Independent Submission J. Tripathi, Ed.
Request for Comments: 6687 J. de Oliveira, Ed.
Category: Informational Drexel University
ISSN: 2070-1721 JP. Vasseur, Ed.
Cisco Systems, Inc.
October 2012
Performance Evaluation
of the Routing Protocol for Low-Power and Lossy Networks (RPL)
Abstract
This document presents a performance evaluation of the Routing
Protocol for Low-Power and Lossy Networks (RPL) for a small outdoor
deployment of sensor nodes and for a large-scale smart meter network.
Detailed simulations are carried out to produce several routing
performance metrics using these real-life deployment scenarios.
Please refer to the PDF version of this document, which includes
several plots for the performance metrics not shown in the plain-text
version.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This is a contribution to the RFC Series, independently of any other
RFC stream. The RFC Editor has chosen to publish this document at
its discretion and makes no statement about its value for
implementation or deployment. Documents approved for publication by
the RFC Editor are not a candidate for any level of Internet
Standard; see Section 2 of RFC 5741.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
http://www.rfc-editor.org/info/rfc6687.
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Copyright Notice
Copyright (c) 2012 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
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to this document.
Table of Contents
1. Introduction ....................................................2
2. Terminology .....................................................3
3. Methodology and Simulation Setup ................................4
4. Performance Metrics .............................................7
4.1. Common Assumptions .........................................7
4.2. Path Quality ...............................................7
4.3. Routing Table Size ........................................10
4.4. Delay Bound for P2P Routing ...............................10
4.5. Control Packet Overhead ...................................11
4.6. Loss of Connectivity ......................................13
5. RPL in a Building Automation Routing Scenario ..................18
5.1. Path Quality ..............................................18
5.2. Delay .....................................................19
6. RPL in a Large-Scale Network ...................................19
6.1. Path Quality ..............................................19
6.2. Delay .....................................................21
6.3. Control Packet Overhead ...................................21
7. Scaling Property and Routing Stability .........................22
8. Comments .......................................................24
9. Security Considerations ........................................25
10. Acknowledgements ..............................................25
11. Informative References ........................................25
1. Introduction
Designing a routing protocol for Low-Power and Lossy Networks (LLNs)
imposes great challenges, mainly due to low data rates, high
probability of packet delivery failure, and strict energy constraints
in the nodes. The IETF ROLL Working Group took on this task and
specified the Routing Protocol for Low-Power and Lossy Networks (RPL)
in [RFC6550].
RPL is designed to meet the core requirements specified in [RFC5826],
[RFC5867], [RFC5673], and [RFC5548].
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This document's contribution is to provide a performance evaluation
of RPL with respect to several metrics of interest. This is
accomplished using real data and topologies in a discrete event
simulator developed to reproduce the protocol behavior.
The following metrics are evaluated:
o Path quality metrics, such as ETX path cost, ETX path stretch, ETX
fractional stretch, and hop distance stretch, as defined in
Section 2 ("Terminology");
o Control plane overhead;
o End-to-end delay between nodes;
o Ability to cope with unstable situations (link churns, node
dying);
o Required resource constraints on nodes (routing table size).
Some of these metrics are mentioned in the aforementioned RFCs,
whereas others have been introduced to consider the challenges and
unique requirements of LLNs as discussed in [RFC6550]. For example,
routing in a home automation deployment has strict time bounds on
protocol convergence after any change in topology, as mentioned in
Section 3.4 of [RFC5826]. [RFC5673] requires bounded and guaranteed
end-to-end delay for routing in an industrial deployment, and
[RFC5548] requires comparatively loose bounds on latency for end-to-
end communication. [RFC5548] mandates scalability in terms of
protocol performance for a network of size ranging from 10^2 to 10^4
nodes.
Although simulation cannot prove formally that a protocol operates
properly in all situations, it can give a good level of confidence in
protocol behavior in highly stressful conditions, if and only if
real-life data are used. Simulation is particularly useful when
theoretical model assumptions may not be applicable to such networks
and scenarios. In this document, real deployed network data traces
have been used to model link behaviors and network topologies.
2. Terminology
Please refer to [ROLL-TERMS] and [RFC6550] for terminology. In
addition, the following terms are specified:
PDR: Packet Delivery Ratio.
CDF: Cumulative Distribution Function.
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Expected Transmission Count (ETX Metric): The expected number of
transmissions to reach the next hop is determined as the inverse
of the link PDR. Consequently, in every hop, if the link quality
(PDR) is high, the expected number of transmissions to reach the
next hop may be as low as 1. However, if the PDR for the
particular link is low, multiple transmissions may be needed.
ETX Path Cost: The ETX path cost metric is determined as the
summation of the ETX value for each link on the route a packet
takes towards the destination.
ETX Path Cost Stretch: The ETX path cost stretch is defined as the
difference between the number of expected transmissions (ETX
Metric) taken by a packet traveling from source to destination,
following a route determined by RPL and a route determined by a
hypothetical ideal shortest path routing protocol (using link ETX
as the metric).
ETX Fractional Stretch (fractional stretch factor of link ETX metric
against ideal shortest path): The fractional path stretch is the
ratio of ETX path stretch to ETX path cost for the shortest path
route for the source-destination pair.
Hop Distance Stretch (stretch factor for node hop distance against
ideal shortest path): The hop distance stretch is defined as the
difference between the number of hops taken by a packet traveling
from source to destination, following a route determined by RPL
and by a hypothetical ideal shortest path algorithm, both using
ETX as the link cost. The fractional hop distance stretch is
computed as the ratio of path stretch to count value between a
source-destination pair for the hypothetical shortest path route
optimizing ETX path cost.
3. Methodology and Simulation Setup
In the context of this document, RPL has been simulated using OMNeT++
[OMNeTpp], a well-known discrete event-based simulator written in C++
and NEtwork Description (NED). Castalia-2.2 [Castalia-2.2] has been
used as a Wireless Sensor Network Simulator framework within OMNeT++.
The output and events in the simulation are visualized with the help
of the Network AniMator, or NAM, which is distributed with the NS
(Network Simulator) [NS-2].
