Close
About
FAQ
Home
Collections
Login
USC Login
Register
0
Selected
Invert selection
Deselect all
Deselect all
Click here to refresh results
Click here to refresh results
USC
/
Digital Library
/
Computer Science Technical Report Archive
/
USC Computer Science Technical Reports, no. 863 (2005)
(USC DC Other)
USC Computer Science Technical Reports, no. 863 (2005)
PDF
Download
Share
Open document
Flip pages
Contact Us
Contact Us
Copy asset link
Request this asset
Transcript (if available)
Content
Admission Control and QoS for Continuous Media
Displays in MANETs
Shahram Ghandeharizadeh
Department of Computer Science
University of Southern California
Los Angeles, CA 90089, USA
Email: shahram@usc.edu
Shyam Kapadia
Department of Computer Science
University of Southern California
Los Angeles, CA 90089, USA
Email: kapadia@usc.edu
Bhaskar Krishnamachari
Department of Computer Science
Department of Electrical Engineering
University of Southern California
Los Angeles, CA 90089, USA
Email: bkrishna@usc.edu
Abstract— We consider continuous media delivery over a mo-
bile ad-hoc network of vehicles equipped with car-to-car peer-to-
peer (C2P2) devices. While the provision of high-bandwidth con-
tinuous media content with tight QoS requirements is challenging
even in static networks, it is considerably more challenging
when the network topology is dynamic due to node mobility.
In this paper, we develop a unified client-centric, distributed
admission control framework for such a C2P2 network. Under
this framework, we develop several admission control strategies,
namely, server count-based admission (SC), server bandwidth-
based admission (SB), path bandwidth-based admission (PB),
and finally a mobility-based admission (MAD [4]) policy, which
can be further enhanced using a second-level sampling-based
admission policy. We also develop QoS utility models to quantify
the performance of these policies. These policies are then eval-
uated through extensive experimental simulations. Our results
show conclusively that traditional admission control strategies
do not work, and that MAD can provide orders of magnitude
improvement when compared with no admission control.
I. INTRODUCTION
During the past few years, automobile manufacturers have
been marketing and selling vehicles equipped with entertain-
ment systems. These systems typically consist of a DVD
player, a fold-down screen, a video game console, and wireless
headphones. In its present form, storage and content are tied
together. This limits the number of available titles to those
DVDs and CDs in the vehicle. We envision a separation
of storage and content where content is staged on demand
across the available storage for previewing. This would provide
passengers access to a large repository of titles. In this vision,
vehicles are equipped with car-to-car, peer-to-peer (C2P2)
devices which form a mobile ad-hoc network (MANET).
Each C2P2 is equipped with abundant amount of storage, a
processor, and a wireless networking card. A C2P2 might
integrate into the navigation system of a vehicle and its
existing network for delivery of data to the on-board fold-
down screen or wireless headphones. Different C2P2 devices
might store different clips and exchange these clips with one
another to support on-demand delivery of continuous media.
The principle characteristic of continuous media such as
video and audio is their high sustained bit rate requirement. If
a system delivers a clip at a rate lower than its pre-specified
rate without special precautions (e.g., pre-fetching), the user
might observe frequent disruptions and delays with video and
random noises with audio. These artifacts are collectively
termed hiccups. A hiccup-free display is the ideal quality of
service (QoS) provided to an end user. A second QoS criterion
is the observed startup latency, defined as the delay observed
from when a user references a clip to the onset of display.
All C2P2 devices may replicate popular titles, but they must
collaborate in order to provide users with a large choice of
content. Each C2P2 may contribute a fraction of its storage in
a peer-to-peer manner to be occupied by the system-assigned
clips. These clips might be referenced by users of those mobile
C2P2 devices that are network reachable (including the local
user). This study focuses on the QoS metrics when a user
references a clip that is available on one or more remote
C2P2s. A C2P2 must offer its passengers only those clips
that it can download and play in a hiccup-free manner within
a reasonable amount of time (e.g. by satisfying a maximum
tolerable startup latency constraint).
Techniques that address these challenges are impacted by
the characteristics of the MANET, placement and delivery
scheduling of data, and admission control policies in support
of a hiccup-free display. Let us consider each in turn. Mobility,
a key MANET characteristic, is the primary challenge because
it dictates the life-time of paths between a producer and a
consumer of data. When the display of data is overlapped
with its delivery, a path repair may incur delays that result
in data starvation and hiccup. In addition, topology changes
impact the availability of both data [6] and bandwidth. The
availability of data is also impacted by its placement across the
nodes and its degree of replication [7]. One may replicate data
at the granularity of either a clip [3] or a block [5]. The system
may replicate the first few blocks of a clip more frequently.
This enables the display of a remote clip to consume the clip’s
first few blocks from either local storage or C2P2 devices that
are a few hops away, minimizing startup latency.
Techniques to schedule delivery of data impact the QoS
observed by active displays. As we shall demonstrate (see
Appendix), content replication across multiple servers and
block-switching techniques provide a significant degree of
protection against the frequent disruptions and topological
changes caused by the mobile environment, by dynamically
providing connections to proximate servers.
Finally, given the high data-rate requirements of continuous
media displays and limited bandwidth resources, the system
must be configured with an admission control policy to
handle multiple simultaneous clip downloads. In particular,
the admission control policy must strive to do a “good job”
of rejecting requests for download that are unlikely to be
satisfied within a reasonable amount of time. Section III
formalizes and quantifies this qualitative notion by providing
some utility models for QoS that take into account the number
of rejected requests, and the number of admitted requests that
are successful and the number of admitted requests that fail
to meet a specified startup latency constraint.
Compared to traditional admission control in static en-
vironments, there are some unique challenges in the C2P2
MANET environment where we have a variable topology
network. For one, the admission control must be performed in
a distributed manner as there is no central coordination point
for the network. Further, it is hard to estimate the resources
that will be available for a clip download (since the topology
will change during the duration of the download).
The key contribution of this paper is the design and analysis
of such simple admission control policies using C2P2/MANET
simulation studies. Even though these techniques are sim-
ple they capture significant components that would be part
of making an appropriate admission control decision. The
policies consider components such as number of available
servers, the available bandwidth at each server, the average
time for which the server is in the range of the client and the
bottleneck bandwidth along the path (traditional approach used
in wired networks). A more powerful and efficient admission
control scheme can be designed by appropriately weighing
each of these components and then combining them. This
study serves as a first step toward this goal by evaluating
policies that consider each of the components individually to
make the admission control decision and then observing the
performance in terms of some utility models.
Recent studies addressing QoS in MANETs are as follows.
A survey of QoS in MANETs is provided by [14], [11],
[17]. These studies either build on top of the existing routing
protocols [18], [12] like DSR,AODV etc or integrate the
QoS metrics as part of the routing protocol [10], [16], [19].
