the root.
o Link L: An edge in the network connecting node a to b will shut
down and its metric value is K when link L is up.
o X set: A node set contains the node impacted by the removal of a
link L such that each node in X set must change its forwarding
paths to avoid link L.
o Y set: A node set contains all destination nodes in that may be
concerned by the down event of the link L.
o D(Yj,Xi): The shortest distacnce from node Xi to Yj when link L is
up.
o D'(Yj,Xi): The shortest distacnce from node Xi to Yj when link L
is down.
o COST(Yj,Xi,max): If node Xi switches its optimal path to Yj, the
reasonable upper metric of link L can be adjusted is
COST(Yj,Xi,max),COST(Yj,Xi,max) = MIN(COST(Yj,Xk,min)),where Xk is
the father of node Xi in case of link L being up.
o COST(Yj,Xi,min): If node Xi switches its optimal path to Yj, the
reasonable lower metric of link L can be adjusted is
COST(Yj,Xi,min), COST(Yj,Xi,min) = D'(Yj,Xi)- D(Yj,Xi) + K.
o R = (R_min,R_max): Here R_min equals COST(Yj,Xi,min) and R_max =
COST(Yj,Xi,min). When the link L!_s metric is set in the range of
(R_min, R_max), node Xi can be switched to its optimal path to Yj
without forwarding loop.
o Seq(Yj): A metric range sequence for Yj contains all metric ranges
of source nodes Xi affected by the removal of a link L.When link
L' s metric set to a value in each metric range of sequence
Seq(Yj) in order, each node Xi will to its optimal path to Yj
without forwarding loop.
3. Overview of Algorithm
The Internet is the most popular network, which is a distributed
system. Depend on its configuration, each network device
communicates with its neighbor, calculate routes and generate the FIB
individually, finally, the packet will be forwarded hop by hop. But
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due to the difference of each device hardware capabilities and
internal/external environments, the route calculation cannot be
scheduled at same time, then micro-loop occur.
In draft [I-D.zxd-rtgwg-ordered-metric-adjustment] we introduces a
method to prevent forwarding loop by adjusting link metric gradually
for several times. In this section we will give an overview of
algorithm and here we just talk about link down event and results
computed in down event are also appropriate to link up event. We
suppose link L connecting node A to B referenced in the following
sections will shut down.
First of all, we have some definitions. Set X consist of Xi which on
sub-tree below A in RSPF tree with root B such that each node in X
must change its forwarding paths to Yj to avoid link L. And the
notation Y that gives the set of "affected destinations". This set
contains all destination nodes that may be concerned by the change of
the link L and set Y consist of Yj which on sub- tree below B in RSPF
tree with root A. In Figure 1, we have X = {A, C, F, G, H} and Y=
{B, D, E}
10
/A----------B\
/ \
10/ \10
/ \
/ \
C D
/\ /\
/ \ / \
/ \10 80/ \50
10/ \ / \
/ \ 10 / 50 \
G \F-----------H----------E
Figure 1 Topology
3.1. Calculating the adjustment range of link metric for each node
This part is used to calculate a reasonable adjustment metric range
for each node Xi to switch its optimal path to a destination Yj
without forwarding loop. Each metric range of Xi related to Yj make
up of a metric range sequence for Yj.
For destination Yj, we calculate the RSPF tree with root Yj when link
L is up and down to determine the values of two variables R_min =
COST(Yj,Xi,min) and R_max = COST(Yj,Xi,max). When the link L's
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metric is set in the range of (R_min,R_max), node Xi can switch to
its optimal path to the root node Yj without forwarding loop.
Then metric ranges of Xi computed above make up of a metric range
sequence for Yj recorded as Seq(Yj). Then the metric of link L can
be set to a value in each range of sequence in order .For example, in
figure 1, we have seq(B) ={[100,120],[80,100],[60,80]} and if we set
the metric of link L to 60, 81 and 101, each node Xi will switch to
its optimal path to the B without forwarding loop.