Note that no versions of the NS itself are used in this simulation
study. Only the visualization tool was borrowed for verification
purposes.
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In contrast with theoretical models, which may have assumptions not
applicable to lossy links, real-life data was used for two aspects of
the simulations:
* Link Failure Model: Derived from time-varying real network traces
containing packet delivery probability for each link, over all
channels, for both indoor network deployment and outdoor network
deployment.
* Topology: Gathered from real-life deployment (traces mentioned
above) as opposed to random topology simulations.
A 45-node topology, deployed as an outdoor network and shown in
Figure 1, and a 2442-node topology, gathered from a smart meter
network deployment, were used in the simulations. In Figure 1, links
between a most preferred parent node and child nodes are shown in
red. Links that are shown in black are also part of the topology but
are not between a preferred parent and child node.
Figure 1 [See the PDF.]
Figure 1: Outdoor Network Topology with 45 Nodes.
Note that this is just a start to validate the simulation before
using large-scale networks.
A set of time-varying link quality data was gathered from a real
network deployment to form a database used for the simulations. Each
link in the topology randomly 'picks up' a link model (trace) from
the database. Each link has a Packet Delivery Ratio (PDR) that
varies with time (in the simulation, a new PDR is read from the
database every 10 minutes) according to the gathered data. Packets
are dropped randomly from that link with probability (1 - PDR). Each
time a packet is about to be sent, the module generates a random
number using the Mersenne Twister random number generation method.
The random number is compared to the PDR to determine whether the
packet should be dropped. Note that each link uses a different
random number generator to maintain true randomness in the simulator
and to avoid correlation between links. Also, the packet drop
applies to all kinds of data and control packets (RPL), such as the
DIO, DAO, and DIS packets defined in [RFC6550]. Figure 2 shows a
typical temporal characteristic of links from the indoor network
traces used in the simulations. The figure shows several links with
perfect connectivity, some links with a PDR as low as 10%, and
several for which the PDR may vary from 30% to 80%, sharply changing
back and forth between a high value (strong connectivity) and a low
value (weak connectivity).
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Figure 2 [See the PDF.]
Figure 2: Example of Link Characteristics.
In the RPL simulator, the LBR (LLN Border Router) or the Directed
Acyclic Graph (DAG) root first initiates sending out DIO messages,
and the DAG is gradually constructed. RPL makes use of trickle
timers: the protocol sets a minimum time period with which the nodes
start re-issuing DAOs, and this minimum period is denoted by the
trickle parameter Imin. RPL also sets an upper limit on how many
times this time period can be doubled; this is denoted by the
parameter DIOIntervalDoublings, as defined in [RFC6550]. For the
simulation, Imin is initially set to 1 second and
DIOIntervalDoublings is equal to 16, and therefore the maximum time
between two consecutive DIO emissions by a node (under a steady
network condition) is 18.2 hours. The trickle time interval for
emitting DIO messages assumes the initial value of 1 second and then
changes over simulation time, as mentioned in [RFC6206].
Another objective of this study is to give insight to the network
administrator on how to tweak the trickle values. These
recommendations could then be used in applicability statement
documents.
Each node in the network, other than the LBR or DAG root, also emits
DAO messages as specified in [RFC6550], to initially populate the
routing tables with the prefixes received from children via the DAO
messages to support Point-to-Point (P2P) and Point-to-Multipoint
(P2MP) traffic in the "down" direction. During these simulations, it
is assumed that each node is capable of storing route information for
other nodes in the network (storing mode of RPL).
For nodes implementing RPL, as expected, the routing table memory
requirement varies according to the position in the DODAG
(Destination-Oriented DAG). The (worst-case) assumption is made that
there is no route summarization (aggregation) in the network. Thus,
a node closer to the DAG will have to store more entries in its
routing table. It is also assumed that all nodes have equal memory
capacity to store the routing states.
For simulations of the indoor network, each node sends traffic
according to a Constant Bit Rate (CBR) to all other nodes in the
network, over the simulation period. Each node generates a new data
packet every 10 seconds. Each data packet has a size of 127 bytes
including 802.15.4 PHY/MAC headers and RPL packet headers. All
control packets are also encapsulated with 802.15.4 PHY/MAC headers.
To simulate a more realistic scenario, 80% of the packets generated
by each node are destined to the root, and the remaining 20% of the
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packets are uniformly assigned as destined to nodes other than the
root. Therefore, the root receives a considerably larger amount of
data than other nodes. These values may be revised when studying P2P
traffic so as to have a majority of traffic going to all nodes as
opposed to the root. In the later part of the simulation, a typical
home/building routing scenario is also simulated, and different path
quality metrics are computed for that traffic pattern.
The packets are routed through the DODAG built by RPL according to
the mechanisms specified in [RFC6550].
A number of RPL parameters are varied (such as the packet rate from
each source and the time period for emitting a new DAG sequence
number) to observe their effect on the performance metric of
interest.
4. Performance Metrics
4.1. Common Assumptions
As the DAO messages are used to feed the routing tables in the
network, they grow with time and size of the network. Nevertheless,
no constraint was imposed on the size of the routing table nor on how
much information the node can store. The routing table size is not
expressed in terms of Kbytes of memory usage but measured in terms of
the number of entries for each node. Each entry has the next-hop
node and path cost associated with the destination node.
The link ETX (Expected Transmission Count) metric is used to build
the DODAG and is specified in [RFC6551].
4.2. Path Quality
Hop Count: For each source-destination pair, the number of hops for
both RPL and shortest path routing is computed. Shortest path
routing refers to a hypothetical ideal routing protocol that would
always provide the shortest path in terms of ETX path cost (or
whichever metric is used) in the network.