There have been studies that support both QoS and multicast
routing [1], [13]. Admission control has been a well known
problem dealt with in wired networks [9]. However, for
wireless ad hoc networks not much work has been done. In this
paper we have used the conventional definition of admission
control with simple policies to study the number of rejected,
satisfied and satisfied requests in a MANET (C2P2 network).
The rest of this paper is organized as follows.The im-
portance of data placement and retrieval scheduling policies
for single client display scenarios is demonstrated in the
Appendix. Next, Section II formulates the admission control
problem, and presents a general framework and several specific
policies to address this challenge. Section III evaluates these
policies using a simulation study on the basis of utility-
based QoS models. Our key findings are that the use of
intelligent admission control policies can significantly reduce
the number of admitted unsatisfied requests. Moreover, a
mobility-prediction based admission policy that we propose,
termed MAD, can provide an order of magnitude improvement
over a naive approach that admits all requests. Our conclusions
and future research directions are contained in Section IV.
II. ADMISSION CONTROL
Section II-A formalizes admission control as the process
of admitting those requests that are able to download a fix-
sized file within a specified duration of time. Section II-
B develops a framework for the admission control policies.
Finally, Section II-C presents four alternative policies for this
framework.
A. Introduction and Problem Statement
While it is possible to overlap the delivery and display
of continuous media clips using buffering, this strategy runs
the risk of incurring hiccups if network resources becomes
temporarily unavailable during delivery. In this work, we will
focus our attention on the delivery of clips that will be played
only after the entire clip is downloaded. We will assume that
each request specifies the file size of the clip to be downloaded
and a maximum download time (i.e. startup latency).
We formulate the admission control problem as follows: A
request to display a clip
comes with a specification that all
blocks of
be downloaded to the requesting C2P2 (denoted
as C2P2 ) within time units. Note that (the download
time or the startup latency) can be significantly smaller than
the playback
1
time of the clip
. For example, an audio
clip
with a display time of 12 minutes might specify as 1 minute. This means that all blocks of this clip must be
materialized in one minute in order for a request referencing
to be admitted to the system.
We shall consider two flavors of admission control: in-
stantaneous and delayed. In instantaneous admission control,
the decision to either reject or admit a request must be
made almost immediately after a request is issued. In delayed
admission control, the admission decision must be made in time units where . An admitted request is termed a
failed request if the system fails to materialize all blocks of
clip
in time units.
Thus, assuming requests are issued to a system, an
admission control policy yield three classes of requests. First,
a fixed number of admitted and rejected requests, termed and , respectively. Note . Second, some of the
admitted requests might fail, denoted as , . And,
finally, some of the admitted requests are serviced successfully,
denoted as , where . An admission control
policy should strive to maximize both and (ideally, to a
maximum of ). By maximizing the total number of admitted
requests ( ), a larger percentage of requests are processed by
the C2P2 MANET. By maximizing , a larger percentage of
admitted requests are processed successfully. In Section III,
we will develop QoS models that define a policy’s utility as a
weighted combination of these raw metrics.
1
The terms playback and display are used interchangeably in this paper
B. An Admission Control Framework
We now propose a framework for solving the admission
control problem assuming an ad-hoc network (e.g. no central-
ized base stations in a cellular wireless network). We focus
on distributed, client-centric, admission control policies that
enable the C2P2 client device to either admit or reject a
request. For the rest of this paper, C2P2 denotes a device
whose user has requested the display of a title.
In order to make an intelligent admission decision, the
admission control component of C2P2 must first obtain
information about the state of the network and availability
of resources. This information is then weighed against the
specifications of the request in order to determine if the request
can be admitted. The admission control policy may consider
several factors such as the number of servers, the availability
of residual bandwidth at servers, the bottleneck bandwidth on
the network path to the server, and whether it is possible to
predict from the mobility pattern of the cars that they might be
able to collaborate to deliver the requested clip. In this paper,
we design and evaluate policies that consider each of the above
mentioned factor individually. A meta-policy may combine
these policies by assigning each an appropriate weight.
In a mobile ad-hoc network such as C2P2, knowledge
of the current bandwidth on the path between a client and
server is insufficient to determine if a given request can be
satisfied. This is because the available bandwidth changes
dynamically and might be either greater or lower in the
future (due to unpredictable mobility-induced changes in the
network structure and network traffic). In order to contend with
the dynamic nature of a C2P2 network, flexible but simple
admission control policies must be designed that consider
different kinds of information about the current state of the
network, and try to predict/estimate based on this information
whether the required resources for a given request can be
provided. In our admission control framework, we develop two
kinds of metrics: a Request Metric ( ), and an Information
Metric (
). The request metric (which is normalized to
be a number between 0 and 100) is a measure of the amount of
resources required for a given request. Hence it depends on the
QoS parameter of interest. In particular, we define as the
ratio of the requested bandwidth over the nominal maximum
wireless link bandwidth as follows:
(1)
where "!$#
&%
is the size of the clip in Mb (MB), and
is also defined in Mbps (MBps).
While is not policy-dependent, the information metric
is, and represents quantitatively the information obtained
by the client about the current and future resources available
in the network. The information metric is normalized to be a
number between 0 and 100.
is monotonic in the estimated
resource availability — the higher the value of
, the more
likely it should be that a given request can be satisfied. Both
and have no units. Now the admission control decision
is simply a matter of comparing the two metrics:
and
, and deciding whether the given
predicts sufficient
availability of resources for the request with metric ’ . A
simple yet flexible form for the admission control policy is
simply to take the difference between
and and compare
with a threshold ( . When
*) ,+-( , a policy admits the
corresponding request. Otherwise the request is rejected.
Note that based on our definitions, the threshold can po-
tentially take on values from ) (which corresponds to
allowing all requests) to
(which corresponds to rejecting
all requests). Thus if ( is too low, there is a danger of greater
unsatisfied requests due to bandwidth contention. On the other
hand, if it is too high, the number of satisfied requests will
be low because too few requests are accepted. The optimal
choice of threshold may be an intermediate value and could
be scenario-dependent as we shall see in our experiments.
Of course, the optimal choice of threshold is also policy-
dependent, since
is defined differently for each policy.
When a request is issued by a client, in instantaneous
mode, a policy must either accept or reject requests using
the threshold computation. With the delayed approach, the
admission control policy may analyze the rate of data flow
for time units, where . A request is admitted if:
(1) a policy admits this request initially, and (2) the average
observed bandwidth during /.