3.2. Merge different Seq(Yj) into a global sequence
According to section 1.1, we have different metric range sequence
corresponding to each destination Yj. However, nodes need to react
to the change of a link weight for all affected destinations. This
part will show how to merge different sequences of each destination
into a minimal sequence effectively, then multiple nodes in set X
simultaneously switch optimal path to each root node without
forwarding loop.
Compare the metric range of each Seq(Yj) and choose range R =
(R_min,R_max) with largest lower bound in these metric ranges. Then
remove the metric range with upper bound larger than the lower bound
R_min of R in all sequence. Repeat operation above when the sequence
of all destination is empty, then algorithm terminates.
When the algorithm finishes, remaining metric ranges
(R_1min,R_1max),(R_2min,R_2max),... (R_nmin,R_nmax) are what we want.
In case of link L going to up, the adjustment process of metric of
link L can be set to R_1min+1,R_2min+1,... ,R_nmin, and configuration
value K. In case of link L going to down, the adjustment process of
metric of link L can be set to configuration value K,
R_nmin+1,R_((n-1)min)+1,... , and R_1min.
In figure 1, In case of link L going to down, the adjustment process
of metric of link L can be set to {10,41,61,81,101}, in proper order,
then there is no forwarding loop during this adjustment.
4. Innovation point of algorithm
There are several different algorithms solving forwarding loops by
adjusting link metric gradually. Compared with other solution, we
put forward two innovation points in each part of our algorithm.
First innovation point of our algorithm is to check whether
destination Yj is affected actually due to the down link. In part 1
of algorithm, if the size of set Y is |Y|, we need to compute 2|Y|
times RSPF tree and its complexity is in |N|log|N|+|E| per times in
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the best case. We just compute an RSPF tree when link L is up
firstly. According to the Dynamic Dijsktra Algorithm, we know that
nodes only on sub- tree below A in RSPF tree with root Yj will change
its optimal paths to destination if link L change. Obviously, if it
is empty on sub- tree below A in RSPF tree with root Yj, only node A
will change its optimal path to root so that no forwarding loop for
destination Yj can occur when link L down. We call it "Unaffected
destination" and in this case we can delete destination Yj from the
set Y. If there are |m| destinations are unaffected, we just need |
2|Y|-|m|| times computation.
The second innovation of our algorithm is that using metric range
express the reasonable adjustment metric range for each node to
change their optimal path to the root node and using a common range
instead of several overlapping ranges to reduce the operation. In
part 2 of algorithm, we merge different range sequences into a global
sequence, however, many ranges overlap with each other so if merge
overlapping ranges into a common range, adjusting link metric several
times can be instead of one time. Like this we can reduce a large
number of metric ranges in a reasonably short time and the final
global sequence will include a few ranges.
Through these two points, we have a minimal metric sequence length
with the fastest computing time among the similar algorithms.
5. Simulation of Algorithm
This section aims at evaluating the performance of our algorithm from
two aspects: sequence length and the time required to compute them on
real IP networks. Firstly, we present the topologies we used in our
algorithms. Then present the results of our simulation, analyzing
the reasonable length, as well as the efficiency of our optimizations
on computing times.
5.1. Environment Setup
Table I summarizes the main characteristics of our simulation
topologies. |N| and |E| respectively represent the number of nodes
and links in the graph. d_m is the maximum degree, and ω gives
the weight distribution. Networks 1-6 of Table I are Rocketfuel
inferred topologies obtained with traceroute campaigns
[inferring-link-weight-e2e]. Networks 7-10 are generated obey power-
law distribution. Networks 9 and 10 are topologies with special
shape. Network 9 is a star topology and network 10 is a circle
consists of several small circles. Networks 4 and 5 come with
inferred IGP weights and other topologies come with random weights in
[1,100].