The Cumulative Distribution Function (CDF) of the hop count for all
paths (n * (n - 1) in an n-node network) in the network with respect
to the hop count is plotted in Figure 3 for both RPL and shortest
path routing. One can observe that the CDF corresponding to 4 hops
is around 80% for RPL and 90% for shortest path routing. In other
words, for the given topology, 90% of the paths have a path length of
4 hops or less with an ideal shortest path routing methodology,
whereas in RPL P2P routing, 90% of the paths will have a length of no
more than 5 hops. This result indicates that despite having a
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non-optimized P2P routing scheme, the path quality of RPL is close to
an optimized P2P routing mechanism for the topology under
consideration. Another reason for this may relate to the fact that
the DAG root is at the center of the network; thus, routing through
the DAG root is often close to an optimal (shortest path) routing.
This result may be different in a topology where the DAG root is
located at one end of the network.
Figure 3 [See the PDF.]
Figure 3: CDF of Hop Count versus Hop Count.
ETX Path Cost: In the simulation, the total ETX path cost (defined
in the Terminology section) from source to destination for each
packet is computed.
Figure 4 shows the CDF of the total ETX path cost, both with RPL and
shortest path routing. Here also one can observe that the ETX path
cost from all sources to all destinations is close to that of
shortest path routing for the network.
Figure 4 [See the PDF.]
Figure 4: CDF of Total ETX Path Cost along Path versus ETX Path Cost.
Path Stretch: The path stretch metric encompasses the stretch factor
for both hop distance and ETX path cost (as defined in the
Terminology section). The hop distance stretch, which is
determined as the difference between the number of hops taken by a
packet while following a route built via RPL and the number of
hops taken by shortest path routing (using link ETX as the
metric), is computed. The ETX path cost stretch is also provided.
The CDF of both path stretch metrics is plotted against the value of
the corresponding path stretch over all packets in Figures 5 and 6,
for hop distance stretch and ETX path stretch, respectively. It can
be observed that, for a few packets, the path built via RPL has fewer
hops than the ideal shortest path where path ETX is minimized along
the DAG. This is because there are a few source-destination pairs
where the total ETX path cost is equal to or less than that of the
ideal shortest path when the packet takes a longer hop count. As the
RPL implementation ignores a 20% change in total ETX path cost before
switching to a new parent or emitting a new DIO, it does not
necessarily provide the shortest path in terms of total ETX path
cost. Thus, this implementation yields a few paths with smaller hop
counts but larger (or equal) total ETX path cost.
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Figure 5 [See the PDF.]
Figure 5: CDF of Hop Distance Stretch versus
Hop Distance Stretch Value.
Figure 6 [See the PDF.]
Figure 6: CDF of ETX Path Stretch versus ETX Path Stretch Value.
The data for the CDF of the hop count and ETX path cost for the ideal
shortest path (SP) and a path built via RPL, along with the CDF of
the routing table size, is given below in Table 1. Figures 3 to 7
relate to the data in this table.
+---------+--------+---------+-----------+------------+-------------+
| CDF | Hop | Hop | ETX Cost | ETX Cost | Routing |
| (%age) | (SP) | (RPL) | (SP) | (RPL) | Table Size |
+---------+--------+---------+-----------+------------+-------------+
| 0 | 1.0 | 1.0 | 1 | 1.0 | 0 |
| 5 | 1.0 | 1.03 | 1 | 1.242 | 1 |
| 10 | 2.0 | 2.0 | 2 | 2.048 | 2 |
| 15 | 2.0 | 2.01 | 2 | 2.171 | 2 |
| 20 | 2.0 | 2.06 | 2 | 2.400 | 2 |
| 25 | 2.0 | 2.11 | 2 | 2.662 | 3 |
| 30 | 2.0 | 2.42 | 2 | 2.925 | 3 |
| 35 | 2.0 | 2.90 | 3 | 3.082 | 3 |
| 40 | 3.0 | 3.06 | 3 | 3.194 | 4 |
| 45 | 3.0 | 3.1 | 3 | 3.41 | 4 |
| 50 | 3.0 | 3.15 | 3 | 3.626 | 4 |
| 55 | 3.0 | 3.31 | 3 | 3.823 | 5 |
| 60 | 3.0 | 3.50 | 3 | 4.032 | 6 |
| 65 | 3.0 | 3.66 | 3 | 4.208 | 7 |
| 70 | 3.0 | 3.92 | 4 | 4.474 | 7 |
| 75 | 4.0 | 4.16 | 4 | 4.694 | 7 |
| 80 | 4.0 | 4.55 | 4 | 4.868 | 8 |
| 85 | 4.0 | 4.70 | 4 | 5.091 | 9 |
| 90 | 4.0 | 4.89 | 4 | 5.488 | 10 |
| 95 | 4.0 | 5.65 | 5 | 5.923 | 12 |
| 100 | 5.0 | 7.19 | 9 | 10.125 | 44 |
+---------+--------+---------+-----------+------------+-------------+
Table 1: Path Quality CDFs.
Overall, the path quality metrics give us important information about
the protocol's performance when minimizing the ETX path cost is the
objective to form the DAG. The protocol, as explained, does not
always provide an optimum path, especially for peer-to-peer
communication. However, it does end up reducing the control overhead
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cost, thereby reducing unnecessary parent selection and DIO message
forwarding events, by choosing a non-optimized path. Despite this
specific implementation technique, around 30% of the packets travel
the same number of hops as an ideal shortest path routing mechanism,
and 20% of the packets experience the same number of attempted
transmissions to reach the destination. On average, this
implementation costs only a few extra transmission attempts and saves
a large number of control packet transmissions.
4.3. Routing Table Size
The objective of this metric is to observe the distribution of the
number of entries per node. Figure 7 shows the CDF of the number of
routing table entries for all nodes. Note that 90% of the nodes need
to store less than 10 entries in their routing table for the topology
under study. The LBR does not have the same power or memory
constraints as regular nodes do, and hence it can accommodate entries
for all the nodes in the network. The requirement to accommodate
devices with low storage capacity has been mandated in [RFC5673],
[RFC5826], and [RFC5867]. However, when RPL is implemented in
storing mode, some nodes closer to the LBR or DAG root will require
more memory to store larger routing tables.
Figure 7 [See the PDF.]
Figure 7: CDF of Routing Table Size with Respect to Number of Nodes.
