Piecewise Framework for End-to-End Network
Awareness Service in Heterogeneous Data Networks
Liang Cheng and Ivan Marsic
Department of Electrical and Computer Engineering and the CAIP Center
Rutgers, The State University of New Jersey
94 Brett Rd., Piscataway, NJ 08854-8058
{chengl,marsic}@caip.rutgers.edu
Abstract — Network awareness is the capability of network applications to be aware of
the characteristics of the networking environment. We present a piecewise framework for
end-to-end network awareness service (NAS) that allows network applications on mobile
devices to acquire end-to-end performance characteristics of heterogeneous data networks.
Example piecewise techniques for awareness of available bandwidth, round-trip time, and
packet loss rate are developed for the framework. Analytical results show that the
framework can achieve substantial performance enhancement over traditional unitary
frameworks in terms of wireless-bandwidth and battery-energy consumption. At the same
time, the piecewise design does not reduce awareness agility. The framework is lightweight
and scalable, and network applications can flexibly employ the NAS to adapt to dynamic
environment in both proactive and reactive manners. Experiments with a prototype
network-aware video-on-demand application exemplify the deployment of the NAS and
demonstrate its near optimum performance.
Index Terms — Network awareness, heterogeneous data networks, adaptive applications,
available bandwidth, round-trip time.
I.
INTRODUCTION
Heterogeneous data networks consisting of both wired and wireless data networks are
becoming ubiquitous with the proliferation of mobile devices. Generally, in a heterogeneous data
network a mobile host with a wireless network interface card (NIC) or a modem may
communicate with other hosts in the Internet via wireless link(s). A generic architecture of
heterogeneous data networks can be abstracted as in Fig. 1. It has two main parts: the mobile
wireless part connected to the fixed wired part via wireless interfaces. The wireless part may
include mobile ad hoc networks (MANET) involving multiple-hop wireless communications [1].
A dedicated proxy or gateway as a service point is generally used for three reasons [2]: (i) the
wireless connection quality is too poor to sustain a typical client-server application, (ii) the
amount of data transferred to the mobile host must be filtered because of the orders of magnitude
difference in bandwidth between the wired and wireless connections, and (iii) many portable
computing devices have display and processing limitations that must be addressed by the proxy
before sending the filtered response to the mobile host. Additionally, a proxy/gateway is
necessary in case of protocol differences between the wired and wireless parts, and this will
likely remain so in the next-generation wide-band wireless networks.
Wireless links in heterogeneous data networks generally exhibit the following characteristics:

Low bandwidth: The bandwidth of wireless connections ranges from several Kbps, e.g., in
GSM and Palm.Net systems, to several hundred Kbps, e.g., in wireless LANs. More
Wireless/Mobile
(MANET included)
Fixed,
Wired
Base
Station
Proxy
or
Gatew ay
Mobile
Device
Internet
Fixed
Devices
Fig. 1. The generic architecture of heterogeneous data networks
2
precisely, wireless Internet access via GSM at present has limited bandwidth from 9.6 Kbps
to 115 Kbps using GPRS (General Packet Radio Service), and a CDPD channel of raw
capacity 19.2 Kbps may be shared by up to 30 users [2].

Large transmission latency: Due to the low bandwidth and media-access-control (MAC)
protocol overhead, the packet-transmission round-trip time (RTT) is large. For example, the
round-trip latency on a GSM link is on the order of 410 ms [3].

