Internet Engineering Task Force (IETF) E. Birrane, III
Request for Comments: 9657 JHU/APL
Category: Informational N. Kuhn
ISSN: 2070-1721 Thales Alenia Space
Y. Qu
Futurewei Technologies
R. Taylor
Aalyria Technologies
L. Zhang
Huawei
September
October 2024
Time-Variant Routing (TVR) Use Cases
Abstract
This document introduces use cases where Time-Variant Routing (TVR)
computations (i.e., routing computations that take into consideration
time-based or scheduled changes to a network) could improve routing
protocol convergence and/or network performance.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are candidates for any level of Internet
Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc9657.
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Table of Contents
1. Introduction
2. Resource Preservation
2.1. Assumptions
2.2. Routing Impacts
2.3. Example
3. Operating Efficiency
3.1. Assumptions
3.2. Routing Impacts
3.3. Example: Cellular Network
3.4. Another Example: Tidal Network
4. Dynamic Reachability
4.1. Assumptions
4.2. Routing Impacts
4.3. Example: Mobile Satellites
4.4. Another Example: Predictable Moving Vessels
5. Security Considerations
6. IANA Considerations
7. Informative References
Acknowledgments
Authors' Addresses
1. Introduction
There is a growing number of use cases where changes to the routing
topology are an expected part of network operations. In these use
cases, the pre-planned loss and restoration of an adjacency, or
formation of an alternate adjacency, should be seen as a
nondisruptive event.
Expected changes to topologies can occur for a variety of reasons.
In networks with mobile nodes, such as unmanned aerial vehicles and
some orbiting spacecraft constellations, links are lost and re-
established as a function of the mobility of the platforms. In
networks without reliable access to power, such as networks
harvesting energy from wind and solar, link activity might be
restricted to certain times of day. Similarly, in networks
prioritizing green computing and energy efficiency over data rate,
network traffic might be planned around energy costs or expected user
data volumes.
This document defines three categories of use cases where a route
computation might beneficially consider time information. Each of
these use cases are included as follows:
1. An overview of the use case describing how route computations
might select different paths (or subpaths) as a function of time.
2. A set of assumptions made by the use case as to the nature of the
network and data exchange.
3. Specific discussion on the routing impacts of the use case.
4. Example networks conformant to the use case.
The use cases that are considered in this document are as follows:
1. Resource Preservation (described in Section 2), where there is
information about link availability over time at the client
level. Time-Variant Routing (TVR) can utilize the predictability
of the link availability to optimize network connectivity by
taking into account endpoint resource preservation.
2. Operating Efficiency (described in Section 3), where there is a
server cost or a path cost usage varying over time. TVR can
exploit the predictability of the path cost to optimize the cost
of the system exploitation. The notion of a path cost is
extended to be a time-dependent function instead of a constant.
3. Dynamic Reachability (described in Section 4), where there is
information about link availability variation between nodes in
the end-to-end path. TVR can exploit the predictability of the
link availability to optimize in-network routing.
The document does not intend to represent the full set of cases where
TVR computations could beneficially impact network performance -- new
use cases are expected to be generated over time. Similarly, the
concrete examples within each use case are meant to provide an
existence proof of the use case and not to present any exhaustive
enumeration of potential examples. It is likely that multiple
example networks exist that could be claimed as instances of any
given use case.
The document focuses on deterministic scenarios. Non-deterministic
scenarios, such as vehicle-to-vehicle communication, are out of the
scope of the document.
2. Resource Preservation
Some nodes in a network might operate in resource-constrained
environments or otherwise with limited internal resources.
Constraints, such as available power, thermal ranges, and on-board
storage, can all impact the instantaneous operation of a node. In
particular, resource management on such a node can require that
certain functionality be powered on (or off) to extend the ability of
the node to participate in the network.
When power on a node is running low, noncritical functions on the
node might be turned off in favor of extending node life.
Alternatively, certain functions on a node may be turned off to allow
the node to use available power to respond to an event, such as data
collection. When a node is in danger of violating a thermal
constraint, normal processing might be paused in favor of a
transition to a thermal safe mode until a regular operating condition
is reestablished. When local storage resources run low, a node might
choose to expend power resources to compress, delete, or transmit
data off the node to free up space for future data collection. There
might also be cases where a node experiences a planned offline state
to save and accumulate power.
In addition to power, thermal, and storage, other resource
constraints may exist on a node such that the preservation of
resources is necessary to preserve the existence (and proper
function) of the node in the network. Nodes operating in these
conditions might benefit from TVR computations as the connectivity of
the node changes over time as part of node preservation.
