2000 Systems Engineering Capstone Conference • University of Virginia
A SIMULATION ANALYSIS OF THE INITIALIZATION
OF DOCSIS-COMPLIANT CABLE MODEM SYSTEMS
Student team: Rolando Domdom, Brian Espey, Mark Goodman, Karen Jones, Vincent Lim
Faculty Advisor: Dr. Stephen Patek
Department of Systems Engineering
Client Advisors: Jerry Lankford and Mac Hartless
Ericsson, Inc.
Lynchburg, VA
E-mail: EUSJERL@am1.ericsson.se
KEYWORDS: Cable Modem, DOCSIS, Event-Driven
simulation, MAC Protocol, Contention Resolution
ABSTRACT
An analysis of Data-Over-Cable System Interfaces
Specifications (DOCSIS) cable modem (CM) systems
has been conducted, with specific emphasis on
initialization and effective network recovery time. An
object-oriented, event-driven simulation has been
developed to simulate cable modem network topologies
consisting of 200, 500, and 1000 CMs. In particular,
the simulation is a high-fidelity model of a singlechannel upstream system, with the assumption of an
infinite bandwidth downstream.
In the analysis, distributions of CM initialization
completion time were constructed using data from the
simulation trials. The plots of the various CM network
topologies show aggregated distributions of CM
initialization time. The 200 and 500 CM network
distributions tend to exhibit a leftward-skew, with
heavy right tails, while the 1000 CM network exhibits
almost a constant rate of CM initialization.
INTRODUCTION
In this paper we present preliminary simulation
results that indicate the recovery times of generic
DOCSIS-compliant networks with up to 1000 CMs that
share a single upstream channel. We consider the case
where all CMs receive power simultaneously, as though
power in the metropolitan area is suddenly restored
after a thunderstorm. The problem we consider is very
similar to the “Ready, Go! Situation” studied by
Ishihara and Okada, which relates to the transient
performance of Ethernet in the classroom where large
numbers of students compete for server access over a
common LAN (Ishihara 1999).
Other researchers have examined the performance
of competing MAC layer protocols for hybrid fiber
cable networks. The purpose of a MAC protocol is to
define a bandwidth allocation scheme and to resolve
contentions. In DOCSIS-compliant networks the
contention resolution algorithm is defined in detail, so
the major decision vendors must make is how to
generate the bandwidth allocation MAP. The first HFC
(hybrid fiber/coax) MAC protocol was the (X)DQRAP
(Wu 1994). The basic principle of this algorithm was a
fixed number of data grant and request opportunities.
The next protocol, CPR, was very similar to the first;
the primary difference was redefining a data mini-slot
to allow for piggybacking requests (Limb 1997).
Piggybacking involves requesting bandwidth during a
data grant period. This feature significantly reduces the
number of request mini-slots needed and causes less
contention during those periods. More recently,
Golmie et al. have compared the performance of
various MAC protocols for HFC networks (Golmie
1997), and IEEE 802.14 and DOCSIS networks
(Golmie 1998).
The basic problem with the current research is that
little information is known about the head-end control
mechanism. Control mechanisms are highly
proprietary, and therefore, have little published in the
public domain. DOCSIS defines this structure and the
basic protocol for cable modem networks, but no
information exists regarding implementation details. In
addition, all prior research as focused on steady-state
traffic flow rather than the initialization process. Our
simulation focuses on the algorithm’s performance
during initialization.
PROJECT SCOPE
This project was commissioned by Ericsson,
Incorporated, a worldwide supplier of
43
A Simulation Analysis of the Initialization of DOCSIS-Compliant Cable Modem Systems
telecommunications products, who is currently
undergoing the development of a new line of cable
modem and cable modem termination system (CMTS)
products. These cable modem products are part of
Ericsson’s strategy to enter into the residential
broadband market.
As part of this cable modem development strategy,
Ericsson desired a means of analyzing the effective
time-to-recovery of a downed cable modem network
after catastrophic failure. This time-to-recovery is
defined as the time needed for all of a network’s CMs
to come back online after being disconnected due to a
network system failure, such as a power outage or a
malfunctioning CMTS.