. This approach is
advantageous because it recognizes the dynamic environmental
changes may either affect delivery of
in a positive or a
negative manner. A positive change is one where a server
becomes network reachable due to mobility. The reverse of
this is a negative change where a server is no longer network
reachable because of mobility. Obviously, as approaches
, the admission control is provided with more time to make
a decision, reducing the number of admitted requests that fail
(minimizing ). However a large may be quite undesirable
for several reasons. First, it might delay a user longer than
necessary prior to rejecting this user. Second, it might waste
network bandwidth by allocating resources to a request that
cannot be satisfied.
In the above scenario, a rejected request based on an initial
instantaneous decision might also be delayed time units
to enable the system to see if changes in the environment
during time units may facilitate admission of this request.
In this case, the client tries to service the rejected request
and observes the amount of available bandwidth during time units. Assume 0 units of data arrive during this period,
-0 size(X). After time units, it invokes the admission
control policy once again with size(X) )10 . If the request is
admitted then it proceeds to service this request. Otherwise, the
request is rejected. This approach suffers from the following
limitation. When bandwidth is scarce, a request that should be
rejected might compete for the available bandwidth with other
active displays, causing admitted requests to fail (reducing
). We do not evaluate this approach in our simulations.
Content replication and switched proximate server selection
are important for robust delivery of high-rate content (see
Appendix). In all our admission control policies, the client
always chooses the closest server from candidate servers.
When C2P2 ’s path to a server breaks at time , C2P2 might have downloaded 0 bytes of a clip
,
0 !$#
&%
. The bandwidth required to download the remainder
of a clip is
. If this bandwidth exceeds the wireless
network bandwidth, the request is discarded as a failed request.
Otherwise, C2P2 identifies another reachable server and
begins downloading the remainder of the clip from that server.
C. Admission Control Policies
This section details four policies for our proposed frame-
work:
1. Server Count (SC): The server count-based admission
control policy counts the number of C2P2 devices that are
within hops of C2P2 , denoted as
. Next, it identifies
and counts the number of devices that contain the referenced
clip X, denoted by . The
value for SC is the density of
these servers:
"
(2)
The main strength of this policy is its simplicity — it might
be implemented using the following probe based approach.
C2P2 sends a controlled flood within hops. When a node
receives a probe, it replies to C2P2 with its identity and
whether it contains clip
. Based on these replies, C2P2 evaluates
and . C2P2 may wait for time units prior
to rendering a decision. The value of might be dictated by
the duration of time required for 2 network transmissions:
transmissions to reach nodes that are hops always
and, another transmissions for their reply to arrive at
C2P2 . This policy suffers from several weaknesses. First, in
instantaneous mode, this policy ignores bandwidth limitations
and fails to detect scenarios where all severs might be
busy processing other requests. In delayed mode, this policy is
provided with some measure of available bandwidth. Second,
this policy ignores mobility where the servers might be
moving away from C2P2 . Once again, delayed mode of
admission control provides this policy with some measure of
this parameter.
2. Server Bandwidth (SB): This policy considers available
bandwidth of each server, requiring each probe to return the
available bandwidth of each server , # % , containing the
referenced clip. Note that the server of choice for C2P2 may
change over the duration of the clip download ( ). Since
each server is a potential candidate and has some probability
of being chosen over the
metric considers the average
available bandwidth across all the servers . SB defines
as:
# % (3)
where is the maximum network bandwidth available
to a C2P2 device. By using
in the denominator, this policy
considers the fraction of nodes that may act as servers. Similar
to SC, SB is also relatively simple to implement using a local
probe.
3. Path Bandwidth (PB): This policy is similar to that used
on traditional admission control and bandwidth reservation in
wired networks, and considers the bottleneck bandwidth on
the path between the client and each server within hops.
This policy only considers the average of all the instantaneous
bottleneck bandwidth along the shortest path from the client
to each server ( ). These estimates will rapidly become stale
as the nodes move and the paths between the C2P2 and the
servers change. Formally, this policy considers the available
bandwidth of most utilized node, BW( ), that lies in the
multi-hop path between each server and the client C2P2 :
# % "
(4)
To implement this policy, a probe accumulates the available
bandwidth of nodes on its path back from a server to C2P2 .
C2P2 identifies the intermediate node with least available
bandwidth as . This policy does not consider dynamic
evolution of the network where changes over time. In
static scenarios, PB improves upon SB because it considers
scenarios where the servers have sufficient bandwidth but the
intermediate hops are data-starved. However as we shall see,
this is not necessarily a good strategy in MANETs.
Note that in all the policies SC, SB and PB the probes
constitute the control traffic which is negligible in comparison
to the amount of data that is being exchanged between the
client and the servers.
4. Mobility Prediction (MAD): The mobility-based ad-
mission control (MAD) policy tries to leverage the mobility
pattern in the network by estimating the duration of time a
server will be in the radio range of the requesting client
C2P2 . This depends on both the direction and speed of and C2P2 , C2P2 sends out a probe message with a certain
time to live to obtain the list of proximate servers . Assuming
# ! "
% denotes the duration of time and C2P2 are
expected to be in radio range, for MAD:
# # ! "
% "
(5)
Here,
is the total number of nodes reachable from
the client via the probe. Note that both the numerator and
denominator are in units of time.
This policy only considers the time that a server is within
radio range of C2P2 . This is conservative because it does not
consider the time the servers are reachable via multiple hops.
Also this policy does not consider the available bandwidth
at each server. We considered a hybrid policy that combines
MAD with SB that takes into account both the mobility of the
servers and the available bandwidth at each server. However,
we have not presented the results for such a policy here.
III. PERFORMANCE EVALUATION
In this section, we quantify the performance trade-off asso-
ciated with the alternative policies. We conducted many exper-
iments with different parameter settings. In order to summarize
Value of a Value of a Value of a
Models rejected request, successfully admitted failed admitted
(M) request,
(M) request,
(M)
Economy 0 1 0
Standard 0 1 -1
Premium -0.5 1 -5
TABLE I
THREE UTILITY MODELS TO QUANTIFY QUALITY OF SERVICE WITH ALTERNATIVE POLICIES.
the lessons learnt, we developed a utility models to summarize
the QoS observed with each policy in one number. These
models are presented in Section III-A. Section III-B presents
our experimental environment that considers both stationary
and mobile servers. Section III-C demonstrates superiority of
MAD to an environment without admission control. Finally,
Section III-D demonstrates that MAD typically outperforms
SC, SB, and PB. This section also includes an in-depth
analysis of MAD with different thresholds and system loads.
A. Utility models for QoS
This section describes three utility models used to quantify
the QoS provided by the different admission control policies.
Recall that an admission control policy divides the total
number of requests into three groups: rejected requests,
requests that are admitted but fail (due to unsatisfied
requirements), and requests that are admitted and succeed
in providing the clip within the specified deadline. Different
users may value each of these differently, as per their utility
model. For the purpose of evaluating our experimental results
we consider three distinct utility models that are representative
of different levels of Quality of Service that can be provided
in a C2P2 ad hoc network. In each model
, we define the
joint utility as a weighted sum:
# % # % # % (6)
where a weight # % indicates the value of component in
model
.