4.4. Delay Bound for P2P Routing
For delay-sensitive applications, such as home and building
automation, it is critical to optimize the end-to-end delay.
Figure 8 shows the upper bound and distributions of delay for paths
between any two given nodes for different hop counts between the
source and destination. Here, the hop count refers to the number of
hops a packet travels to reach the destination when using RPL paths.
This hop distance does not correspond to the shortest path distance
between two nodes. Note that each packet has a length of 127 bytes,
with a 240-kbps radio, which makes the transmission delay
approximately 4 milliseconds (ms).
Figure 8 [See the PDF.]
Figure 8: Comparison of Packet Latency, for Different Path Lengths,
Expressed in Hop Count.
RFCs 5673 [RFC5673] and 5548 [RFC5548] mention a requirement for the
end-to-end delivery delay to remain within a bounded latency. For
instance, according to the industrial routing requirement,
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non-critical closed-loop applications may have a latency requirement
that can be as low as 100 ms, whereas monitoring services may
tolerate a delay in the order of seconds. The results show that
about 99% of the end-to-end communication (where the maximum hop
count is 7 hops) is bounded within the 100-ms requirement, for the
topology under study. It should be noted that due to poor link
condition, there may be packet drops triggering retransmission, which
may cause larger end-to-end delivery delays. Nodes in the proximity
of the LBR may become congested at high traffic loads, which can also
lead to higher end-to-end delay.
4.5. Control Packet Overhead
The control plane overhead is an important routing characteristic in
LLNs. It is imperative to bound the control plane overhead. One of
the distinctive characteristics of RPL is that it makes use of
trickle timers so as to reduce the number of control plane packets by
eliminating redundant messages. The aim of this performance metric
is thus to analyze the control plane overhead both in stable
conditions (no network element failure overhead) and in the presence
of failures.
Data and control plane traffic comparison for each node: Figure 9
shows the comparison between the amount of data packets
transmitted (including forwarded packets) and control packets (DIO
and DAO messages) transmitted for all individual nodes when link
ETX is used to optimize the DAG. As mentioned earlier, each node
generates a new data packet every 10 seconds. Here one can
observe that a considerable amount of traffic is routed through
the DAG root itself. The x axis indicates the node ID in the
network. Also, as expected, the nodes that are closer to the DAG
root and that act as routers (as opposed to leaves) handle much
more data traffic than other nodes. Nodes 12, 36, and 38 are
examples of nodes next to the DAG root, taking part in routing
most of the data packets and hence having many more data packet
transmissions than other nodes, as observed in Figure 9. We can
also observe that the proportion of control traffic is negligible
for those nodes. This result also reinforces the fact that the
amount of control plane traffic generated by RPL is negligible on
these topologies. Leaf nodes have comparable amounts of data and
control packet transmissions (they do not take part in routing the
data).
Figure 9 [See the PDF.]
Figure 9: Amount of Data and Control Packets Transmitted against
Node Id Using Link ETX as Routing Metric.
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Data and control packet transmission with respect to time: In
Figures 10, 11, and 12, the amount of data and control packets
transmitted for node 12 (low rank in DAG, closer to the root),
node 43 (in the middle), and node 31 (leaf node) are shown,
respectively. These values stand for the number of data and
control packets transmitted for each 10-minute interval for the
particular node, to help understand what the ratio is between data
and control packets exchanged in the network. One can observe
that nodes closer to the DAG root have a higher proportion of data
packets (as expected), and the proportion of control traffic is
negligible in comparison with the data traffic. Also, the amount
of data traffic handled by a node within a given interval varies
largely over time for a node closer to the DAG root, because in
each interval the destination of the packets from the same source
changes, while 20% of the packets are destined to the DAG root.
As a result, the pattern of the traffic that is handled changes
widely in each interval for the nodes closer to the DAG root. For
the nodes that are farther away from the DAG root, the ratio of
data traffic to control traffic is smaller, since the amount of
data traffic is greatly reduced.
The control traffic load exhibits a wave-like pattern. The amount of
control packets for each node drops quickly as the DODAG stabilizes,
due to the effect of trickle timers. However, when a new DODAG
sequence is advertised (global repair of the DODAG), the trickle
timers are reset and the nodes start emitting DIOs frequently again
to rebuild the DODAG. For a node closer to the DAG root, the amount
of data packets is much larger than that of control packets and
somewhat oscillatory around a mean value. The amount of control
packets exhibits a 'saw-tooth' behavior. In the case where the ETX
link metric is used, when the PDR changes, the ETX link metric for a
node to its child changes, which may lead to choosing a new parent
and changing the DAG rank of the child. This event resets the
trickle timer and triggers the emission of a new DIO. Also, the
issue of a new DODAG sequence number triggers DODAG re-computation
and resets the trickle timers. Therefore, one can observe that the
number of control packets attains a high value for one interval and
comes down to lower values for subsequent intervals. The interval
with a high number of control packets denotes the interval where the
timers to emit a new DIO are reset more frequently. As the network
stabilizes, the control packets are less dense in volume. For leaf
nodes, the amount of control packets is comparable to that of data
packets, as leaf nodes are more prone to face changes in their DODAG
rank as opposed to nodes closer to the DAG root when the link ETX
value in the topology changes dynamically.
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Figure 10 [See the PDF.]
Figure 10: Amount of Data and Control Packets Transmitted
for Node 12.
Figure 11 [See the PDF.]
Figure 11: Amount of Data and Control Packets Transmitted
for Node 43.
Figure 12 [See the PDF.]
Figure 12: Amount of Data and Control Packets Transmitted
for Node 31.
4.6. Loss of Connectivity
Upon link failures, a node may lose its parents -- preferred and
backup (if any) -- thus leading to a loss of connectivity (no path to
the DAG root). RPL specifies two mechanisms for DODAG repairs,
referred to as global repair and local repair. In this document,
simulation results are presented to evaluate the amount of time data
packets are dropped due to a loss of connectivity for the following
two cases: a) when only using global repair (i.e., the DODAG is
rebuilt thanks to the emission of new DODAG sequence numbers by the
DAG root), and b) when using local repair (poisoning the sub-DAG in
case of loss of connectivity) in addition to global repair. The idea
is to tune the frequency at which new DODAG sequence numbers are
generated by the DAG root, and also to observe the effect of varying
the frequency for global repair and the concurrent use of global and
local repair. It is expected that more frequent increments of DODAG
sequence numbers will lead to a shorter duration of connectivity loss
at a price of a higher rate of control packets in the network. For
the use of both global and local repair, the simulation results show
the trade-off in amount of time that a node may remain without
service and total number of control packets.