To aid Ericsson in analyzing network recovery
time, a simulation was designed for the intent of
simulating and analyzing the effective time-to-recovery
of cable modem network systems. This simulation is
based on DOCSIS, which is the definition of a standard
interface between cable lines and modems that allows
interoperability between different CM products.
OVERVIEW OF DOCSIS NETWORKS
Development of cable modems is currently
governed by one of two protocols, IEEE 802.14 or
DOCSIS. The modems being developed by Ericsson
follow the DOCSIS protocol. DOCSIS was developed
by a consortium of cable TV operators, and defines the
interface specifications for a cable modem network.
A cable modem network is comprised of two basic
elements: the downstream and the upstream. The
downstream is the medium through which the CMTS
sends messages to all cable modems. Its bandwidth is
very wide, so for the purposes of modeling, one can
reasonably make the assumption that the bandwidth is
infinite. In other words, the CMTS can send as much
information as necessary to all attached CMs, and the
only factor which determines how long it will take a
CM to receive the message is the physical distance
between it and the CMTS.
The second major component of a DOCSIS
network is the upstream. The upstream is the medium
through which CMs send messages to the CMTS. In
general, these messages are varying types of requests,
either for physical maintenance or more upstream
bandwidth, or they are data packets that a particular
modem is sending to the CMTS. Within one upstream
channel, the signal for only one frame can occupy a
44
particular point on the cable line at a time. If two or
more frames occupy the cable at the same point and at
the same time, i.e. if a collision occurs, the CMTS is
unable to interpret the resulting message. Thus none of
the CMs that sent a request at that time on the same
upstream channel will receive a response from the
CMTS, and they will have to send another request. To
reduce the collision problems associated with this
process, DOCSIS defines a protocol that all CMs must
follow whenever they need to use the upstream.
The purpose of this protocol is twofold: 1) to
regulate access to the upstream, so that only certain
CMs can use it at a given time, and 2) to provide a
means of contention resolution, so that when a collision
does occur, it is resolved as quickly as possible. The
first function is implemented through the bandwidth
allocation MAP, and the second through a well-defined
contention resolution process.
Bandwidth Allocation MAP
The bandwidth allocation MAP is a message that
the CMTS sends to all CMs on a particular
downstream. It describes the ways in which an
upstream channel can be used for a certain range of
time. There are essentially three different types of
actions a CM can perform within a given MAP interval:
1) attempt to join the network, 2) request bandwidth, or
3) send data using bandwidth the CMTS has granted it.
The first two actions may result in contention, as
the time intervals described in these regions of the
MAP define broadcast opportunities. The third action
occurs within a unicast time interval, allowing only one
particular CM to transmit, so it is impossible for
contention to occur. The basic decision the CMTS
must make when constructing the MAP is how to
distribute the amount of time allocated to each of these
three types of actions.
The MAP is composed of an array of various types
of information elements (IE), each of which describes
allowed upstream usage for a certain time interval. In
our simulation, we consider four types of IEs: Initial
Maintenance, Station Maintenance, Requests, and
Grants.
A CM uses the first two when it begins the
initialization process. The purpose of these regions is
to range the CMs, so they are aware of the physical
propagation delay experienced by frames as they
traverse the cable line. The details of how these two
2000 Systems Engineering Capstone Conference • University of Virginia
regions are used are discussed below. After joining the
network, the CM only uses the second two types of IEs.
A portion of an example map is shown in Figure 1.
Address
BROADCAST
BROADCAST
CM2
Type
Initial
Maintenance
Request
Grant
Offset
0
Initialization Process
The initialization process for a single CM can be
divided into eight primary phases (DOCSIS, 1999).
Figure 2 gives a visual representation of the
initialization process flow.
4
16
Figure 1—Sample MAP Message
Each row in this table represents one IE. The
offset field is used for determining the time interval
described by the information element. For example, the
second IE in this map defines a broadcast Request
region that begins at a time that is 4 mini-slots after the
start time of this map. A mini-slot is a discrete unit of
time that must be a power of two multiple of 6.25 s.
All components on a DOCSIS network track time in
these discrete mini-slots.