Table I shows the weights for three QoS models employed
to evaluate the experimental results. The economy model does
not penalize a policy for either rejected or failed requests
and only rewards accepted requests. The Standard model is
indifferent to rejections, but penalizes failed admitted requests
as much as it rewards successfully served admitted requests.
The Premium model resembles a high QoS environment. It
penalizes rejected requests (albeit slightly), rewards admitted
requests that are satisfied, and highly penalizes admitted
requests that fail. The high penalty for a failed admitted request
reflects our intuition that users are likely to be annoyed if
their C2P2 device initiates a download, spends a considerable
amount of time (maybe time units) only to discover that it
cannot display a clip because an incomplete file is downloaded.
While these utility models can be applied on a per-user
basis and different QoS levels can be integrated within the
same system in practice, for the purposes of analyzing our
experimental result and comparing the different admission
control policies, we shall apply each QoS utility model to
the system as a whole for the duration of the experiment.
B. Experimental Setup
The results presented in this section are based on a new
simulator for C2P2 networks written using C programming
language
2
. The simulator models road stretches and cars that
navigate these stretches. A car might be configured with
a C2P2 device that provides a fixed amount of network
bandwidth. Each C2P2 device implements the DSR [8] routing
policy. However, we believe that a the choice of the routing
protocol does not impact the trends in the observed results. A
proactive protocol like DSDV would have shown similar re-
sults. We analyzed different scenarios consisting of a different
number of road-stretches, different number of C2P2-equipped
cars, mobility patterns, and request specifications.
Our basic experiment consists of thirteen bi-directional
road-stretches, numbered from 0 to 12, see Figure 1. Three
stationary servers (these could be parked vehicles, access
points, or base stations) are located on the following road-
stretches: 1, 6, and 11, numbered , , and , respectively.
The radio-range of each C2P2 spans 3 road-stretches: its
current road stretch and its two adjacent road stretches. Thus,
a C2P2 on either road-stretch 0 or 2 is in the radio range of
which is on road-stretch 1. Note that there exists four road
stretches that are dark because they are not 1-hop reachable
by a server. However, given sufficient number of clients and
transitive routing of packets across these clients, these dark
road stretches may lit-up as they become network reachable
to either one or two servers. We analyzed configurations
consisting of 13 possible client C2P2 nodes (in addition to
the 3 server nodes), with the simultaneous active displays
ranging from 1 to 13 in our experiments. Initially, all cars
are assigned to the road-stretches such that they are evenly
spaced across the road-stretches. For example, with 13 cars,
one car is assigned to each road-stretch. The initial direction
of each car is chosen randomly (i.e. to move left or right).
The speed of each car is fixed at 5 meters per second. Once a
car reaches the end of either road stretch 0 or 12, it switches
2
We initially considered using the ns-2 simulator that is widely used in
the MANET community for these simulation experiments, but found that
significant programming extensions to ns-2 would be required to implement
the kinds of online per-client dynamic admission-control policies we are
interested in evaluating. The simulator we have written is designed to
incorporate such policies with ease and provides for much faster simulations.
We plan to make the simulator available in the near future for use by others
investigating admission control.
0 1 2 3 4 5 6 7 8 9 10 11 12
s1 s0 s2
Fig. 1. Thirteen road stretches with 3 stationary servers.
directions and moves toward the opposite end. Within each
configuration, the cars that are requesting clips (corresponding
to the number of active displays) are chosen randomly and
they re-issue requests periodically every 60 seconds. A total
of 100 requests are issued in each experiment. This means
that with 10 active displays, each client issues 10 requests on
average. All three servers are assumed to contain a replica of
the clips being requested. Rejected requests do not consume
any network bandwidth and are terminated immediately. Once
a request is admitted to the system, it downloads a clip for 60
seconds. The maximum network bandwidth of a C2P2 device
is assumed to be 10 Mbps. The referenced clip is a media clip
with a bandwidth requirement of 340 Kbps. The display time
of each clip is fixed at 12 minutes. We require the clip (size
30MB) to be downloaded in 60 seconds, requiring a download
bandwidth requirement of 4 Mbps.
To analyze the impact of server mobility, we modify this
basic experimental setup to include scenarios where all servers
are also mobile. We also analyzed the impact of load in two
ways: first, by varying the number of simultaneous clients issu-
ing requests, termed active displays. Second, by changing the
download bandwidth requirement to 8 Mbps, corresponding to
a clip size 60MB and a media playback time of 24 minutes
at 340 Kbps rate.
C. A Case for Admission Control
We start with experimental results that justify the use of
intelligent admission control policies in an ad-hoc network of
C2P2 devices. Figure 2 shows utility of MAD (Instantaneous
mode of operation) with alternative models when compared
with an environment that does not utilize an admission control
policy. Figurer 3 shows the number of rejected, satisfied,
and unsatisfied requests for both MAD and the no admission
control case. (As we shall show in Section III-D, MAD
is generally a superior admission control policy.) Without
admission control, the simulator admits a request upon its
arrival independent of server availability. Figure 2 shows the
performance of MAD for two different thresholds: -20 and -
40. The x-axis of this figure shows the number of concurrent
clients that initiate the display of a clip, termed number of
active displays. This controls the load in the environment and
is increased from 1 to 13. The y-axis of this figure is the
utility of each model. We present results for all models shown
in Table I. Each presented data point is an average of results
obtained from 10 experiments utilizing different seeds. (The
random seed impacts the order and identity of clients issuing
requests.)
With 3 stationary servers and a maximum wireless band-
width of 10 Mbps per C2P2 device, our experimental environ-
ment supports a total bandwidth of 30 Mbps. All active clients
issue their requests at the same time. A total of 100 requests
are issued by all clients in this environment. This implies that
with two or fewer number of active displays, if there were
either no dark regions or unpredictability due to mobility,
without admission control, a total of 100 requests would be
satisfied. Figure 3.b shows that only one half of requests are
served successfully with 2 or fewer active displays. This is
because of mobility and dark regions that cause an active client
to starve for data, failing to download a clip
in time
units. This is reflected in the number of unsatisfied requests
admitted to the system, see Figure 3.c.
A primary observation from Figures 2 and 3 is that an
environment with no admission control is clearly inferior to
Instantaneous-MAD with ( =-40. With the Premium model,
MAD shows a much better performance compared to the other
policies. With the Standard utility model, MAD remains supe-
rior. With the Economy model, however, no admission control
outperforms MAD when ( =-20. This threshold renders MAD
too conservative, forcing it to admit too few requests. While
all these requests are processed successfully, see Figure 3.c
where =0 with ( =-20, MAD does reject requests that can
be processes successfully. With a more relaxed threshold (-40),
these requests are admitted into the system, enabling MAD to
outperform the environment with no-admission control when
using the Economy model.