2.1. Assumptions
To effectively manage on-board functionality based on available
resources, a node must comprehend specific aspects concerning the
utilization and replenishment of resources. It is expected that
patterns of the environment, device construction, and operational
configuration exist with enough regularity and stability to allow
meaningful planning. The following assumptions are made with this
use case:
1. Known resource expenditures. It is assumed that there exists
some determinable relationship between the resources available on
a node and the resources needed to participate in a network. A
node would need to understand when it has met some condition for
participating in, or dropping out of, a network. This is
somewhat similar to predicting the amount of battery life left on
a laptop as a function of likely future usage.
2. Predictable resource accumulation. It is assumed that the
accumulation of resources on a node are predictable such that a
node might expect (and be able to communicate) when it is likely
to next rejoin a network. This is similar to predicting the time
at which a battery on a laptop will be fully charged.
3. Consistent cost functions. It is assumed that resource
management on a node is deterministic such that the management of
a node as a function of resource expenditure and accumulation is
consistent enough for link planning.
2.2. Routing Impacts
Resource management in these scenarios might involve turning off
elements of the node as part of on-board resource management. These
activities can affect data routing in a variety of ways.
1. Power Savings. On-board radios may be turned off to allow other
node processing. This may happen on power-constrained devices to
extend the battery life of the node or to allow a node to perform
some other power-intensive task.
2. Thermal Savings. On-board radios may be turned off if there are
thermal considerations on the node, such as an increase in a
node's operating temperature.
3. Storage Savings. On-board radios may be turned on with the
purpose of transmitting data off the node to free local storage
space to collect new data.
Whenever a communications device on a node changes its powered state
there is the possibility (if the node is within range of other nodes
in a network) that the topology of the network is changed, which
impacts route calculations through the network. Additionally,
whenever a node joins a network there may be a delay between the
joining of the node to the network and any discovery that may take
place relating to the status of the node's functional neighborhood.
During these times, forwarding to and from the node might be delayed
pending some synchronization.
2.3. Example
An illustrative example of a network necessitating resource
preservation is an energy-harvesting wireless sensor network. In
such a network, nodes rely exclusively on environmental sources for
power, such as solar panels. On-board power levels may fluctuate
based on various factors including sensor activity, processing
demands, and the node's position and orientation relative to its
energy source.
Consider a simple three-node network where each node accumulates
power through solar panels. Power available for radio frequency (RF)
transmission is shown in Figure 1. In this figure, each of the three
nodes (Node 1, Node 2, and Node 3) has a different plot of available
power over time. This example assumes that a node will not power its
radio until available power is over some threshold, which is shown by
the horizontal line on each plot.
Node 1 Node 2 Node 3
P | P | ------- P | --
o | ---- -- o | / \ o | / \
w |~/~~~~\~~~~~/~~\~~ w |~/~~~~~~~~~\~~~~~~ w |~~~~~~~~/~~~~\~~~~
e |/ \ / \ e |/ \ e | / \
r | --- - r | ----- r |------- ---
+---++----++----++- +---++----++----++- +---++----++----++-
t1 t2 t3 t1 t2 t3 t1 t2 t3
Time Time Time
Figure 1: Node Power over Time
The connectivity of this three-node network changes over time in ways
that may be predictable and are likely able to be communicated to
other nodes in this small sensor network. Examples of connectivity
are shown in Figure 2. This figure shows a sample of network
connectivity at three times: t1, t2, and t3.
* At time t1, Node 1 and Node 2 have their radios powered on and are
expected to communicate.
* At time t2, it is expected that Node 1 has its radio off but that
Node 2 and Node 3 can communicate.
* Finally, at time t3, it is expected that Node 1 may be turning its
radio off, that Node 2 and Node 3 are not powering their radios,
and there is no expectation of connectivity.
+----------+ +----------+ +----------+
t1 | Node 1 |--------| Node 2 | | Node 3 |
+----------+ +----------+ +----------+
+----------+ +----------+ +----------+
t2 | Node 1 | | Node 2 |--------| Node 3 |
+----------+ +----------+ +----------+
+----------+ +----------+ +----------+
t3 | Node 1 | | Node 2 | | Node 3 |
+----------+ +----------+ +----------+
Figure 2: Topology over Time
3. Operating Efficiency
Some nodes in a network might alter their networking behavior to
optimize metrics associated with the cost of a node's operation.