Figure 13 shows the CDF of time spent by any node without service,
when the data packet rate is one packet every 10 seconds and a new
DODAG sequence number is generated every 10 minutes. This plot
reflects the property of global repair without any local repair
scheme. When all the parents are temporarily unreachable from a
node, the time before it hears a DIO from another node is recorded,
which gives the time without service. We define the DAG repair timer
as the interval at which the LBR increments the DAG sequence number,
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thus triggering a global re-optimization. In some cases, this value
might go up to the DAG repair timer value, because until a DIO is
heard, the node does not have a parent and hence no route to the LBR
or other nodes not in its own sub-DAG. Clearly, this situation
indicates a lack of connectivity and loss of service for the node.
Figure 13 [See the PDF.]
Figure 13: CDF: Loss of Connectivity with Global Repair.
The effect of the DAG repair timer on time without service is plotted
in Figure 14, where the source rate is 20 seconds/packet and in
Figure 15, where the source sends a packet every 10 seconds.
Figure 14 [See the PDF.]
Figure 14: CDF: Loss of Connectivity for Different
Global Repair Period, Source Rate 20 Seconds/Packet.
Figure 15 [See the PDF.]
Figure 15: CDF: Loss of Connectivity for Different
Global Repair Period, Source Rate 10 Seconds/Packet.
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The data for Figures 13 and 15 can be found in Table 2. The table
shows how the CDF of time without connectivity to the LBR increases
while we increase the time period to emit new DAG sequence numbers,
when the nodes generate a packet every 10 seconds.
+---------+------------------+------------------+-------------------+
| CDF | Repair Period | Repair Period | Repair Period |
| (%age) | 10 Minutes | 30 Minutes | 60 Minutes |
+---------+------------------+------------------+-------------------+
| 0 | 0.464 | 0.045 | 0.027 |
| 5 | 0.609 | 0.424 | 0.396 |
| 10 | 1.040 | 1.451 | 0.396 |
| 15 | 1.406 | 3.035 | 0.714 |
| 20 | 1.934 | 3.521 | 0.714 |
| 25 | 2.113 | 5.461 | 1.856 |
| 30 | 3.152 | 5.555 | 1.856 |
| 35 | 3.363 | 7.756 | 6.173 |
| 40 | 4.9078 | 8.604 | 6.173 |
| 45 | 8.575 | 9.181 | 14.751 |
| 50 | 9.788 | 21.974 | 14.751 |
| 55 | 13.230 | 30.017 | 14.751 |
| 60 | 17.681 | 31.749 | 16.166 |
| 65 | 29.356 | 68.709 | 16.166 |
| 70 | 34.019 | 92.974 | 302.459 |
| 75 | 49.444 | 117.869 | 302.459 |
| 80 | 75.737 | 133.653 | 488.602 |
| 85 | 150.089 | 167.828 | 488.602 |
| 90 | 180.505 | 271.884 | 488.602 |
| 95 | 242.247 | 464.047 | 488.602 |
| 100 | 273.808 | 464.047 | 488.602 |
+---------+------------------+------------------+-------------------+
Table 2: Loss of Connectivity Time, Data Rate - 10 Seconds / Packet.
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The data for Figure 14 can be found in Table 3. The table shows how
the CDF of time without connectivity to the LBR increases while we
increase the time period to emit new DAG sequence numbers, when the
nodes generate a packet every 20 seconds.
+---------+------------------+------------------+-------------------+
| CDF | Repair Period | Repair Period | Repair Period |
| (%age) | 10 Minutes | 30 Minutes | 60 Minutes |
+---------+------------------+------------------+-------------------+
| 0 | 0.071 | 0.955 | 0.167 |
| 5 | 0.126 | 2.280 | 1.377 |
| 10 | 0.403 | 2.926 | 1.409 |
| 15 | 0.902 | 3.269 | 1.409 |
| 20 | 1.281 | 16.623 | 3.054 |
| 25 | 2.322 | 21.438 | 5.175 |
| 30 | 2.860 | 48.479 | 5.175 |
| 35 | 3.316 | 49.495 | 10.30 |
| 40 | 3.420 | 93.700 | 25.406 |
| 45 | 6.363 | 117.594 | 25.406 |
| 50 | 11.500 | 243.429 | 34.379 |
| 55 | 19.703 | 277.039 | 102.141 |
| 60 | 22.216 | 284.660 | 102.141 |
| 65 | 39.211 | 285.101 | 328.293 |
| 70 | 63.197 | 376.549 | 556.296 |
| 75 | 88.986 | 443.450 | 556.296 |
| 80 | 147.509 | 452.883 | 1701.52 |
| 85 | 154.26 | 653.420 | 2076.41 |
| 90 | 244.241 | 720.032 | 2076.41 |
| 95 | 518.835 | 1760.47 | 2076.41 |
| 100 | 555.57 | 1760.47 | 2076.41 |
+---------+------------------+------------------+-------------------+
Table 3: Loss of Connectivity Time, Data Rate - 20 Seconds / Packet.
Figure 16 shows the effect of the DAG global repair timer period on
control traffic. As expected, as the frequency at which new DAG
sequence numbers are generated increases, the amount of control
traffic decreases because DIO messages are sent less frequently to
rebuild the DODAG. However, reducing the control traffic comes at a
price of increased loss of connectivity when only global repair is
used.
Figure 16 [See the PDF.]
Figure 16: Amount of Control Traffic for Different
Global Repair Periods.