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+----+---------+-----+-----+----+------------+
| ID | Network | |N| | |E| | dm | w |
+----+---------+-----+-----+----+------------+
| 1 | Abovenet| 17 | 74 | 8 | [1,100] |
+----+---------+-----+-----+----+------------+
| 2 | Exodus | 21 | 72 | 7 | [1,100] |
+----+---------+-----+-----+----+------------+
| 3 | Tiscali | 27 | 128 | 18 | [1,100] |
+----+---------+-----+-----+----+------------+
| 4 | Sprint | 30 | 138 | 11 | [1,100] |
+----+---------+-----+-----+----+------------+
| 5 | Geant | 23 | 74 | 6 | [156,9953] |
+----+---------+-----+-----+----+------------+
| 6 | AT&T | 154 | 376 | 29 | [1,222] |
+----+---------+-----+-----+----+------------+
| 7 | Pow-1 | 50 | 200 | 7 | [1,100] |
+----+---------+-----+-----+----+------------+
| 8 | Pow-2 | 100 | 400 | 7 | [1,100] |
+----+---------+-----+-----+----+------------+
| 9 | Pow-3 | 200 | 800 | 7 | [1,100] |
+----+---------+-----+-----+----+------------+
| 10 | Pow-4 | 500 | 2000| 7 | [1,100] |
+----+---------+-----+-----+----+------------+
| 11 | star | 13 | 44 | 6 | [5,101] |
+----+---------+-----+-----+----+------------+
| 12 | circle | 41 | 90 | 3 | [8,99] |
+----+---------+-----+-----+----+------------+
Table 1 Main graph properties of simulation network
5.2. Measurement and Methods
In the results of simulation we focus on the length of metric
sequence and computing time. The shortest sequence length, the less
operation will we do. Thus we expect to get a minimal sequence and
computing time is as short as possible.
Firstly we have a loop-risk test on all simulation networks. We
carry on 1000 times tests on each network making a link fail
randomly, recording the total number that we need to use our
algorithm to reconfigure fail link. This test will show that in most
topologies the risk of loop will occur frequently when a link fail.
So it is very important to search an efficient algorithm to prevent
micro-loop.
Then we carry on |E| times tests in each network mentioned above
ensuring that every link in topology will fail once, recording the
sequence length and computing time each time and analysis it.
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5.3. Simulation results
5.3.1. Micro-loop risk
Table 2 shows that possibility of micro-loop occurrence is more 50%
in most topology. So it is very important to search an efficient
algorithm to prevent forwarding loop.
+-----------+-------+-------+-------+-------+-------+-------+
| Topo-ID | 1 | 2 | 3 | 4 | 5 | 6 |
+-----------+-------+-------+-------+-------+-------+-------+
| Loop risk | 52.1% | 66.8% | 66.8% | 64.1% | 73.0% | 29.1% |
+-----------+-------+-------+-------+-------+-------+-------+
+-----------+-------+-------+-------+-------+-------+-------+
| Topo-ID | 7 | 8 | 9 | 10 | 11 | 12 |
+-----------+-------+-------+-------+-------+-------+-------+
| Loop risk | 77.6% | 89.1% | 91.6% | 96.1% | 56.3% | 100.0%|
+-----------+-------+-------+-------+-------+-------+-------+
Table 2 Micro-loop risk percentage
5.3.2. Sequence length and computing time
Tables 2 and 3 respectively give an overview of metric sequence
length and computing time observed on our set of simulation networks.
Minimal Metric Sequence Length: The length of the sequence we
obtained in most cases are relatively short. The actual network
topology of 1-4,6 and 11, for more than half of the cases, no
intermediate metric is needed, as the size of the sequence is equal
to 0 and more than 90% of the cases, less than two intermediate
metrics are needed to ensure a loop-free convergence in case of a
link removal. For all networks, in most case approximately less than
ten intermediate metrics can solve micro-loop during the convergence.