While the resource preservation use case described in Section 2
addresses node survival, this use case discusses non-survival
efficiencies such as the financial cost to operate the node and the
environmental impact (cost) of using that node.
When a node operates using some preexisting infrastructure, there is
typically some cost associated with the use of that infrastructure.
Sample costs are included as follows:
1. Nodes that use existing wireless communications, such as a
cellular infrastructure, must pay to communicate to and through
that infrastructure.
2. Nodes supplied with electricity from an energy provider pay for
the power they use.
3. Nodes that cluster computation and activities might increase the
temperature of the node and incur additional costs associated
with cooling the node (or collection of nodes).
4. Beyond financial costs, assessing the environmental impact of
operating a node may also be modeled as a cost associated with
node operation, to include achieving carbon credits or other
incentives for green computing.
When the cost of using a node's resources changes over time, a node
can benefit from predicting when data transmissions might optimize
costs, environmental impacts, or other metrics associated with
operation.
3.1. Assumptions
The ability to predict the impact of a node's resource utilization
over time presumes that the node exists within a defined environment
(or infrastructure). Some characteristics of these environments are
listed as follows:
1. Cost Measurability. The impacts of operating a node within its
environment can be measured in a deterministic way. For example,
the cost-per-bit of data over a cellular network or the cost-per-
kilowatt of energy used are known.
2. Cost Predictability. Changes to the impacts of resource
utilization are known in advance. For example, if the cost of
energy is less expensive in the evening than during the day,
there exists some way of communicating this change to a node.
3. Cost Persistent. Changes to the cost of operating in the
environment persist for a sufficient amount of time such that
behavior can be adjusted in response to changing costs. If costs
change too rapidly, it is likely not possible to meaningfully
react to their change.
4. Cost Magnitude. The magnitude of cost changes is such that a
node experiences a minimum threshold cost reduction through
optimization. A specified time period is designated for
measuring the cost reduction.
3.2. Routing Impacts
Optimizing resource utilization can affect route computation in ways
similar to those experienced with resource preservation. The route
computation may not change the available path, but the topology as
seen by an endpoint would be different. Cost optimization can impact
route calculation in a variety of ways, some of which are described
as follows:
1. Link Filtering. Data might be accumulated on a node waiting for
a cost-effective time for data transmission. Individual link
costs might be annotated with cost information such that
adjacencies with a too high cost might not be used for
forwarding. This effectively filters which adjacencies are used
(possibly as a function of the type of data being routed).
2. Burst Planning. In cases where there is a cost savings
associated with fewer longer transmissions (versus many smaller
transmissions), nodes might refuse to forward data until a
sufficient data volume exists to justify a transmission.
3. Environmental Measurement. Nodes that measure the quality of
individual links can compute the overall cost of using a link as
a function of the signal strength of the link. If link quality
is insufficient due to environmental conditions (such as clouds
on a free-space optical link or long distance RF transmission in
a storm) the cost required to communicate over the link may be
too much, even if access to infrastructure is otherwise in a less
expensive time of day.
In each of these cases, some consideration of the efficiency of
transmission is prioritized over achieving a particular data rate.
Waiting until data rate costs are lower takes advantage of platforms
using time-of-use rate plans -- both for pay-as-you-go data and
associated energy costs. Accumulating data volumes and choosing more
opportune times to transmit can also result in less energy
consumption by radios and, thus, less operating cost for platforms.
3.3. Example: Cellular Network
One example of a network where nodes might seek to optimize operating
cost is a set of nodes operating over cellular connections that
charge both peak and off-peak data rates. In this case, individual
nodes may be allocated a fixed set of "peak" minutes such that
exceeding that amount of time results in expensive overage charges.
Generally, the concept of peak and off-peak minutes exists to deter
the use of a given network at times when the cellular network is
likely to encounter heavy call volumes (such as during the workday).
Just as pricing information can act as a deterrent (or incentive) for
a human cellular user, this pricing information can be codified in
ways that also allow machine-to-machine (M2M) connections to
prioritize off-peak communications for certain types of data
exchange. Many M2M traffic exchanges involve schedulable activities,
such as nightly bulk file transfers, pushing software updates,
synchronizing datastores, and sending noncritical events and logs.
These activities are usually already scheduled to minimize impact on
businesses and customers but can also be scheduled to minimize
overall cost.