Note that utility of MAD with ( =-20 is a constant positive
with the Economy, Standard, and Premium models. When ( =-
40, MAD’s utility drops as a function of load because the
number of unsatisfied requests increases with this threshold,
see Figure 3.c.
D. Detailed Experimental Results
Tables II, III, and IV show utility of each model in both
Instantaneous and Delayed mode. Each table summarizes
results for a specific model: Economy, Standard, and Premium.
A table reports the maximum observed utility for each policy
and the corresponding threshold at which this maximum is
realized. We employ a set notation to show threshold values
because the same maximum might have been observed for
several ( values.
Two different system loads are presented: First, a light
system load where 2 random clients issue requests at a time.
Even if both reference different clips from the same server,
the available network bandwidth (10 Mbps) accommodates
both requests, allocating a total of 8 Mbps. Second, a high
2 Active Displays 10 Active Displays
Instantaneous Delayed, =15 Sec Instantaneous Delayed, =15 Sec
Max Threshold Max Threshold Max Threshold Max Threshold
utility utility utility utility Admit-All 43.00 N/A 42.00 N/A 22.00 N/A 18.00 N/A
Reject-All 0 N/A 0 N/A 0 N/A 0 N/A
SC 45.00 43.00 30.00 22.00 SB 45.00 43.00 27.00 19.00 PB 45.00 43.00 27.00 19.00 MAD 45.00 43.00 29.00 22.00 TABLE II
A COMPARSION OF ALTERNATIVE ADMISSION CONTROL POLICIES WITH THE ECONOMY MODEL, = 4 MBPS, AND 16 C2P2 DEVICES: 3
STATIONARY SERVERS AND 13 MOBILE CANDIDATE CLIENTS.
2 Active Displays 10 Active Displays
Instantaneous Delayed, =15 Sec Instantaneous Delayed, =15 Sec
Max Threshold Max Threshold Max Threshold Max Threshold
utility utility utility utility Admit-All -14.00 N/A 32.00 N/A -56.00 N/A 13.00 N/A
Reject-All 0 N/A 0 N/A 0 N/A 0 N/A
SC 11.00 32.00 -101,-80,-60,-40 1.00 18.00 SB 13.00 32.00 -101,-80,-60,-40 1.00 13.00 -101,-80,-60 PB 12.00 32.00 -101,-80,-60,-40 1.00 13.00 -101,-80,-60 MAD 18.00 33.00 21.00 21.00 TABLE III
A COMPARSION OF ALTERNATIVE ADMISSION CONTROL POLICIES WITH THE STANDARD MODEL, = 4 MBPS, AND 16 C2P2 DEVICES: 3
STATIONARY SERVERS AND 13 MOBILE CANDIDATE CLIENTS.
2 Active Displays 10 Active Displays
Instantaneous Delayed, =15 Sec Instantaneous Delayed, =15 Sec
Max Threshold Max Threshold Max Threshold Max Threshold
utility utility utility utility Admit-All -242.00 N/A -12.80 N/A -368.00 N/A -14.70 N/A
Reject-All -10 N/A -10 N/A -10 N/A -10 N/A
SC -8.90 -8.90 -8.90 -5.40 SB -8.90 -8.90 -8.90 -8.90 PB -8.90 -8.90 -8.90 -8.90 MAD 9.80 9.80 9.30 9.30 TABLE IV
A COMPARSION OF ALTERNATIVE ADMISSION CONTROL POLICIES WITH THE PREMIUM MODEL, = 4 MBPS, AND 16 C2P2 DEVICES: 3
STATIONARY SERVERS AND 13 MOBILE CANDIDATE CLIENTS.
system load where 10 random clients issue requests simul-
taneously, requiring 40 Mbps and exhausting the available
bandwidth. (With 3 servers, a maximum bandwidth of 30
Mbps is supported by the system assuming an even distribution
of active displays across the clients.) With each load, a total
of 100 requests are issued to the system. The utility shown
with the Economy model, see Table II, is the number of
satisfied requests. A low system load results in a higher
number of satisfied requests with all policies when compared
with a high load. This value is maximized by increasing the
number of admitted requests, explaining why Instantaneous
mode outperforms the Delayed mode. With the Standard utility
model assigning a -1 to each failed admitted request, Delayed
mode of operation becomes superior to Instantaneous, see
Table III. This holds true across all policies. Note that SB and
PB are no different than Admit-all in this case. This is reflected
by the fact that a threshold of -101 (admits all requests) is one
of the thresholds that maximizes utility of SB and PB with the
Standard model.
With the Premium model assigning a -5 to each failed
admitted requests and a -0.1 to each rejected request, MAD
outperforms all other policies, see Table IV. Note that the
Delayed mode continues to improve an Admit-all policy.
Moreover, a policy that rejects all requests provides a com-
parable utility to SC, SB, and PB.
Tables II, III, and IV show the superiority of MAD with the
Premium and Standard models. With these models, a Delayed
mode of operation that samples available bandwidth for 15
seconds further enhances all policies (including the naive
Admit-all policy). In the rest of this section, we analyze the
characteristics of MAD as a function of different thresholds
1 2 3 4 5 6 7 8 9 10 11 12 13
−400
−350
−300
−250
−200
−150
−100
−50
0
50
Number of Active Displays
Premium Utitlity
MAD, Threshold = −20
MAD, Threshold = −40
No Admission Control
2.a) Premium
1 2 3 4 5 6 7 8 9 10 11 12 13
−70
−60
−50
−40
−30
−20
−10
0
10
20
30
Number of Active Displays
Standard Utitlity
MAD, Threshold = −20
MAD, Threshold = −40
No Admission Control
2.b) Standard
1 2 3 4 5 6 7 8 9 10 11 12 13
0
10
20
30
40
50
Number of Active Displays
Economy Utitlity
MAD, Threshold = −20
No Admission Control
MAD, Threshold = −40
2.c) Economy
Fig. 2. A comparison of Instantaneous-MAD with an environment that
employs no admission control. The environment consists of 16 C2P2 devices:
3 stationary servers and 13 mobile clients.
with mobile servers, Instantaneous versus Delayed mode of
operation, and different loads (both number of active displays
and bandwidth required during ).
Figure 4 shows the utility of MAD with alternative models
for both stationary and mobile servers as a function of alter-
native threshold ( ( ) settings. This figure shows the following.