For all networks, there are several factors which influence the
length of the metric sequence: 1) network size: Apparently when the
size becomes larger, the number of source and destination nodes
significantly increased, so more intermediate state are needed. In
our simulation, network 10 is the biggest which has 500 nodes and
2000 links and we can notice that it need more intermediate state
than others. 2) network shape: a ring network is easy to form a deep
, so there can be many nodes on the ring forward its packets along
the same direction to destination. When an upstream link is closed,
if all these nodes go backward to avoid a failed link, it will
produce a number of cycles, as the length of the sequence is longer
than others certainly. Results of network 12 prove it. 3) weight
distribution: When a network with a large weight range, there may be
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less overlap range. So the common range remained will be more thus
we get a longer sequence such as network 5.
Although in a few cases the length of metric sequence is a little
long, but most of our sequence have a very reasonable size.
Computing Time: We will show that compared with others, our algorithm
has the shortest computing time. Here we focus on a similar
algorithm [lightweight-algorithm-minimal-operation-impact] (We call
it to "Graceful" for short in the following content) and give a
contrast between them. Table III gives computing time percentiles
including worst-cases results.
According to the statistical, for all networks except network 10, the
maximum computation time for calculating a sequence is below a few
hundred milliseconds. Compared with the Graceful algorithm, there is
no significant difference when network size is small. However, with
network size increasing our algorithm performs better significantly
which can be completed in a shorter time. That is because two
innovative points in our algorithm. It can reduce the computation
time effectively.
+-----+-------+------+-------+-------+-------+-------+-------+-----+
| | Length distribution (%) | MAX |
| NID +------------------------------------------------------+ |
| ||MMS|=0| <=1 | <=2 | <=5 | <=8 | <=10 | <=15 ||MMS||
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|1 |69.64% |89.29%|98.21% |100.00%|100.00%|100.00%|100.00%| 3 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|2 |50.00% |80.65%|90.32% |100.00%|100.00%|100.00%|100.00%| 5 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|3 |55.41% |83.78%|91.89% |100.00%|100.00%|100.00%|100.00%| 4 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|4 |54.72% |83.02%|96.23% |100.00%|100.00%|100.00%|100.00%| 4 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|5 |21.88% |29.69%|59.38% |82.81% |93.75% |87.50% |100.00%| 15 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|6 |77.69% |96.15%|98.46% |100.00%|100.00%|100.00%|100.00%| 4 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|7 |32.76% |60.92%|79.31% |94.25% |97.70% |95.98% |100.00%| 14 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|8 |25.26% |49.21%|63.16% |84.74% |91.58% |87.63% |98.68% | 21 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|9 |11.15% |23.23%|38.32% |68.50% |84.65% |75.72% |98.56% | 34 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|10 |4.38% |10.56%|18.28% |44.85% |65.35% |73.79% |89.39% | 87 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
|11 |53.33% |93.33%|100.00%|100.00%|100.00%|100.00%|100.00%| 2 |
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+-----+-------+------+-------+-------+-------+-------+-------+-----+
|12 |0.00% |0.00% |1.11% |57.78% |84.44% |62.22% |94.44% | 22 |
+-----+-------+------+-------+-------+-------+-------+-------+-----+
Table 3 Length Distribution
+-----+---------------------------------------------------------+
| | Computing Time Distribution (ms) |
| +------------+-------------+-------------+----------------+
| NID | 50th | 80th | 100th | Mean |
| +------------+----+--------+----+--------+-------+--------+
| |OMA|Graceful|OMA |Graceful|OMA |Graceful|OMA |Graceful|
+-----+---+--------+----+--------+----+--------+-------+--------+
|1 |1 |1 |1 |1 |2 |5 |0.64 |0.96 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|2 |1 |1 |4 |3 |4 |8 |1.56 |1.82 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|3 |1 |1 |1 |4 |7 |8 |1.22 |2.23 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|4 |1 |1 |3 |4 |7 |10 |1.79 |2.33 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|5 |2 |3 |3 |6 |20 |15 |2.44 |3.41 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|6 |6 |13 |49 |175 |189 |294 |35.68 |69.11 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|7 |3 |4 |8 |18 |24 |52 |4.76 |9.32 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|8 |12 |21 |37 |86 |113 |351 |20.95 |49.50 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|9 |71 |172 |192 |650 |589 |2541 |111.55 |346.83 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|10 |907|2467 |2656|6886 |7365|24206 |1445.28|3689.79 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|11 |1 |0 |1 |1 |2 |2 |0.80 |0.47 |
+-----+---+--------+----+--------+----+--------+-------+--------+
|12 |6 |18 |9 |31 |14 |131 |6.17 |26.27 |
+-----+---+--------+----+--------+----+--------+-------+--------+
Table 4 Computing Time Distribution
6. Conclusion
In document [I-D.zxd-rtgwg-ordered-metric-adjustment] we introduces a
method to prevent forwarding loop by adjusting link metric gradually
for several times. In this document we mainly give the simulation
data. We have a simple review firstly then focus on the innovation
in our algorithm. Then give the simulation results such that our
algorithm have the best performance among the current mainstream
algorithms to prevent forwarding loop.