Consider a simple three-node network, similar to the one pictured in
Figure 1, except that in this case the resource that varies over time
is the cost of the data exchange. This case is illustrated below in
Figure 3. In this figure, a series of three plots are given, one for
each of the three nodes (Node 1, Node 2, and Node 3). Each of these
nodes exists in a different cellular service area that has different
peak and off-peak data rate times. This is shown in each figure by
times when the cost is low (off-peak) and when the cost is high
(peak).
Node 1 Node 2 Node 3
C | +--------- C |--+ C |-------------+
o | | o | | o | |
s | | s | | s | |
t |-------+ t | +---------------- t | +-------
| | |
+---++----++----++-- +----++----++----++-- +----++----++-----++--
t1 t2 t3 t1 t2 t3 t1 t2 t3
Time Time Time
Figure 3: Data Cost over Time
Given the presumption that peak times are known in advance, the cost
of data exchange from Node 1 through Node 2 to Node 3 can be
calculated. Examples of these data exchanges are shown in Figure 4.
From this figure, both times t1 and t3 result in a smaller cost of
data exchange than choosing to communicate data at time t2.
+-----------+ +-----------+ +-----------+
t1 | Node N1 |---LOW----| Node N2 |---HIGH---| Node N3 |
+-----------+ +-----------+ +-----------+
+-----------+ +-----------+ +-----------+
t2 | Node N1 |---HIGH---| Node N2 |---HIGH---| Node N3 |
+-----------+ +-----------+ +-----------+
+-----------+ +-----------+ +-----------+
t3 | Node N1 |---HIGH---| Node N2 |----LOW---| Node N3 |
+-----------+ +-----------+ +-----------+
Figure 4: Data Exchange Cost over Time
While not possible in every circumstance, a highly optimized plan
could be to communicate from Node 1 to Node 2 at time t1 and then
queue data at Node 2 until time t3 for delivery to Node 3. This case
is shown in Figure 5.
+-----------+ +-----------+
t1 | Node N1 |---LOW----| Node N2 |
+-----------+ +-----------+
+-----------+ +-----------+
t3 | Node N2 |----LOW---| Node N3 |
+-----------+ +-----------+
Figure 5: Data Cost Using Storage
3.4. Another Example: Tidal Network
Another example related to operating efficiency is often referred to
as a "tidal network," in which traffic volume undergoes significant
fluctuations at different times. Take, for instance, a campus
network, where thousands of individuals go to classrooms and
libraries during the daytime and retire to the dormitories at night.
This results in a regular oscillation of network traffic across
various locations within the campus.
In the context of a tidal network scenario, energy-saving methods may
include the deactivation of some or all components of network nodes.
These activities have the potential to alter network topology and
impact data routing in a variety of ways. Ports on network nodes can
be selectively disabled or enabled based on traffic patterns, thereby
reducing the energy consumption of nodes during periods of low
network traffic.
More information on tidal networks can be found in [TIDAL].
4. Dynamic Reachability
When a node is placed on a mobile platform, the mobility of the
platform (and thus the mobility of the node) may cause changes to the
topology of the network over time. The impacts on the dynamics of
the topology can be very important. To the extent that the relative
mobility between and among nodes in the network and the impacts of
the environment on the signal propagation can be predicted, the
associated loss and establishment of adjacencies can also be planned
for.
Mobility can cause the loss of an adjacent link in several ways, such
as that which follows:
1. Node mobility can cause the distance between two nodes to become
large enough that distance-related attenuation causes the mobile
node to lose connectivity with one or more other nodes in the
network.
2. Node mobility can also be used to maintain a required distance
from other mobile nodes in the network. While moving, external
characteristics may cause the loss of links through occultation
or other hazards of traversing a shared environment.
3. Node mobility can cause the distance between two nodes to vary
quickly over time, making it complicated to establish and
maintain connectivity.
4. Nodes equipped with communication terminals capable of adjusting
their orientation or moving behind and emerging from barriers
will also establish and lose connectivity with other nodes as a
function of that motion.
Mobile nodes, like any node, may encounter issues regarding resource
preservation and cost efficiency. In addition, they may face unique
challenges associated with their mobility. The intermittent
availability of links can lead to dynamic neighbor relationships at
the node level. This use case aims to examine the routing
implications of motion-induced changes to network topology.
4.1. Assumptions
Predicting the impact of node mobility on route computation requires
some information relating to the nature of the mobility and the
nature of the environment being moved through. Some information
presumed to exist for planning is listed as follows:
1. Path Predictability. The path of a mobile node through its
environment is known (or can be predicted) as a function of (at
least) time. It is presumed that mobile nodes using TVR
algorithms would not exhibit purely random motion.