Frequent and volatile bandwidth changing: Because of dynamic channel sharing and fading,
the available bandwidth of a wireless link changes frequently and abruptly. Due to the
movement of the mobile user, the connection may experience cell handoff and even blackout.
Additional fundamental constraints that need to be addressed are limitations of mobile devices,
including low processing power, small memory, small display, and limited battery lifetime. If
Moore’s law continues to hold, mobile devices in the near future will probably not be
constrained by the low processing power and the memory size. However, the small displays and
limited battery lifetime are long-term factors that should be considered.
A.
Adaptive Applications
Considering the above limitations, network applications need to be adaptive to the network
environment to make their performance acceptable or to be enhanced. A stock-trading scenario
below illustrates these points.
While sitting in a taxi, a day trader connects his laptop to the Internet through a GSM network.
He tracks his stock portfolio on an e-investment website. The intra-day charts of the stocks and
market indices give him information for trading decisions. Assume that when he wants to get the
chart of a stock quote, the taxi enters a handoff region. A problem occurs since the decreased
bandwidth of the GSM link cannot support transmission of the chart image. After the connection
switches to a new cell, he may have missed a trading opportunity even if the newly available
bandwidth can again support the image data traffic. A more serious problem may occur if he
3
places a stock trading order while the taxi crosses a tunnel and the connection experiences a
blackout. The connection with the website is broken and the order gets lost! A solution to the
above problems is to make the application adaptive to the heterogeneous data networks. If the
link cannot afford the image transmission, then it can send the data in a table format or send just
the key indices that reflect the dynamics of the stock curve. A solution for the broken connection
due to a blackout may be communication protocol stack adaptation to the wireless network [2].
B.
Network Awareness
For an application on a mobile device to achieve behavior adaptation, it first needs to be aware
of the characteristics of the heterogeneous data network. Awareness is the basis of adaptation.
For example, a rate-adaptive application needs to be aware of the available bandwidth along the
communication path. In addition to adaptation, resource reservation is another well-known
technique for enhancing the end-to-end performance of network applications. Awareness is
required even here, since the resource policy-making and admission-control are based on the
awareness of the network characteristics. In this paper, network awareness is the capability of
network applications on mobile devices to be aware of the end-to-end performance
characteristics of heterogeneous data networks, such as available bandwidth and round-trip time.
Awareness about dynamic context, such as the geographic location or proximity, may also be of
interest, but it is not considered here.
An important consideration is that the cost of acquiring network awareness does not exceed its
utility. If not obtained efficiently, awareness may impact the performance of heterogeneous data
networks, especially the mobile devices, due to resource consumption overheads, including CPU,
wireless bandwidth, and battery energy. An obvious example is the out-of-band method of
measuring bottleneck bandwidth by saturating the communication path with a continuous stream
of probe packets [4]. The cost/utility tradeoff is still an open research problem.
4
In this paper, we present a piecewise framework that partitions the end-to-end network
awareness measurements into the wired and wireless components. This approach is a natural
outcome of the observations about the difference between the characteristics and the dynamics of
wired and wireless networks. The adaptive network applications on mobile devices use the
network awareness service (NAS) to efficiently acquire end-to-end network conditions. NAS is
designed as lightweight and scalable in terms of resource consumption and deployment
overhead, so that it can be applied to small mobile devices.
Based on the separation principle [5], which states that media transfer, control, and
management are functionally distinct architectural activities, this research focuses on the
piecewise NAS framework rather than adaptation schemes for network applications. However,
we show the feasibility and flexibility of employing the framework using adaptive applications
in both proactive and reactive modes. Most of the existing adaptive applications adjust their
behaviors according to the observed packet loss. They are called reactive because no action is
taken unless the received quality of service (QoS) deteriorates. By taking advantage of the
piecewise framework presented in this paper, the applications can also can effectively adapt to
the dynamic environment in a proactive manner before the QoS deteriorates.
The paper is organized as follows. Section II describes the piecewise framework and relevant
techniques for end-to-end network awareness service. Performance analysis is presented in
Section III. A video-on-demand experiment in Section IV demonstrates the feasibility and
flexibility of employing the NAS framework. Section V discusses related work. Finally, Section
VI concludes the paper.
5
II.
A.
NETWORK AWARENESS SERVICE (NAS)
Piecewise NAS Framework
Fig. 2 illustrates the piecewise framework for network awareness. The framework acquires,
measures, integrates, and distributes the parameters that reflect the characteristics of
heterogeneous data networks.
There are four main components: the NAS daemon, the NAS data repository, the NAS
manager, and the NAS agent. The SNMP (Simple Network Management Protocol) agent and
manager in Fig. 2 are optional in the piecewise framework.

The NAS daemons implement different end-to-end network awareness techniques, e.g.,
available bandwidth measurement. They are optional components at the mobile device,
subject to the resource limitations.