First, the utility of all the models improves when servers are
mobile. Second, the utility of all models becomes a fixed
constant when ( + . Third, the observed trends when
comparing the Delayed mode with the Instantaneous mode in
a mobile server environment are similar to the discussion of
stationary servers. We now describe the first two observations
in turn. (See the discussion of Tables II, III, and IV for an
explanation of the third observation.) To explain the first, note
that there are no permanent dark regions with mobile servers.
1 2 3 4 5 6 7 8 9 10 11 12 13
0
10
20
30
40
50
60
70
80
90
100
Number of Active Displays
Number of Rejected Requests
MAD, Threshlod = −20
MAD, Threshlod = −40
No Admission Control
3.a) Rejected
1 2 3 4 5 6 7 8 9 10 11 12 13
0
10
20
30
40
50
60
70
80
90
100
Number of Active Displays
Number of Satisfied Requests
MAD, Threshold = −40
No Admission Control
MAD, Threshold = −20
3.b) Successful Admit
1 2 3 4 5 6 7 8 9 10 11 12 13
0
10
20
30
40
50
60
70
80
90
100
Number of Active Displays
Number of Unsatisfied Requests
No Admission Control
MAD, Threshold = −40
MAD, Threshold = −20
3.c) Failed Admit
Fig. 3. Rejected ( ), successfully admitted ( ), and failed admitted ( )
requests with different number of active displays and 16 C2P2 devices: 3
stationary servers, and 13 mobile clients.
With permanent dark regions (stationary servers), an actively
displaying client might travel in one of these regions where
no bandwidth is available for a sustained period of time. Upon
exiting such an area, this client attempts to download the
remaining portion of a clip at a higher rate. When this rate
exceeds 10 Mbps (maximum network bandwidth), this request
is discarded as a failed request because it is impossible to
download the remaining portion of the clip in the remaining
time. With transient dark regions as is the case with mobile
servers, the likelihood of such scenarios is minimized.
In Figure 4 (and Figure 5), MAD rejects all requests with
(-+
. This causes the Standard and Economy models to
converge to zero, while the Premium model converges to -
10 because each rejected request has a value of -0.1 (see
Table I) and there are 100 requests. This explains the second
−101 −80 −60 −40 −20 0 20 40 60 80 100
−60
−50
−40
−30
−20
−10
0
10
20
30
40
50
THRESHOLD
UTILITY
Premium Stationary
Standard Stationary
Economy Stationary
Premium Mobile
Standard Mobile
Economy Mobile
4.a) Instantaneous
−101 −80 −60 −40 −20 0 20 40 60 80 100
−60
−50
−40
−30
−20
−10
0
10
20
30
40
50
THRESHOLD
UTILITY
Premium Stationary
Standard Stationary
Economy Stationary
Premium Mobile
Standard Mobile
Economy Mobile
4.b) Delayed
Fig. 4. Analysis of Instantaneous and Delayed MAD with mobile and
stationary servers. The environment consists of 16 C2P2 devices: 3 servers,
and 13 mobile clients. With mobile, servers and clients move at the same
speed. With stationary, servers are parked in road stretches 1, 6, and 11.
observation.
Figure 5 compares Delayed MAD with different system
loads. While Figure 5.a shows two different number of simul-
taneous displays (2 and 10), Figure 5.b shows two different
bandwidth requirements during (4 and 8 Mbps). Both
figures show a reduced utility for all models with a higher
system load. Moreover, the threshold that maximizes the utility
of a model changes for a given system load. For example,
the maximum utility with the Premium model is observed
at a different threshold; at -60 with 8 Mbps and -20 with 4
Mbps, See Figure 5.b. This is also observed with the Standard
and Economy models. This again emphasizes that for a given
utility model, each policy has an ideal threshold ( which
provides the best performance.
IV. CONCLUSIONS AND FUTURE RESEARCH
DIRECTIONS
The primary contribution of this paper is a mobility based
admission control policy (MAD) for delivery of continuous
−101 −80 −60 −40 −20 0 20 40 60 80 100
−60
−50
−40
−30
−20
−10
0
10
20
30
40
50
THRESHOLD
UTILITY
Premium With 2 Active Displays
Standard With 2 Active Displays
Economy With 2 Active Displays
Premium With 10 Active Displays
Standard With 10 Active Displays
Economy With 10 Active Displays
5.a) 4 Mbps Media Type
−101 −80 −60 −40 −20 0 20 40 60 80 100
−60
−50
−40
−30
−20
−10
0
10
20
30
40
50
THRESHOLD
UTILITY
Premium With 4MbpS Media
Standard With 4MbpS Media
Economy With 4MbpS Media
Premium With 8MbpS Media
Standard With 8MbpS Media
Economy With 8MbpS Media
5.b) 2 Active Displays
Fig. 5. A comparison of Delayed MAD ( = 15 Sec) with different load
parameters and 16 C2P2 devices: 3 mobile servers, and 13 mobile clients.
media in an ad-hoc network of C2P2 devices. Our experimen-
tal results demonstrate the following. First, with continuous
media, an environment that employs admission control is
superior to one without admission control. Second, MAD
outperforms traditional admission control policies (for wired
networks) when the environment is penalized for failing to
service admitted requests. Third, with a delayed approach,
under heavy load conditions, the performance of all policies
including the naive admit-all approach improves considerably.
We are currently extending this study in several ways. First,
we are developing techniques that enable our framework to
determine the appropriate threshold value ( ( ) for use with
MAD. Based on the experimental results of Section III-
D, these techniques estimate system load and the expected
amount of work imposed by a display to determine ( . Second,
we are investigating the use of a deadline driven data delivery
technique as an extension of DSR to further maximize the
number of admitted requests that are satisfied with MAD. The
discussions of Section III assumed the data is delivered from a
server to a client at a rate of
. With the deadline driven
approach, each block is tagged with a lifetime. This enables a
server to push data to a client at a faster rate than
when
the ad-hoc network is idle. Moreover, deadlines might expedite
delivery of those blocks that are needed more urgently. Finally,
we are investigating the design and evaluation of a meta-policy
that combines SC, SB, and PB with MAD. This policy assigns
a weight to each component in order to consider both available
bandwidth and mobility.
V. ACKNOWLEDGMENTS
This research was supported in part by NSF grant IIS-
0307908, National Library of Medicine LM07061-01, and an
unrestricted cash gift from Microsoft Research.
REFERENCES
[1] B. Bellur, R. Ogier, and F. Templin. Topology broadcast based on
reverse-path forwarding. Internet-Draft Version 01, IETF, March 2001.
Work in progress.
[2] E. Cohen and S. Shenker. Replication Strategies in Unstructured Peer-
to-Peer Networks. In Proceedings of the ACM SIGCOMM, August 2002.
[3] S. Ghandeharizadeh and T. Helmi. An Evaluation of Alternative Contin-
uous Media Replication Techniques in Wireless Peer-to-Peer Networks.