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7. IANA Considerations
This document includes no request to IANA.
8. Security Considerations
This document is not currently believed to introduce new security
concerns.
9. Acknowledgments
TBD.
10. References
10.1. Normative References
[I-D.zxd-rtgwg-ordered-metric-adjustment]
Zhang, X. and G. Yan, "Algorithm for Ordered Metric
Adjustment", draft-zxd-rtgwg-ordered-metric-adjustment-00
(work in progress), October 2013.
[inferring-link-weight-e2e]
Mahajan, R., Spring, N., Wetherall, D., and T. Anderson,
"Inferring link weights using end-to-end measurements",
November 2002.
[lightweight-algorithm-minimal-operation-impact]
Clad, F., Merindol, P., Pansiot, J., Francois, P., and O.
Bonaventure, "Graceful Convergence in Link-State IP
Networks: A Lightweight Algorithm Ensuring Minimal
Operational Impact, IEEE 1063-6692", February 2014.
10.2. Informative References
[RFC1195] Callon, R., "Use of OSI IS-IS for routing in TCP/IP and
dual environments", RFC 1195, DOI 10.17487/RFC1195,
December 1990, <http://www.rfc-editor.org/info/rfc1195>.
[RFC2119] 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>.
[RFC2328] Moy, J., "OSPF Version 2", STD 54, RFC 2328,
DOI 10.17487/RFC2328, April 1998,
<http://www.rfc-editor.org/info/rfc2328>.
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[RFC5715] Shand, M. and S. Bryant, "A Framework for Loop-Free
Convergence", RFC 5715, DOI 10.17487/RFC5715, January
2010, <http://www.rfc-editor.org/info/rfc5715>.
[RFC6976] Shand, M., Bryant, S., Previdi, S., Filsfils, C.,
Francois, P., and O. Bonaventure, "Framework for Loop-Free
Convergence Using the Ordered Forwarding Information Base
(oFIB) Approach", RFC 6976, DOI 10.17487/RFC6976, July
2013, <http://www.rfc-editor.org/info/rfc6976>.
Authors' Addresses
Shiqi Yang
UESTC
No.2006, Xi Yuan Ave., Westn High-Tech Zone
Chengdu 611731
China
Email: yangsq90309@163.com
Hongfang Yu
UESTC
No.2006, Xi Yuan Ave., Westn High-Tech Zone
Chengdu 611731
China
Email: yuhf.uestc@139.com
Xudong Zhang
Huawei
Huawei Bld., No.156 Beiqing Rd.
Beijing 100095
China
Email: zhangxudong@huawei.com
Nan Wu
Huawei
Huawei Bld., No.156 Beiqing Rd.
Beijing 100095
China
Email: eric.wu@huawei.com
Yang, et al. Expires May 28, 2016 [Page 12]