Contention Resolution
When two or more CMs decide to send data within
the same time interval of a broadcast MAP region, a
collision occurs, destroying both messages. A
contention resolution algorithm (CRA) defines the
protocol the CMs must follow when attempting
retransmission. The CRA is based on the concept of
backoff windows. A backoff window defines an
interval from which a CM randomly selects a number.
This number indicates the number of transmission
opportunities it must defer before it attempts
retransmission.
DOCSIS defines that the size of the back-off
window must grow in truncated binary exponential
form, with the initial backoff window and the
maximum backoff window controlled by the CMTS.
The values are communicated to the CMs in the MAP
and represent a power-of-two value. When a CM sends
its first request it sets its back-off window, b, equal to
the Data Backoff Start defined in the map. The CM
randomly selects a number, R, between 0 and 2 b - 1. It
defers R transmission opportunities, retransmits, and
waits for a response from the CMTS. If the CM still
does not receive a response, it increments b by 1 (unless
b is equal to the maximum back-off value), and repeats
the process. This cycle continues until the CM receives
a response, or it until it exhausts its retries. DOCSIS
defines the exhaustion point as 16 retries.
Figure 2—Flow Diagram of DOCSIS Initialization [Adapted
from DOCSIS, 1999]
Our simulation captures the first seven of these
phases. The first two are primarily physical layer
operations, and are modeled in the simulation simply as
receipts of various types of control frames from the
CMTS. Beginning with the third phase, Ranging, each
CM must use the upstream to complete the required
tasks.
At the start of the Ranging process, a CM scans the
map for an Initial Maintenance region. It generates a
Ranging Request within this interval, and waits for a
Ranging Response from the CMTS as well as a Station
Maintenance opportunity in a subsequent map.
Because this region is broadcast, contention may occur,
in which case the CM would attempt retransmission
based on the CRA discussed above. Once the CMTS
receives the initial Ranging Request, it adds a unicast
Station Maintenance region for the CM in the next map.
The CM uses this region to generate a second Ranging
Request. Once it receives a response from the CMTS,
it moves on to the next phase of initialization.
The remaining phases are all implemented as
Request/Grant cycles. For instance, to obtain an IP
45
A Simulation Analysis of the Initialization of DOCSIS-Compliant Cable Modem Systems
address, the CM generates a Request for bandwidth,
waits for a Grant in the next map, and sends the IP
address request in a subsequent Grant region. The time
to completion for these phases is modeled as a normally
distributed random variable with a specified mean and
variance.
PROBLEM DEFINITION
Ericsson is most interested in the initialization
phase, since a network is considered to have fully
“recovered” from a power outage once all CMs have
completed initialization. Once power is restored, all
CMs will simultaneously come online and attempt to
initialize themselves on the network. The large number
of resulting collisions forces many CMs to back off and
defer transmission opportunities, causing increased time
to initialization and redundancy in data retransmission.
The focus of this paper was to determine how
recovery time varied with the number of CMs that were
attempting to initialize on a given network. We utilized
a basic map generation algorithm (discussed in the next
section), which prioritizes IEs by their type, to examine
the effectiveness of this algorithm.
THE SIMDOCSIS SIMULATION TOOL
Assumptions
In order to begin analyzing network recovery time,
a network simulation was designed to model DOCSIScompliant cable modem networks. While the
SimDOCSIS model attempts to follow the DOCSIS
specifications as accurately as possible, the following
assumptions were made to simplify modeling:






Downstream bandwidth is infinite
Processing time is constant for all frame types
Downstream and upstream are error free (no
noise)
Receipt of two SYNC messages completes
synchronization
Min and max backoff window exponents are
fixed during the operation of the network
Times for TFTP file transfer, IP address
acquisition, and time-of-day acquisition are
normally distributed
The assumption of infinite downstream bandwidth
is based on its significantly larger size relative to the
upstream’s smaller bandwidth. Also, with respect to
the receipt of SYNC messages, we assume that the
46
required physical operations occur instantaneously.
Thus, the only aspect that we model is the receipt of
two synchronization frames from the CMTS.