In Third International ACM Workshop on Data Engineering for Wire-
less and Mobile Access (MobiDE, in conjunction with MobiCom’03),
September 2003.
[4] S. Ghandeharizadeh, T. Helmi, S. Kapadia, and B. Krishnamachari. A
Case for a Mobility based Admission Control Policy. In In Proceedings
of the 10th International Conference on Distributed Multimedia Systems,
September 2004.
[5] S. Ghandeharizadeh, B. Krishnamachari, and S. Song. Placement of
Continuous Media in Wireless Peer-to-Peer Networks. IEEE Transac-
tions on Multimedia, April 2004.
[6] Shahram Ghandeharizadeh, Shyam Kapadia, and Bhaskar Krishna-
machari. PA V AN: a policy framework for content availabilty in vehicular
ad-hoc networks. In VANET ’04: Proceedings of the first ACM workshop
on Vehicular ad hoc networks, pages 57–65. ACM Press, 2004.
[7] Shahram Ghandeharizadeh, Shyam Kapadia, and Bhaskar Krishna-
machari. Comparison of Replication Strategies for Content Availability
in C2P2 Networks. In To Appear in MDM ’05: Proceedings of the 6th
International Conference on Mobile Data Management. ACM Press,
2005.
[8] D. B Johnson and D. A Maltz. Dynamic source routing in ad hoc
wireless networks. In Imielinski and Korth, editors, Mobile Computing,
volume 353. Kluwer Academic Publishers, 1996.
[9] E. Knightly and N. Shroff. Admission control for statistical qos: Theory
and practice. IEEE Network, vol. 13, no. 2, pp. 20-29, 1999.
[10] Seoung-Bum Lee, Gahng-Seop Ahn, Xiaowei Zhang, and Andrew T.
Campbell. INSIGNIA: An IP-based quality of service framework for
mobile ad hoc networks. Journal of Parallel and Distributed Computing,
60(4):374–406, 2000.
[11] P. Mohapatra, J. Li, and C. Gui. Qos in mobile ad hoc networks. IEEE
Wireless Communications, pp. 44-52, June 2003.
[12] Elena Pagani and Gian Paolo Rossi. A framework for the admission con-
trol of qos multicast traffic in mobile ad hoc networks. In Proceedings
of the 4th ACM international workshop on Wireless mobile multimedia,
pages 2–11. ACM Press, 2001.
[13] C. Perkins, E. Royer, and S. Das. Ad hoc on demand distance vector
(aodv) routing. Internet-Draft Version 07, IETF, November 2000. Work
in progress.
[14] D. Perkins and H. Hughes. A survey on quality of service support
in wireless ad hoc networks. Journal of Wireless Communications
and Mobile Computing (WCMC), Speical Issue on Mobile Ad Hoc
Networking Research, Trends and Application, 2(5) pp. 503-513, 2002.
[15] J. D. Salehi, Z. Zhang, J. F. Kurose, and D. Towsley. Supporting
Stored Video: Reducing Rate Variability and End-to-End Resource
Requirements through Optimal Smoothing. In Proceedings of the 1996
ACM Sigmetrics Conference, May 1996.
[16] Prasun Sinha, Raghupathy Sivakumar, and Vaduvur Bharghavan.
CEDAR: a core-extraction distributed ad hoc routing algorithm. In
INFOCOM (1), pages 202–209, 1999.
[17] K. Wu and J. Harms. Qos support in mobile ad hoc networks. Crossing
Boundaries - an interdisciplinary journal, Vol 1, No 1, 2001.
[18] H. Xiao, W. Seah, A. Lo, and K. Chua. A flexible quality of service
model for mobile ad-hoc networks. In IEEE VTC, 2002.
[19] C. Zhu and M. Corson. Qos routing for mobile ad hoc networks. In
INFOCOM, June 2001.
APPENDIX
Before we move on to address the challenges of admission
control for multiple simultaneous displays, we first need to
understand the basic data placement and delivery scheduling
strategies that are needed to provide satisfactory performance
even in a single display scenario. This section focuses on the
display of a single clip in a C2P2 network. It shows that
content replication and block-level switched retrieval of data
from proximate servers provides low-latency delivery of high
data-rate content.
The basic principle behind replication and block-switched
retrieval is as follows.
The clip is replicated on servers, and divided into iden-
tifiable segments known as blocks (of size ). The client is
assumed to be able to determine the identity and location of
these servers through a resource discovery protocol. (See [2]
for a description of techniques to identify nodes with relevant
data in an unaddressable wired network.) When the retrieval
of a clip is initiated, the client requests the first block from
the nearest server. As each successive block is retrieved, this
process is repeated, with the client always requesting the
information from the nearest server. There are therefore two
key parameters of interest: the block size , and the number
of servers .
Intuitively, as the number of servers is increased and the
block size is decreased, there is a greater diversity of choices
(higher likelihood that at least one server is always near the
client in the network) and greater adaptation to mobility (due
to increased frequency of switching), enabling an improvement
in the quality of continuous media retrieval. This requires
greater overhead as the frequency of switching increases.
However, this approach depends on the block size and speed of
the cars which in turn determines the frequency of switching.
Note that if the mobility characteristics of the environment lead
to frequent changes in the proximate server when a decision is
to be made per block then the benefits of choosing the closest
server may become lower the cost incurred due to the added
overhead of extremely frequent switches. In other cases, when
the proximate server does not change on a per block basis the
benefits obtained are substantial and exceed the overhead in
the server selection process. (Typically much lesser overhead
is incurred in probing to find out if the current server is the
”closest” than switching to a new server, and if this switches
are not that frequent then compared to the duration of data
transfer this overhead also becomes negligible.) We validate
this through simulation results but have ignored the switching
overhead.
Figure 6 shows sample runs of data delivery using block-
switched retrieval tested via ns-2 simulation of a 20-node
MANET with random waypoint mobility.
0 50 100 150 200 250
0
2
4
6
8
10
12
14
x 10
4
(a) Block size 1MB
Time (s)
Packet Sequence Number
server switches
Packet
Arrival Time
Playback
Time
path failures
Startup
Latency
6.a block size = 1 MB
0 50 100 150 200
0
2
4
6
8
10
12
14
x 10
4
(b) Block size 1KB
Packet Sequence Number
Packet
Arrival Time
server switches
Playback
Time
Time (s)
path failure
Startup
Latency
6.b block size = 1 KB
Fig. 6. Simulation results of packet arrival times for a sample
run involving block-based staggered non-redundant retrieval from 8
servers for two different block sizes; maximum node velocity 30
meters/sec; delivery rate 2 Mbps and playback rate 4 Mbps.