The NAS data repository holds all data of interest that reflect network conditions. Data are
acquired by NAS daemons and other service agents (e.g., SNMP agents) at the proxy or
gateway, collected from the NAS agents at mobile devices, integrated via an inference
engine, and distributed by the NAS manager. The repository has a hierarchical architecture
conforming to the SNMP MIB standard for simplicity, extensibility, and compatibility.

The NAS manager manages the NAS data repository. It collects, integrates, and distributes
the NAS data. The manager may communicate with other service agents, such as the SNMP
Mobile
Client
Proxy or Gateway
SNMP
Manager
Applications
NAS Agent
NAS Daemon
Base
Station
SNMP
Agent
Internet
NAS
Manager
Inference
Engine
NAS Daemon
NAS Data
Repository
Fig. 2. Piecewise NAS framework
6
agent or SNMP manager, to acquire additional information that the existing NAS daemons
do not provide. Its inference engine integrates data collected from the NAS daemons and the
SNMP manager or agent, if available. Once the manager is activated at the proxy or gateway,
it advertises its existence through a well-known multicast channel defined in the NAS
implementation. When the manger receives a query message from a NAS agent, it replies
with the corresponding NAS data.

The NAS agent provides NAS data to applications. It sends registration messages to the NAS
manager through the well-known multicast channel in activation. Applications may subscribe
to the NAS agent, or send an explicit query message to it. If the information required by the
query message cannot be obtained by the local NAS daemons, the NAS agent will send a
query to the NAS manager to perform the awareness task and return the reply.
The NAS query/reply procedure can occur at the application’s startup phase and/or at the
runtime phase. At startup, the applications deploy NAS in the proactive mode, while at runtime
they may use NAS in both proactive and reactive modes.
B.
Service/Data Distribution Model
The agility of the adaptive applications depends on the timely distribution of the NAS data. It
is therefore important to distribute data effectively between different entities in the NAS
framework. We use two distribution models: a push and a pull model.
In the push model, the data provider takes the initiative by “pushing” data to the consumer,
while in the pull model the consumer “pulls” data from the supplier. In the NAS framework,
applications subscribe to the NAS agent, thus implementing the push model, while the pull
model is implemented through the applications’ querying the NAS agent (and the NAS
manager). There are pros and cons for using either model. The push model requires runtime
support for data delivery while the pull model needs only a well-defined pull interface. However,
only a hookup with callback is needed to use the push model, while the time and frequency of
7
polling must be specified in the pull model. In fact, these two models can be considered as the
tradeoff between simplicity of implementation (pull model) and simplicity of use (push model).
In the mobile wireless environment, it may be difficult to know when to push and when to pull.
There are security/privacy concerns about the push model because it sometimes reveals the
current status of the mobile user. However, the push model can provide timely updates for the
information. We chose a combination data distribution model for the NAS framework; both
models are used to distribute the NAS data.
Techniques for measuring network parameters are essential for the NAS. Three methods for
end-to-end NAS are presented below: (i) available bandwidth estimation by processing the interpacket time, (ii) round-trip time measurement of a data path, and (iii) awareness of packet loss
type in heterogeneous data networks.
C.
Piecewise Techniques for End-to-End NAS
Unlike a traditional, unitary awareness framework in which measurement tasks are solely
performed at mobile hosts, techniques in the piecewise NAS framework acquire end-to-end
network conditions in a piecewise way. Basically the network awareness is divided into two
parts: awareness of the mobile wireless part and the fixed wired part, as illustrated in Fig. 1. The
NAS manager or NAS agent integrates the intermediate results about these two parts and
provides the result to the application. Here we present three example piecewise NAS techniques.
Same spacing is preserved on higher speed links
= Time to process P bytes packet
Minimum packet spacing
at bottleneck link
P
Packets
Link speed estimation = P/ 
Fig. 3. Available bandwidth awareness
8
1) Available Bandwidth
The end-to-end available bandwidth is a frequently used parameter for adaptive applications.
Generally, the end-to-end available bandwidth is the bottleneck bandwidth along the
communication data path.
The essential idea of an awareness method for available bandwidth [4] uses inter-packet time