SimDOCSIS MAP Algorithm
The MAP algorithm implemented in the
SimDOCSIS simulation tool is based on the
prioritization of IEs by type. First, the CMTS checks to
see if needs to include an Initial Maintenance region,
based on the amount of time that has elapsed since the
last Initial Maintenance region was included. Next, the
CMTS grants Station Maintenance IEs for all CMs who
successfully sent Ranging Requests in a previous Initial
Maintenance interval. The CMTS then allocates grants
based on Requests that it has received. The CMTS uses
the remaining time described by the map for the
Request region. Each map is 400 mini-slots (10 ms)
long.
EXPERIMENT DESIGN
These experiments were designed to analyze the
effective time-to-recovery of a downed cable modem
network due to catastrophic failure, such as a power
outage. The experiment’s objective was to determine
how recovery time varies with the number of CMs on
the network. In order to analyze network recovery
time, three cable modem network topologies consisting
of 200, 500, and 1000 CMs were simulated. CMs were
evenly dispersed along a 5000-meter trunk-line from
the CMTS. Also, all CMs were initialized to come
online immediately (i.e. at 0 ms) to mimic the nearinstantaneous powering-on of CMs after a power
outage failure is resolved.
In this paper, we ignore the effect of network
traffic attributable to CMs that complete initialization.
This is based on the assumption that immediately after
a power-outage is resolved, people on the network will
not be attempting to use their modems, thus a minimal
amount of data traffic is generated.
CM initialization time data were collected from
fifty simulated trials of each network configuration.
The data were then aggregated and plotted in
histograms to approximate the initialization completion
time distributions for each of the three network types.
2000 Systems Engineering Capstone Conference • University of Virginia
Table 1—Experiment Simulation Parameters
Parameter
Number of CMs
CM Distances
Number of Upstreams
Time-Ticks Per Mini-Slot
Online Time
No. of Mini-Slots per Map
Slots Per Initial Maint.
Slots Per Station Maint.
Slots Per Request
Ranging-Backoff Window
Data-Backoff Window
TFTP Time Dist.
Value
200, 500, and 1000
Evenly dispersed within
5,000-meters of CMTS
1
4
0 ms
400
56
1
1
0-1
4-8
~N(2000, 10002)
RESULTS
200 CM Network
although the network consists of relatively few CMs,
meaning that contention is probably not the primary
factor. It is more likely that in this small network, these
last-to-initialize CMs had a longer TFTP response
interval relative to other CMs. This greater TFTP time
can significantly delay their initialization completion
while waiting for a TFTP response.
Most of the completion variance is in the 3,0009,000 ms region, which can be attributed to the highlevel of contention during these intervals. As the
number of initialized CMs increases, the variance of
completed CMs diminishes, reflecting the effect of
fewer contentions.
In general, a 200 CM network completes within
20,000 ms, with most completing CM initialization
within 14,000-17,000 ms region (75%-quartile is at
about 16,000 ms). Figure 4 depicts the total network
initialization times for the fifty simulated runs.
Results from the simulation trials of a 200 CM
network are shown below. Figure 3 is a histogram
showing the number of CMs initialized during a given
time interval for all fifty simulation runs. The middle
curve depicts the average number of CMs initialized
during an interval, and the two surrounding curves
represent the first standard deviations at that interval.
Figure 4—Cumulative Network Initialization Times for
Simulations of 200 CM Network
500 CM Network
Figure 3—Distribution of CM Initialization Times for
Simulations of 200 CM Network
This heavy-tailed distribution is left-skewed,
indicating that most CMs were able to quickly initialize
within the first half (~10,000 ms) of total simulation
time. However, the marginal number of CMs
initialized diminishes thereafter, and a significant
amount of time is needed to initialize the relatively few
remaining CMs. Part of this lag in time is attributable
to the result of heavy contention for the IM region,
Figure 5 gives the CM initialization times for a 500
CM network. Again, the distribution seems to exhibit
the leftward skew present in the 200 CM case, and
peaks in the 11,000-12,000 ms time interval. The
heavy tail in this larger CM network is due to both the
TFTP effects also present in the 200 CM network, and
the large amount of contention for IM slots. Further
examination into the levels of network contention have
found that, on average, a 500 CM network is subject to
approximately 20,000 IM contention incidences, which
is about a five-fold increase over the 2,000 incidences
of IM contention in the 200 CM networks.