Other simulation parameters are: area is 200x200m, link-
bandwidth is 10 Mega bits per second (Mbps), clip-display
time is 120 Seconds, and the underlying routing protocol is
Dynamic Source Routing (DSR) [8]. The vertical lines in
Figure 6.a represent times when there is a server switch.
There are two sloping curves. The top one shows the packet
arrival times, with some small gaps that correspond to path
breaks due to mobility. The second curve is a line which
represents the rate of data playback as a function of time
3
.
For illustration, we have chosen a playback rate of 4 Mbps
in this figure, while the delivery rate is 2 Mbps. The point
where the playback time curve intersects the x-axis represents
3
We focus on clips with a Constant Bit Rate (CBR) display requirement.
Hiccup-free delivery of Variable Bit Rate (VBR) encoded clips can be
conceptualized as a sequence of CBR delivery schedules [15].
0 2 4 6 8 10 12
0
5
10
15
20
25
30
35
40
45
50
No of servers
Startup latency (seconds)
1KB
1MB
7.a Startup latency
0 2 4 6 8 10 12
1.2
1.25
1.3
1.35
1.4
1.45
1.5
1.55
1.6
x 10
5
No of servers
Total no of packet transmissions
1MB
1KB
7.b Total transmitted packets
Fig. 7. Startup latency and total bandwidth usage (in terms of
packets hops) for hiccup-free delivery as a function of the number of
servers for low and high block sizes (max. velocity 30 m/s, delivery
and playback rate 4 Mbps) for a single-client scenario.
the minimum startup latency for hiccup-free display. When
there are greater numbers of path failures, the startup latency
deteriorates. When a smaller block size (such as 1KB) is used,
the client refreshes its server connection frequently and there
are fewer intra-block path failures in this scenario and as a
result the corresponding startup latency can be reduced
4
.
Figure 7 shows how the startup latency and the total in-
network bandwidth usage varies as a function of the number
of servers for different block sizes.
The first observation is that the startup latency is reduced
significantly as the number of servers (i.e. the degree of
4
Naturally, choosing a smaller block-size also increases the number of
route setup messages required by the underlying DSR protocol, but this
additional overhead is relatively insignificant compared to the large bandwidth
application data.
replication) is increased. We can see reductions of more than
20 times in some cases, by increasing the number of servers
from 1 to 12. Figure 7 also shows that the startup latency can
be reduced by up to 12 times with a 1 KB block size when
compared with a 1 MB block size. These results show an order
of magnitude QoS improvements in the startup latency metric
and significant reduction in bandwidth usage with sufficient
replication and block-switched retrievals.
Having addressed the strategies needed for robust single-
display downloads, we now turn to the challenges inherent
in the download of multiple simultaneous displays. Given the
limited bandwidth of wireless channels, the high data-rates
for continuous media delivery, and the dynamics of the C2P2
MANET topology, intelligent admission control is a must.
Abstract (if available)
Linked assets
Computer Science Technical Report Archive
Conceptually similar
PDF
USC Computer Science Technical Reports, no. 864 (2005)
PDF
USC Computer Science Technical Reports, no. 862 (2005)
PDF
USC Computer Science Technical Reports, no. 865 (2005)
PDF
USC Computer Science Technical Reports, no. 791 (2003)
PDF
USC Computer Science Technical Reports, no. 778 (2002)
PDF
USC Computer Science Technical Reports, no. 628 (1996)
PDF
USC Computer Science Technical Reports, no. 587 (1994)
PDF
USC Computer Science Technical Reports, no. 615 (1995)
PDF
USC Computer Science Technical Reports, no. 602 (1995)
PDF
USC Computer Science Technical Reports, no. 627 (1996)
PDF
USC Computer Science Technical Reports, no. 666 (1998)
PDF
USC Computer Science Technical Reports, no. 634 (1996)
PDF
USC Computer Science Technical Reports, no. 830 (2004)
PDF
USC Computer Science Technical Reports, no. 685 (1998)
PDF
USC Computer Science Technical Reports, no. 618 (1995)
PDF
USC Computer Science Technical Reports, no. 619 (1995)
PDF
USC Computer Science Technical Reports, no. 598 (1994)
PDF
USC Computer Science Technical Reports, no. 748 (2001)
PDF
USC Computer Science Technical Reports, no. 592 (1994)
PDF
USC Computer Science Technical Reports, no. 629 (1996)
Description
Shahram Ghandeharizadeh, Tooraj Helmi, Shyam Kapadia, Bhaskar Krishnamachari. "Admission control and QoS for continuous media displays in MANETs." Computer Science Technical Reports (Los Angeles, California, USA: University of Southern California. Department of Computer Science) no. 863 (2005).
Asset Metadata
Creator
Ghandeharizadeh, Shahram
(author),
Helmi, Tooraj
(author),
Kapadia, Shyam
(author),
Krishnamachari, Bhaskar
(author)
Core Title
USC Computer Science Technical Reports, no. 863 (2005)
Alternative Title
Admission control and QoS for continuous media displays in MANETs (
title
)
Publisher
Department of Computer Science,USC Viterbi School of Engineering, University of Southern California, 3650 McClintock Avenue, Los Angeles, California, 90089, USA
(publisher)
Tag
OAI-PMH Harvest
Format
12 pages
(extent),
technical reports
(aat)
Language
English
Unique identifier
UC16269693
Identifier
05-863 Admission Control and QoS for Continuous Media Displays in MANETs (filename)
Legacy Identifier
usc-cstr-05-863
Format
12 pages (extent),technical reports (aat)
Rights
Department of Computer Science (University of Southern California) and the author(s).
Internet Media Type
application/pdf
Copyright
In copyright - Non-commercial use permitted (https://rightsstatements.org/vocab/InC-NC/1.0/
Source
20180426-rozan-cstechreports-shoaf
(batch),
Computer Science Technical Report Archive
(collection),
University of Southern California. Department of Computer Science. Technical Reports
(series)
Access Conditions
The author(s) retain rights to their work according to U.S. copyright law. Electronic access is being provided by the USC Libraries, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.
Repository Name
USC Viterbi School of Engineering Department of Computer Science
Repository Location
Department of Computer Science. USC Viterbi School of Engineering. Los Angeles\, CA\, 90089
Repository Email
csdept@usc.edu
Inherited Values
Title
Computer Science Technical Report Archive
Coverage Temporal
1991/2017
Repository Email
csdept@usc.edu
Repository Name
USC Viterbi School of Engineering Department of Computer Science
Repository Location
Department of Computer Science. USC Viterbi School of Engineering. Los Angeles\, CA\, 90089
Publisher
Department of Computer Science,USC Viterbi School of Engineering, University of Southern California, 3650 McClintock Avenue, Los Angeles, California, 90089, USA
(publisher)
Copyright
In copyright - Non-commercial use permitted (https://rightsstatements.org/vocab/InC-NC/1.0/