to estimate the characteristics of the bottleneck link. If two packets (e.g., ICMP probe packets)
travel together so that they are queued as a pair at the bottleneck link with no packet intervening
between them, then their inter-packet spacing is proportional to the processing time required for
the bottleneck link to transmit the second packet of the pair (Fig. 3).
We have implemented the inter-packet time method in the NAS framework. In our
implementation, the probing packets can be payload packets, and explicit ICMP probe packets as
well. When an application queries the NAS agent about the available bandwidth, the agent
delegates the NAS manager to perform this awareness task by sending a query with the address
of the destination host. The manager divides the task into three steps. First, it activates the NAS
daemon at the proxy/gateway responsible for the awareness of available bandwidth to measure
the available bandwidth between the mobile host and the proxy, say, b1. Next, it gets the
available bandwidth between the proxy and the remote Internet host/server from the
measurement by the NAS daemon, say, b2. Finally the manager computes the minimum of b1 and
b2 and replies back to the NAS agent with the result. Note that the two steps can be done in
parallel. Finally, the NAS agent replies with the result to the application.
2) Round-Trip Time and Packet Loss Rate
Similar to the awareness of the available bandwidth, the NAS agent delegates this task to the
NAS manager. The RTTs are measured between the mobile host and the proxy/gateway (rtt1)
and between the proxy/gateway and the remote Internet host/server (rtt2). The manager replies
with the sum of rtt1 and rtt2 back to the NAS agent, which finally replies to the application.
9
Similarly, the packet loss rates are measured between the mobile host and the proxy/gateway
(l1) and between the proxy/gateway and the remote Internet host/server (l2). The manager replies
the result, 1(1l1)(1l2), back to the NAS agent, which forwards the reply to the application.
The NAS framework is piecewise not only because the NAS components are distributed
between the mobile device and the proxy/gateway, but also since the awareness tasks are
distributed from the NAS agent at the mobile host to the NAS manager at the proxy/ gateway.
The next section shows that the wireless bandwidth and computing resources are preserved.
III.
A.
PERFORMANCE ANALYSIS
Performance Analysis Model
The performance analysis model for heterogeneous data networks is shown in Fig. 4. Although
the wireless communication in MANET may be multi-hop, the data path between a mobile
device and the proxy/gateway can still be abstracted as a one-hop wireless link in terms of the
end-to-end packet-loss characteristics. Similar to Fig. 1, there are two parts: the mobile wireless
part and the fixed wired part. The end-to-end path is between the mobile host and the remote
host. Either of them could be the sender or the receiver. Assume that the path between the sender
and the receiver is symmetric, meaning that the communication path characteristics are the same
in both directions. In practice this is not true, though the difference is usually small.
We denote the loss rate of the mobile wireless part as r1, which is the probability of a packet
being incorrectly transmitted/received between the mobile device and the proxy/gateway. The
loss rate of the fixed wired part is r2, which is the probability of a packet being incorrectly
Mobile
Device
r1
Proxy/
Gateway
r2
Fig. 4. Performance analysis model
10
Remote
Host
transmitted/received between the proxy/gateway and the remote host. In practice, r1 and r2
change dynamically because of the Internet and wireless channel dynamics, user mobility, cellhandovers, and possible infrastructure changes in MANET. However, for this performance
analysis, without loss of generality, we assume that the heterogeneous data networks are in a
steady state so that r1 and r2 remain the same over a certain period of time, say, 20 RTTs. The
rationale is that if the piecewise framework performs better than the unitary one for each short
period of time, then the piecewise framework will perform better overall than the unitary one.
The probability of a packet being successfully transmitted/received between the mobile device
and the remote host is:
p = (1–r1)(1–r2)
(1)
We will use the awareness of the available bandwidth as an analysis example below. A similar
analysis applies to the awareness of round-trip time.
B.
Awareness of Available Bandwidth
According to the description of the method for measuring the available bandwidth, only when
both packets in a back-to-back packet-pair travel a round trip successfully can a correct value of
the available bandwidth be computed. For simplicity we assume that the query and the reply
packets between the NAS agent and NAS manager have the same size as that of the probe
packet.
1) Wireless Bandwidth Consumption