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From the above results, it is clear that the time the protocol takes
to re-establish routes and to converge, after an unexpected link or
device failure happens, is fairly long. [RFC5826] mandates that "the
routing protocol MUST converge within 0.5 seconds if no nodes have
moved". Clearly, implementation of a repair mechanism based on new
DAG sequence numbers alone would not meet the requirements. Hence, a
local repair mechanism, in the form of poisoning the sub-DAG and
issuing a DIS, has been adopted.
The effect of the DAG repair timer on time without service when local
repair is activated is now observed and plotted in Figure 17, where
the source rate is 20 seconds/packet. A comparison of the CDF of
loss of connectivity for the global repair mechanism and the global +
local repair mechanism is shown in Figures 18 and 19 (semi-log plots,
x axis in logarithmic scale and y axis in linear scale), where the
source generates a packet every 10 seconds and 20 seconds,
respectively. For these plots, the x axis shows time in log scale,
and the y axis denotes the corresponding CDF in linear scale. One
can observe that using local repair (with poisoning of the sub-DAG)
greatly reduces loss of connectivity.
Figure 17 [See the PDF.]
Figure 17: CDF: Loss of Connectivity for Different DAG Repair Timer
Values for Global+Local Repair, Source Rate 20 Seconds/Packet.
Figure 18 [See the PDF.]
Figure 18: CDF: Loss of Connectivity for Global Repair and
Global+Local Repair, Source Rate 10 Seconds/Packet.
Figure 19 [See the PDF.]
Figure 19: CDF: Loss of Connectivity for Global Repair and
Global+Local Repair, Source Rate 20 Seconds/Packet.
A comparison between the amount of control plane overhead used for
global repair only and for the global plus local repair mechanism is
shown in Figure 20, which highlights the improved performance of RPL
in terms of convergence time at very little extra overhead. From
Figure 19, in 85% of the cases the protocol finds connectivity to the
LBR for the concerned nodes within a fraction of seconds when local
repair is employed. Using only global repair leads to repair periods
of 150-154 seconds, as observed in Figures 13 and 14.
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Figure 20 [See the PDF.]
Figure 20: Number of Control Packets for Different
DAG Sequence Number Period, for Both Global Repair
and Global+Local Repair.
5. RPL in a Building Automation Routing Scenario
Unlike the previous traffic pattern, where a majority of the total
traffic generated by any node is destined to the root, this section
considers a different traffic pattern, which is more prominent in a
home or building routing scenario. In the simulations shown below,
the nodes send 60% of their total generated traffic to the physically
1-hop distant node and 20% of traffic to a 2-hop distant node; the
other 20% of traffic is distributed among other nodes in the network.
The CDF of path quality metrics such as hop count, ETX path cost,
average hop distance stretch, ETX path stretch, and delay for P2P
routing for all pairs of nodes is calculated. Maintaining a low
delay bound for P2P traffic is of high importance, as applications in
home and building routing typically have low delay tolerance.
5.1. Path Quality
Figure 21 shows the CDF of the hop count for both RPL and ideal
shortest path routing for the traffic pattern described above.
Figure 22 shows the CDF of the expected number of transmissions (ETX)
for each packet to reach its destination. Figures 23 and 24 show the
CDF of the stretch factor for these two metrics. To illustrate the
stretch factor, an example from Figure 24 will be given next. For
all paths built by RPL, 85% of the time, the path cost is less than
the path cost for the ideal shortest path plus one.
Figure 21 [See the PDF.]
Figure 21: CDF of End-to-End Hop Count for RPL and
Ideal Shortest Path in Home Routing.
Figure 22 [See the PDF.]
Figure 22: CDF of ETX Path Cost Metric for RPL and
Ideal Shortest Path in Home Routing.
Figure 23 [See the PDF.]
Figure 23: CDF of Hop Distance Stretch from Ideal Shortest Path.
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Figure 24 [See the PDF.]
Figure 24: CDF of ETX Metric Stretch from Ideal Shortest Path.
5.2. Delay
To get an idea of maximum observable delay in the above-mentioned
traffic pattern, the delay for different numbers of hops to the
destination for RPL is considered. Figure 25 shows how the end-to-
end packet latency is distributed for different packets with
different hop counts in the network.
Figure 25 [See the PDF.]
Figure 25: Packet Latency for Different Hop Counts in RPL.
For this deployment scenario, 60% of the traffic has been restricted
to a 1-hop neighborhood. Hence, intuitively, the protocol is
expected to yield path qualities that are close to those of ideal
shortest path routing for most of the paths. From the CDF of the hop
count and ETX path cost, it is clear that peer-to-peer paths are more
often closer to an ideal shortest path. The end-to-end delay for
distances within 2 hops is less than 60 ms for 99% of the delivered
packets, while packets traversing 5 hops or more are delivered within
100 ms 99% of the time. These results demonstrate that for a normal
routing scenario of an LLN deployment in a building, RPL performs
fairly well without incurring much control plane overhead, and it can
be applied for delay-critical applications as well.
6. RPL in a Large-Scale Network
In this section, we focus on simulating RPL in a large network and
study its scalability by focusing on a few performance metrics: the
latency and path cost stretch, and the amount of control packets.
The 2442-node smart meter network with its corresponding link traces
was used in this scalability study. To simulate a more realistic
scenario for a smart meter network, 100% of the packets generated by
each node are destined to the root. Therefore, no traffic is
destined to nodes other than the root.
6.1. Path Quality
To investigate RPL's scalability, the CDF of the ETX path cost in the
large-scale smart meter network is compared to a hypothetical ideal
shortest path routing protocol that minimizes the total ETX path cost
(Figure 26). In this simulation, the path stretch is also calculated
for each packet that traverses the network. The path stretch is
determined as the difference between the path cost taken by a packet
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while following a route built via RPL and a path computed using an
ideal shortest path routing protocol. The CDF of the ETX fractional
stretch, which is determined as the ETX metric stretch value over the
ETX path cost of an ideal shortest path, is plotted in Figure 27.
The fractional hop distance stretch value, as defined in the
Terminology section, is shown in Figure 28.