2. Environmental Knowledge. When otherwise well-connected mobile
nodes pass through certain elements of their environment (such as
a storm, a tunnel, or the horizon), they may lose connectivity.
The duration of this connectivity loss is assumed to be
calculable as a function of node mobility and the environment
itself.
4.2. Routing Impacts
Changing a network topology affects the computation of paths (or
subpaths) through that topology. In particular, the following
features can be implemented in a network with mobile nodes such that
different paths might be computed over time:
1. Adjacent Link Expiration. A node might be able to predict that
an adjacency will expire as a function of that node's mobility,
the other node's mobility, or some characteristic of the
environment. Determining that an adjacency has expired allows a
route computation to plan for that loss rather than default to an
error recovery mechanism.
2. Adjacent Link Resumption. Just as the loss of an adjacency can
be predicted, it may be possible to predict when an adjacency
will resume.
3. Data Rate Adjustments. The achievable data rate over a given
link is not constant over time and may vary significantly as a
function of both relative mobility between a transmitter and
receiver as well as the environment being transmitted through.
Knowledge of both mobility and environmental state may allow for
prediction of data rates, which may impact path computation.
4. Adjacent Link Filtering. Separate from the instantaneous
presence or absence of an adjacency, a route computation might
choose to not use an adjacency if that adjacency is likely to
expire in the near future or if it is likely to experience a
significant drop in predicted data rate.
4.3. Example: Mobile Satellites
A relatively new type of mobile network that has emerged over the
past several years is the Low Earth Orbit (LEO) networked
constellation. There are a number of such constellations being built
by both private industry and governments. While this example
describes LEO satellite systems, the mobility events can be applied
to satellite systems orbiting at different altitudes (including Very
LEO (V-LEO) or Medium Earth Orbit (MEO)).
Many LEO networked constellations have a similar operational concept
of hundreds to thousands of inexpensive spacecraft that can
communicate both with their orbital neighbors as well as down to any
ground station that they happen to be passing over. A ground station
is a facility used to communicate with satellites in LEO. The
relationship between an individual spacecraft and an individual
ground station becomes somewhat complex as each spacecraft may only
be over a single ground station for a few minutes at a time.
Moreover, as a function of the constellation topology, there are
scenarios where (1) the inter-satellite links need to be shut down
for interference avoidance purposes or (2) the network topology
changes, which modifies the neighbors of a given spacecraft.
A LEO networked constellation represents a good example of planned
mobility based on the predictability of spacecraft in orbit. While
other mobile vehicles may encounter unpredictable fluctuations in
velocity, spacecraft operate in an environment with relatively stable
velocity conditions. This determinism makes them an excellent
candidate for TVR computations. However, inter-satellite link
failures could still introduce unpredictability in the network
topology.
Consider three spacecraft (N1, N2, and N3) following each other
sequentially in the same orbit. This is sometimes called a "string
of pearls" configuration. Spacecraft N2 always maintains
connectivity to its two neighbor spacecraft: N1, which is behind in
the orbit, and N3, which is ahead in the orbit. This configuration
is illustrated in Figure 6. While these spacecraft are all mobile,
their relative mobility ensures continuous contact with each other
under normal conditions.
.--. .--. .--.
####-| N1 |-#### <---> ####-| N2 |-#### <---> ####-| N3 |-####
\__/ \__/ \__/
Figure 6: Three Sequential Spacecraft
Flying over a ground station imposes a non-relative motion between
the ground and the spacecraft -- namely that any given ground station
will only be in view of the spacecraft for a short period of time.
The times at which each spacecraft can see the ground station is
shown in the plots in Figure 7. In this figure, ground contact is
shown when the plot is high, and a lack of ground contact is shown
when the graph is low. From this, we see that spacecraft N3 can see
ground at time t1, N2 sees ground at time t2, and spacecraft N1 sees
ground at time t3.
Spacecraft N1 Spacecraft N2 Spacecraft N3
G | G | G |
r | +--+ r | +--+ r | +--+
o | | | o | | | o | | |
u | | | u | | | u | | |
n |--------------+ +- n |---------+ +------- n |---+ +-------------
d | d | d |
+---++----++----++-- +----++----++----++-- +----++----++----++--
t1 t2 t3 t1 t2 t3 t1 t2 t3
Time Time Time
Figure 7: Spacecraft Ground Contacts over Time
Since the ground station in this example is stationary, each
spacecraft will pass over it, resulting in a change to the network
topology. This topology change is shown in Figure 8. At time t1,
any message residing on N3 and destined for the ground could be
forwarded directly to the ground station. At time t2, that same
message would need to, instead, be forwarded to N2 and then forwarded
to ground. By time t3, the same message would need to be forwarded
from N2 to N1 and then down to ground.