Unitary Framework
In a unitary framework, the available bandwidth is determined on the mobile device. For
example, in a proactive mode, the mobile device sends out the back-to-back probe packet-pair,
receives the bounced packet pair from the remote host, and computes the available bandwidth.
Assume that the packet loss rates r1 and r2 are packet-independent. Based on (1), the probability
 of obtaining a correct measurement if only one packet-pair is used can be expressed as:
11
 = p4
(2)
If multiple, say m, packet-pairs are used, then the probability of obtaining a correct
measurement increases, and is expressed as:
 = 1 – (1 – p4)m
(3)
Therefore to achieve probability approximately equal to 1, m should be as large as possible. To
achieve the success probability of , where p2<<1, at least Nt packet pairs should be sent, where

N t  log
4 (1   )
 (1 p )

because  < 1 – (1 – p4)m, if p<1, where
(4)
is the ceiling operator. Since the consumption of
wireless bandwidth is proportional to the number of packet-pairs, we use Nt as the index.

Piecewise Framework
In our piecewise framework, the available bandwidth measurement in the fixed wired part does
not consume wireless bandwidth. Only the awareness of the available bandwidth in the mobile
wireless part contributes to wireless bandwidth consumption.
Based on the previous analysis, to achieve the same success probability , at least Np packet
pairs should be used:

N p  log
4 ) (1   )  0.5
(
1

q


(5)
where q = 1 – r1. The 0.5 packet-pair comes from a query packet plus a reply packet between the
NAS manager and the NAS agent.
Since p<q, it follows that Nt>Np. Therefore the performance enhancement in terms of wireless
bandwidth consumption is (Nt–Np)/Nt. This can be substantial in the presence of unreliable
communication as is the case in the Internet. For example, the dominant packet loss rate in the
U.S. part of the Internet is around 10% [6]. The performance enhancement for realistic loss rates
is shown in Fig. 5. Assuming r1=r2=0.1 and =99.99%, the performance enhancement is 44.1%.
12
Fig. 5. Performance enhancement with =99.99%
An additional benefit of the performance enhancement is battery energy saving, since the radio
device draws a much greater amount of power during transmission [7].
2) Awareness Agility
Note that the piecewise NAS framework involves packets exchange between the NAS agent
and the NAS manager, and the communication between the NAS agent and the application and
between the NAS manager and the NAS daemon as well. Therefore it is necessary to determine
whether the piecewise framework impairs the agility of network awareness, i.e., the timely
measurement and, ultimately, the application’s response.
Assume that the RTT between the proxy/gateway and the remote host is t1, and the RTT
between the mobile host and the proxy/gateway is t2. Also, denote the communication time
between the NAS agent and application as t3 and between the NAS manager and the NAS
daemon as t4. We ignore the computing time. Finally, suppose that the interval between the
packet-pairs is so small that the duration from sending out the first packet-pair to the last packetpair in a measurement can be neglected.