Looking at the path quality plots, it is obvious that RPL works in a
non-optimal fashion in this deployment scenario as well. However, on
average, for each source-destination pair, the ETX fractional stretch
is limited to 30% of the ideal shortest path cost. This fraction is
higher for paths with shorter distances and lower for paths where the
source and destination are far apart. The negative stretch factor
for the hop count is an interesting feature of this deployment and is
due to RPL's decision to not switch to another parent where the
improvement in path quality is not significant. As mentioned
previously, in this implementation, a node will only switch to a new
parent if the advertised ETX path cost to the LBR through the new
candidate parent is 20% better than the old one. The nodes tend to
hear DIOs from a smaller hop count first, and later do not always
shift to a larger hop count and smaller ETX path cost. As the
traffic is mostly to the DAG root, some P2P paths built via RPL do
yield a smaller hop count from source to destination, albeit at a
larger ETX path cost.
As observed in Figure 26, 90% of the packets transmitted during the
simulation have a (shortest) ETX path cost to destination less than
or equal to 12. However, via RPL, 90% of the packets will follow
paths that have a total ETX path cost of up to 14. Though all
packets are destined to the LBR, it is to be noted that this
implementation ignores a change of up to 20% in total ETX path cost.
Figures 27 and 28 indicate that all paths have a very low ETX
fractional stretch factor as far as the total ETX path cost is
concerned, and some of the paths have lower hop counts to the LBR or
DAG root as well when compared to the hop count of the ideal shortest
path.
Figure 26 [See the PDF.]
Figure 26: CDF of Total ETX Path Cost versus ETX Path Cost.
Figure 27 [See the PDF.]
Figure 27: CDF of ETX Fractional Stretch versus
ETX Fractional Stretch Value.
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Figure 28 [See the PDF.]
Figure 28: CDF of Fractional Hop Count Stretch.
6.2. Delay
Figure 29 shows how end-to-end packet latency is distributed for
different hop counts in the network. According to [RFC5548], Urban
LLNs (U-LLNs) are delay tolerant, and the information, except for
critical alarms, should arrive within a fraction of the reporting
interval (within a few seconds). The packet generation for this
deployment has been set higher than usual to incur high traffic
volume, and nodes generate data once every 30 seconds. However, the
end-to-end latency for most of the packets is condensed between
500 ms and 1 s, where the upper limit corresponds to packets
traversing longer (greater than or equal to 6 hops) paths.
Figure 29 [See the PDF.]
Figure 29: End-to-End Packet Delivery Latency
for Different Hop Counts.
6.3. Control Packet Overhead
Figure 30 shows the comparison between data packets (originated and
forwarded) and control packets (DIO and DAO messages) transmitted by
each node (link ETX is used as the routing metric). Here one can
observe that in spite of the large scale of the network, the amount
of control traffic in the protocol is negligible in comparison to
data packet transmission. The smaller node ID for this network
actually indicates closer proximity to the DAG root, and nodes with
high ID numbers are actually farther away from the DAG root. Also,
as expected, we can observe in Figures 31, 32, and 33 that the
(non-leaf) nodes closer to the DAG root have many more data packet
transmissions than other nodes. The leaf nodes have comparable
amounts of data and control packet transmissions, as they do not take
part in routing the data. As seen before, the data traffic for a
child node has much less variation than the nodes that are closer to
the DAG root. This variation decreases with increase in DAG depth.
In this topology, Nodes 1, 2, and 3, etc., are direct children of
the LBR.
Figure 30 [See the PDF.]
Figure 30: Data and Control Packet Comparison.
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Figure 31 [See the PDF.]
Figure 31: Data and Control Packets over Time for Node 1.
Figure 32 [See the PDF.]
Figure 32: Data and Control Packets over Time for Node 78.
Figure 33 [See the PDF.]
Figure 33: Data and Control Packets over Time for Node 300.
In Figure 34, the effect of the global repair period timer on control
packet overhead is shown.
Figure 34 [See the PDF.]
Figure 34: Numbers of Control Packets for Different
Global Repair Timer Periods.
7. Scaling Property and Routing Stability
An important metric of interest is the maximum load experienced by
any node (CPU usage) in terms of the number of control packets
transmitted by the node. Also, to get an idea of scaling properties
of RPL in large-scale networks, it is also key to analyze the number
of packets handled by the RPL nodes for networks of different sizes.
In these simulations, at any given interval, the node with maximum
control overhead load is identified. The amount of maximum control
overhead processed by that node is plotted against time for three
different networks under study. The first one is Network 'A', which
has 45 nodes and is shown in Figure 1 (Section 3); the second is
Network 'B', which is another deployed outdoor network with 86 nodes;
and the third is Network 'C', which is the large deployed smart meter
network with 2442 nodes as noted previously in this document.
In Figure 35, the comparison of maximum control loads is shown for
different network sizes. For the network with 45 nodes, the maximum
number of control packets in the network stays within a limit of
50 packets (per 1-minute interval), where for the networks with 86
and 2442 nodes, this limit stretches to 100 and 2 * 10^3 packets per
1-minute interval, respectively.
Figure 35 [See the PDF.]
Figure 35: Scaling Property of Maximum Control Packets
Processed by Any Node over Time.
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For a network built with low-power devices interconnected by lossy
links, it is of the utmost importance to ensure that routing packets
are not flooded in the entire network and that the routing topology
stays as stable as possible. Any change in routing information,
especially parent-child relationships, would reset the timer, leading
to emitting new DIOs, and would hence change the node's path metric
to reach the root. This change will trigger a series of control
plane messages (RPL packets) in the DODAG. Therefore, it is
important to carefully control the triggering of DIO control packets
via the use of thresholds.
In this study, the effect of the tolerance value that is considered
before emitting a DIO reflecting a new path cost is analyzed. Four
cases are considered:
o No change in DAG depth of a node is ignored;
o The implementation ignores a 10% change in the ETX path cost to
the DAG root. That is, if the change in total path cost to the
root/LBR -- due to DIO reception from the most preferred parent or
due to shifting to another parent -- is less than 10%, the node
will not advertise the new metric to the root;
o The implementation ignores a 20% change in ETX path cost to the
DAG root for any node before deciding to advertise a new depth;
o The implementation ignores a 30% change in the total ETX path cost
to the DAG root of a node before deciding to advertise a new
depth.