47
A Simulation Analysis of the Initialization of DOCSIS-Compliant Cable Modem Systems
In Figure 7, we can see that a 500 CM network
generally has an effective time-to-recovery of 44,000
ms. The 75%-quartile lies at approximately in the
36,000-38,000 ms interval, where a little over 40
simulations complete the initialization process for all
500 modems.
Figure 5—Distribution of CM Initialization Times for
Simulations of 500 CM Network
Perhaps the most interesting feature of this graph is
the anomalous second “peak” that is apparent in the
16,000-18,000 ms time interval. Completion time
variance increases in this time interval as more CMs
come onto the network.
It is important to note that this phenomenon is
evident not only on the aggregate level, but also within
the individual simulation runs as well. Figure 6 shows
a sample of five simulation trials, in which the “dip” at
the 14,000-16,000 ms interval, and the corresponding
second peak are clearly present.
Figure 7—Cumulative Network Initialization Times for
Simulations of 500 CM Network
1000 CM Network
The 1000 CM network was the most timeconsuming to simulate, due to the large amount of
contention and network activity during the initialization
process. Figure 8 presents the distribution of CM
initialization times. The distinctiveness of the peaks,
which are so prominent in the 200 and 500 CM
networks, have flattened-out considerably as the
marginal rate of CM completion is more evenly
distributed across the entire initialization process.
Figure 6—Sample Plots of Individual Simulations of 500 CM
Networks
At this point the reason for the second peak is
unclear. One hypothesis is that these peaks are the
result of a sudden increase of CMs attempting to
initialize onto the network—such as when the duration
of a range response timeout expires, or when CMs
restart their backoff windows after exhausting their
retry limit. Further research is needed in profiling the
states of CMs throughout the initialization process to
determine the effects of timeouts and exhausting retries.
48
Figure 8—Distribution of CM Initialization Times for
Simulations of 1000 CM Network
2000 Systems Engineering Capstone Conference • University of Virginia
Another aspect of this flattened curve is that the
initialization distribution does not appear to exhibit an
obvious leftward shift present in the cases of the 200
and 500 CM networks. This indicates that, unlike the
other two smaller networks, there is actually enough IM
contention early in the initialization process to force the
majority of CMs to defer a large number of times
before they receive an IM acknowledgement. Thus the
marginal rate of initialization is much more gradual
than in the 200 or 500 CM network cases.
Figure 8 also shows evidence of the multiple-peak
effect first witnessed in the simulations of the 500 CM
network. In the 1000 CM case, however, there appear
to be at least two peaks—one in the 77,000-99,000 ms
interval, and another at 121,000-132,000 ms range. We
are currently investigating the cause of these peaks.
The 1000 CM network graph shows a fairly high
level of variance, which stays relatively constant over
the duration of the simulation, again indicating that
contention is consistently hampering CMs from
initializing themselves to the network. This high,
constant variance, even as the simulations near
completion, is uncharacteristic of the 200 CM and 500
CM networks.
The following graph in Figure 9 shows that a 1000
CM network has an initialization recovery time of
approximately 230,000 ms, with at least 75% of the
simulation trials completing their initialization
processes within 190,000-198,000 ms.
simulating and analyzing the performance of DOCSIScompliant cable modem networks. This project was
commissioned by Ericsson, Incorporated, a worldwide
supplier of telecommunications products, desired a
DOCSIS simulation to analyze the effective time-torecovery of a downed cable modem network after
catastrophic failure.
In order to begin analyzing recovery time, network
scenarios consisting of 200, 500 and 1000 CM
networks were each simulated for fifty trial runs. Data
were collected for each simulation run and aggregated
to produce histograms representing distributions of CM
initialization times over a given time interval. The
approximate recovery times for each network type are
given in Table 2.
Table 2—Summary of Network Recovery Times
CMs
200
500
1000
Approximate Recovery Time (ms)
20,000
44,000
230,000
In general, the 200 and 500 CM network cases
exhibited leftward-shifted distributions with heavy tails.
This indicated that most CMs were able to initialize
fairly quickly, but the remaining few tended to have
delayed completion times due to long TFTP response
times (primarily in the 200 CM case), and large
amounts of deferral opportunities resulting from IM
contention.