Unitary Framework
The time tt to achieve successful measurement is (t1+t2).
13

Piecewise Framework
The time tp to achieve successful measurement is expressed as
tp = max(t1,t2) + t3 + t4 + t2
(6)
because the measurement between the mobile device and the proxy/gateway and the
measurement between the proxy/gateway and the remote host are conducted in parallel. Since the
RTT between different hosts is several orders of magnitude greater than the inter-process
communication time between different applications on the same host, t3 and t4 can be neglected.
Therefore, assuming that the transmission times for Nt or Np packet-pairs are negligible, the
time difference between the piecewise framework and the unitary framework is:
max(t1,t2) – t1
(7)
which is zero for t1t2, or t2–t1 for t1<t2. In the latter case the piecewise framework impairs
application’s agility. However, since the RTTs are almost always in the range of milliseconds,
the impact is negligible.
Similar analysis can be applied to the awareness of round-trip time and packet loss rate.
C.
Scalability of the NAS Framework
1) Scaling Up to a Multi-Tasking OS
As higher performance and energy-efficient processors and memory chips emerge, more and
more mobile devices will support multitasking operating systems. Thus multiple network
applications can be simultaneously active at the device. Suppose that, in a unitary framework, M
applications are adaptive to the available bandwidth and perform the measurements
independently. To achieve success probability , the total wireless bandwidth consumption will
be MNt, if there is no correlation among their individual measurement successes.
In contrast, to achieve the same success probability  in the piecewise NAS framework, the
total wireless bandwidth consumed will be only Np. An application sends the query to the NAS
agent that delegates the task to the NAS manager. The manager obtains the value of the available
14
bandwidth, b, from the NAS daemon at the proxy/gateway, and feeds it back to the agent.
Finally, the agent gets the traffic-multiplexing ratio, , of that application from the traffic
scheduler, and replies with the final result, b, to the application. Therefore the other
applications can share the measurement result b multiplied by their corresponding trafficmultiplexing ratios.
2) Scaling Down to a Lightweight OS
The minimum footprint of the NAS framework at the mobile host is the NAS agent, which is
the service interface communicating with the NAS manager. The NAS daemons can all run at the
NAS manager’s host. The agent is a simple proxy for invoking NAS manager’s services, thus
making the framework downward scalable.
IV.
A.
EXPERIMENTS
Experimental Testbed
The topology of the experimental testbed is shown in Fig. 6. It can be viewed as consisting of
Router
Sw itch
Serial
Link
Sw itch
Proxy
Router
Wearable
Computer
Wireless LAN
Base Station
Sw itch
Video
Workstation
Base Station
Workstation
Handheld PC
Fig. 6. Topology of the testbed
15
Internet
two parts, a wireless LAN and the Internet. A Proxim wireless LAN [8] is used in the wirelessnetwork part. Wireless laptops and wearable computers communicate with other hosts via base
stations in the slave-master mode. Roaming and mobile IP are supported. The wireless link is the
bottleneck unless otherwise stated.
B.
Video-on-Demand Application
In this experiment, the performance of an adaptive video-on-demand (VoD) application
deploying NAS framework is studied. The VoD system is based on the client/server paradigm.
The client, a mobile device, connects to the video server and requests video streams. Our
hypothesis is that better performance can be achieved by deploying NAS.
1) Application Implementation
During the application configuration, the user can set the preferred video frame size and rate,
and the range of compression ratios of the video stream for different scenarios. The preferred rate
is computed accordingly. For example, a QCIF format with 30 frames per second and
compression ratio around 200:1 in PB mode using H.263 coder for a wireless channel will
generate about 125 Kbps data traffic. Alternatively, the preferred rates can also be set using
predefined values.
At the startup phase when a client establishes a connection, it acquires the available bandwidth
from the NAS agent. After comparing the measured bandwidth to the preferred rate, the
minimum of these two is fed back to the server as the expected sending rate. Thus, the server’s
sending rate is set proactively to avoid aggressive or conservative startup. The VoD server
provides different levels of QoS to different clients based on their feedback. For example, it
sends a high-resolution video stream at high frame rate to the client communicating via a highbandwidth channel, e.g., wireless LAN, and a relatively low-resolution video stream at low
frame rate to the client communicating via a low-bandwidth channel, e.g., GSM.
16
During runtime, the client can apply the NAS framework in both proactive and reactive ways.
When the available bandwidth decreases, network awareness is deployed reactively. Specifically,
the available bandwidth cannot support the current server’s sending rate, so some packets are
lost. If the packet loss rate exceeds a given threshold, e.g., 20%, which is noticeably greater than
the normal wireless transmission loss rate, the client then obtains the available bandwidth from
the NAS agent. The measurement is based on the payload packets rather than the probe packets.
The client sets the measured value as the expected sending rate of the video server and feeds it
back to the server so the server can adjust its sending rate accordingly. When the available
bandwidth increases, there is no noticeable event to reveal this unless the video application
learns it in a proactive way. During the no-packet-loss period, the client periodically acquires the
available bandwidth from the NAS agent. The frequency depends on the dynamics of the