This decision does affect the optimum path quality to the DAG root.
As observed in Figure 36, for 0% tolerance, 95% of paths used have an
ETX fractional stretch factor of less than 10%. Similarly, for 10%
and 20% tolerance levels, 95% of paths will have a 15% and 20% ETX
fractional path stretch. However, the increased routing stability
and decreased control overhead are the profit gained from the 10%
extra increase in path length or ETX path cost, whichever is used as
the metric to optimize the DAG.
Figure 36 [See the PDF.]
Figure 36: ETX Fractional Stretch Factor
for Different Tolerance Levels.
As the above-mentioned threshold also affects the path taken by a
packet, this study also demonstrates the effect of the threshold on
routing stability (number of times P2P paths change between a source
and a destination). For Network 'A' (shown in Figure 1) and the
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large smart meter network 'C', the CDF of path change is plotted in
Figures 37 and 38, respectively, against the fraction of path change
for different thresholds (triggering the emission of a new DIO upon
path cost change).
If X packets are transferred from source A to destination B, and out
of X times, Y times the path between this source-destination pair is
changed, then we compute the fraction of path change as Y/X * 100%.
This metric is computed over all source-destination pairs, and the
CDF is plotted in the y axis.
Figure 37 [See the PDF.]
Figure 37: Distribution of Fraction of Path Change for Network A.
Figure 38 [See the PDF.]
Figure 38: Distribution of Fraction of Path Change
for Large Network C.
This document also compares the CDF of the fraction of path change
for three different networks -- A, B, and C. Figure 39 shows how the
three networks exhibit a change of P2P path when a 30% change in
metric cost to the root is ignored before shifting to a new parent.
Figure 39 [See the PDF.]
Figure 39: Comparison of Distribution of Fraction of Path Change.
8. Comments
All the simulation results presented in this document corroborate the
expected protocol behavior for the topologies and traffic model used
in the study. For the particular discussed scenarios, the protocol
is shown to meet the desired delay and convergency requirements and
to exhibit self-healing properties without external intervention,
incurring negligible control overhead (only a small fraction of data
traffic). RPL provided near-optimum path quality for most of the
packets in the scenarios considered here and is able to trade off
control overhead for path quality via configurable parameters (such
as decisions on when to switch to a new parent), as per the
application and device requirements; thus, RPL can trade off routing
stability for control overhead as well. Finally, as per the
requirement of urban LLN deployments, the protocol is shown to scale
to larger topologies (several thousand nodes), for the topologies
considered in this implementation.
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9. Security Considerations
This document describes investigations performed in the Castalia
wireless sensor network simulator; it does not consider packets on
the Internet. [RFC6550] describes security considerations for RPL
networks.
10. Acknowledgements
The authors would like to acknowledge Jerald P. Martocci, Mukul
Goyal, Emmanuel Monnerie, Philip Levis, Omprakash Gnawali, and Craig
Partridge for their valuable and helpful suggestions over metrics to
include and overall feedback.
11. Informative References
[Castalia-2.2]
Boulis, A., "Castalia: Revealing pitfalls in designing
distributed algorithms in WSN", Proceedings of the 5th
international conference on Embedded networked sensor
systems (SenSys'07), pp. 407-408, 2007.
[NS-2] "The Network Simulator version 2 (ns-2)",
<http://www.isi.edu/nsnam/ns/>.
[OMNeTpp] Varga, A., "The OMNeT++ Discrete Event Simulation System",
Proceedings of the European Simulation
Multiconference (ESM'2001), June 2001.
[RFC5548] Dohler, M., Ed., Watteyne, T., Ed., Winter, T., Ed., and
D. Barthel, Ed., "Routing Requirements for Urban Low-Power
and Lossy Networks", RFC 5548, May 2009.
[RFC5673] Pister, K., Ed., Thubert, P., Ed., Dwars, S., and T.
Phinney, "Industrial Routing Requirements in Low-Power and
Lossy Networks", RFC 5673, October 2009.
[RFC5826] Brandt, A., Buron, J., and G. Porcu, "Home Automation
Routing Requirements in Low-Power and Lossy Networks",
RFC 5826, April 2010.
[RFC5867] Martocci, J., Ed., De Mil, P., Riou, N., and W. Vermeylen,
"Building Automation Routing Requirements in Low-Power and
Lossy Networks", RFC 5867, June 2010.
[RFC6206] Levis, P., Clausen, T., Hui, J., Gnawali, O., and J. Ko,
"The Trickle Algorithm", RFC 6206, March 2011.
Tripathi, et al. Informational [Page 25]
RFC 6687 Performance Evaluation of RPL October 2012
[RFC6550] Winter, T., Ed., Thubert, P., Ed., Brandt, A., Hui, J.,
Kelsey, R., Levis, P., Pister, K., Struik, R., Vasseur,
JP., and R. Alexander, "RPL: IPv6 Routing Protocol for
Low-Power and Lossy Networks", RFC 6550, March 2012.
[RFC6551] Vasseur, JP., Ed., Kim, M., Ed., Pister, K., Dejean, N.,
and D. Barthel, "Routing Metrics Used for Path Calculation
in Low-Power and Lossy Networks", RFC 6551, March 2012.
[ROLL-TERMS]
Vasseur, JP., "Terminology in Low power And Lossy
Networks", Work in Progress, September 2011.
Authors' Addresses
Joydeep Tripathi (editor)
Drexel University
3141 Chestnut Street 7-313
Philadelphia, PA 19104
USA
EMail: jt369@drexel.edu
Jaudelice C. de Oliveira (editor)
Drexel University
3141 Chestnut Street 7-313
Philadelphia, PA 19104
USA
EMail: jau@coe.drexel.edu
JP. Vasseur (editor)
Cisco Systems, Inc.
11, Rue Camille Desmoulins
Issy Les Moulineaux 92782
France
EMail: jpv@cisco.com
Tripathi, et al. Informational [Page 26]