Unlike the 200 and 500 CM cases, the 1000 CM
network tends to show a flattened curve throughout the
initialization process, indicating a high enough level of
contention force many CMs to defer transmission
opportunities. This is reflected in the gradual (almost
linear) rate of CM initialization, relative to the other
two smaller networks.
Finally, the 500 and 1000 CM cases also show
evidence of “multiple-peaks.” However, the cause for
these additional peaks is unknown, and further
investigation into the individual CM states at these
peak-times is needed.
Figure 9—Cumulative Network Initialization Times for
Simulations of 500 CM Network
CONCLUSIONS
An object-oriented, event-driven simulation
program has been developed for the purpose of
RECOMMENDATIONS
The results presented in this paper give a good
first-cut approximation of effective network recovery
time from a catastrophic failure. Clearly, recovery time
can be improved by increasing the initial size of the IM
49
A Simulation Analysis of the Initialization of DOCSIS-Compliant Cable Modem Systems
region to accommodate the flood of early, simultaneous
traffic characteristics after a power outage. This would
require a trade-off with other aspects of the network,
such as data transmission. To effectively determine the
optimal use of bandwidth several factors should be
studied further.
Due to the extendable feature-set of SimDOCSIS,
additional types of analyses can be performed to
determine optimal bandwidth allocation algorithms.
The modular nature of the SimDOCSIS simulation
framework allows for the inclusion of multiple
bandwidth allocation MAP algorithms to test their
relative performance under similar conditions to the
experiments discussed in this paper.
Further analyses using SimDOCSIS should also
investigate the effects of including traffic modeling
(both telephony and data) upon initialization times.
The inclusion of traffic should significantly increase the
effective recovery time, and requires better, more
dynamic MAP algorithms to accommodate the tradeoffs
in initializing CMs still not on the network and granting
bandwidth to already initialized CMs. Including partial
grants and piggybacking should significantly aid in the
CM initialization process with traffic. Finally, quality
of service (QOS) needs to be considered for accurately
modeling networks with varying service levels for its
CMs.
REFERENCES
MCNS Holdings L.P. “Data-Over-Cable Service
Interface Specifications, Radio Frequency Interface
Specifications, SP-RFIv1.1-I02-990731” Cable
Television Laboratories, Inc., 1999.
Golmie, N., G. Pieris, S. Masson and D. Su. “A MAC
Protocol for HFC Networks: Design Issues and
Performance Evaluation.” Computer
Communications, vol. 20, pgs. 1042-1050, 1997.
Golmie, N., F. Monveaux, and D. Su. “A Comparison
of MAC Protocols for Hybrid Fiber/Coax
Networks: IEEE 802.14 vs. MCNS.” National
Institute of Standards and Technology, 1998.
Ishihara, S. and M. Okada: "Performance Investigation
of Ethernet LANs for Educational Computer
Systems", Proc. of SMC'99 - 1999 IEEE Systems,
Man, and Cybernetics Conference.
50
Limb, J.O. and D. Sala, “A Protocol for Efficient
Transfer of Data over Fiber/Coax Systems”, IEEE
Trans on Networking, Vol. 5, No. 6, Dec. 1997.
Wu, C-T. and G. Campbell, “Extended DQRAP
(XDQRAP) A Cable TV Protocol Functioning as a
Distributed Switch”, Proc. 1994 1st International
Workshop on Community Networking, pp. 191-198,
July 13-14, 1994.
BIOGRAPHIES
Rolando Domdom is a fourth-year Systems
Engineering major, concentrating in both computer
information and management systems. Rolando will be
working for Sprint Inc. in the Fall of 2000.
Brian Espey is a fourth-year Systems Engineering
major, concentrating in computer information systems,
with a minor in computer science. Brian has accepted a
software engineering position with Best! Software, Inc.
Mark Goodman is a fourth-year Systems Engineering
student, concentrating in economic systems. Mark will
be working for Merrill Lynch in New York City, NY.
Karen Jones is a fourth-year Systems Engineering
student, with a minor in biomedical systems. Karen has
accepted a position with Ernst & Young.
Vincent Lim is a fourth-year Systems Engineering
student, with a major in economics, and a minor in
computer science. Vince has accepted a position as an
investment baking analyst at Salomon Smith Barney.