+------+ +------+
t1 | N2 |----------| N3 |
+------+ +---+--+
|
/|\
\___/
/ \
Ground
Station
------------------------------------------------------------------
+------+ +------+ +------+
t2 | N1 |----------| N2 |----------| N3 |
+------+ +---+--+ +------+
|
/|\
\___/
/ \
Ground
Station
------------------------------------------------------------------
+------+ +------+ +------+
t3 | N1 |----------| N2 |----------| N3 |
+---+--+ +------+ +------+
|
/|\
\___/
/ \
Ground
Station
------------------------------------------------------------------
Figure 8: Constellation Topology over Time
This example focuses on the case where the spacecrafts fly over a
ground station and introduce changes in the network topology. There
are also scenarios where the in-constellation network topology varies
over time following a deterministic time-driven operation from the
ground system. More information on in-constellation network topology
can be found in [SAT-CONSTELLATION] and [SCN]. For this example, and
in particular for within constellation network topology changes, the
TVR approach is important to avoid the Interior Gateway Protocol
(IGP) issues mentioned in [SAT-CONSTELLATION].
4.4. Another Example: Predictable Moving Vessels
Another relevant example for this use case involves the movement of
vessels with predictable trajectories, such as ferries or planes.
These endpoints often rely on a combination of satellite and
terrestrial systems for Internet connectivity, capitalizing on their
predictable journeys.
This scenario also covers situations where nodes employ dynamic
pointing solutions to track the mobility of other nodes. In such
cases, nodes dynamically adjust their antennas and application
settings to determine the optimal timing for data transmission along
the path.
5. Security Considerations
While this document does not define a specific mechanism or solution,
it serves to motivate the use of time-based validation and revocation
strategies. Therefore, security considerations are anticipated to be
addressed elsewhere, such as within a TVR schedule definition or
through a protocol extension utilizing a TVR schedule. However, it's
important to note that time synchronization is critical within a
network employing a TVR schedule. Any unauthorized changes to
network clocks can disrupt network functionality, potentially leading
to a Denial of Service (DoS) attack.
6. IANA Considerations
This document has no IANA actions.
7. Informative References
[SAT-CONSTELLATION]
Han, L., Li, R., Retana, A., Chen, M., Su, L., and T.
Jiang, "Problems and Requirements of Satellite
Constellation for Internet", Work in Progress, Internet-
Draft, draft-lhan-problems-requirements-satellite-net-06,
4 January 2024, <https://datatracker.ietf.org/doc/html/
draft-lhan-problems-requirements-satellite-net-06>.
[SCN] Wood, L., "Satellite Constellation Networks",
Internetworking and Computing over Satellite Networks, pp.
13-34, DOI 10.1007/978-1-4615-0431-3_2, April 2003,
<https://link.springer.com/
chapter/10.1007/978-1-4615-0431-3_2>.
[TIDAL] Zhang, L., Zhou, T., Dong, J., and N. Nzima, "Use Case of
Tidal Network", Work in Progress, Internet-Draft, draft-
zzd-tvr-use-case-tidal-network-02, 28 July 2023,
<https://datatracker.ietf.org/doc/html/draft-zzd-tvr-use-
case-tidal-network-02>.
Acknowledgments
Many thanks to Tony Li, Peter Ashwood-Smith, Abdussalam Baryun,
Arashmid Akhavain, Dirk Trossen, Brian Sipos, Alexandre Petrescu,
Haoyu Song, Hou Dongxu, Tianran Zhou, Jie Dong, Nkosinathi Nzima, and
Vinton Cerf for their useful comments that helped improve the
document.
Authors' Addresses
Edward J. Birrane, III
JHU/APL
Email: edward.birrane@jhuapl.edu
Nicolas Kuhn
Thales Alenia Space
Email: nicolas.kuhn.ietf@gmail.com
Yingzhen Qu
Futurewei Technologies
Email: yingzhen.ietf@gmail.com
Rick Taylor
Aalyria Technologies
Email: rtaylor@aalyria.com
Li Zhang
Huawei
Email: zhangli344@huawei.com