wireless link, and it may affect the agility of the video application. We are currently investigating
a method where the server’s sending rate grows to uncover the possible available bandwidth
increase, similar to the TCP’s congestion control algorithm.
2) Emulation Results
The objective of this experiment is to test the validity and agility of our method for availablebandwidth measurement in a dynamic networking environment. The experiment was conducted
in the testbed shown in Fig. 6. A serial link is used to emulate the wireless link in terms of
dynamically changing bandwidth, since we can control the bandwidth of the serial link
explicitly. The workstation with a camera in the Internet part sends live video streams to the
clients to emulate the VoD server. The workstation without the camera on the Internet requests a
video stream from the server and emulates the wireless clients.
The experiment results are shown in Fig. 7(a) and 7(b). The preferred rate is set at 125 Kbps,
and the client acquires the available bandwidth every minute. At the startup phase, the client
notifies the server about the measured available bandwidth and the server adjusts its sending rate
accordingly, so there is no packet loss. At 45 sec, the available bandwidth decreases to 72 Kbps
17
30
Actual Data Volume
Optimal Data Volume
120
25
100
Data volume (M bits)
Available bandwidth (Kbps)
140
Bandwidth
Loss Rate
80
Packet loss rate (%)
60
20
15
10
40
5
20
0
0
0
50
100
150
time (second)
200
250
0
50
100
150
time (second)
(a)
200
250
(b)
140
120
Receiving rate (Kbps)
100
80
60
40
20
0
0
100
200
300
time (second)
400
500
600
(c)
Fig. 7. (a) Bandwidth and packet loss rate of an emulated wireless link. (b) Actual data volume transmitted vs.
optimal data volume. (c) Receiving rate at the client with a wireless link in campus networks
leading to a packet loss. The client reactively acquires the new available bandwidth and feeds the
information back to the server to slow down. During runtime, the client also proactively acquires
the available bandwidth so that the server can accelerate its sending rate if the available
bandwidth increases (e.g., at 108 sec the bandwidth increases back to 125 Kbps). The
application’s agility for the available bandwidth decrease is very good since the time gap
between the decrease and the server’s slowdown is negligible, which is shown in Fig. 7(a).
However, when the available bandwidth increases, there are noticeable time gaps between the
18
increase and the server’s subsequent speedup. The actual received data volume is a bit smaller
than the optimal data volume that could be received (Fig. 7b). The results show that the actual
volume is a good approximation to the optimal volume since network awareness enables the
application to adapt in both proactive and reactive manners.
3) Experimental Results
This experiment was conducted using a campus network. The mobile client, which is the
wearable computer in the wireless LAN, connects to the VoD server, which is the workstation
with a camera in the Internet part (Fig. 6). The preferred rate is again set to 125 Kbps. Fig. 7(c)
shows the data-receiving rate at the client side. The available bandwidth is for most of the time
lower than the preferred rate. However, based on the emulation results, we can conclude that the
receiving rate at the mobile client approximates the available bandwidth of the wireless link.
Fig. 7(c) demonstrates the capability of wireless-aware applications to adjust to the channel
handoff and blackout. For example, in the period between 150 and 220 seconds, the mobile client
was experiencing relatively small bandwidth because of the handoff between two base stations.
In the period between 350 and 410 seconds, the client was out of range of the base stations so
that the available bandwidth is almost zero (the video at the wearable computer was frozen).
V.
RELATED WORK
Since the wireless channel is subject to dynamic behavior of limited bandwidth, path loss,
large delay, fading, and environmental interference [9], there are numerous efforts on application
adaptation to wireless link limitations and its dynamics [10]. A survey of research in adaptive
schemes and applications in wireless communication environments is given in [11].
The Odyssey project [12] develops a platform for adaptive mobile data access. Their
application-aware adaptation architecture is intermediate in the architectural spectrum between
the laissez-faire adaptation [13] and application-transparent adaptation [14] architectures. In
Odyssey’s scheme, the operating system is responsible for monitoring resource availability and
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notifying applications of relevant changes. Each application decides how to best exploit the
available resources [15]. This model of adaptation is similar to that of the NAS framework.
However, Odyssey’s architectural components reside on the mobile device, in either the user
space or the operating system, which is not as scalable as the piecewise NAS framework.
Moreover, the network awareness in the above three adaptation architectures is acquired at the
mobile client. In contrast, the algorithms for network awareness acquisition in our framework are
piecewise, which offers the advantages discussed earlier.
In [16], an architecture is presented for exporting environment awareness to mobile computing
applications. A change in the environment is modeled as an asynchronous event and an
associated event delivery framework is proposed. Unlike the NAS framework, the environment
monitor resides in the mobile host and only the push model is deployed for event delivery.
Another framework for developing network-aware applications is presented in [17] but the
techniques for awareness acquisition are not elaborated. This framework is also unitary.
Additionally, only reactive QoS adaptation is mentioned.
Our research is distinguished in that the NAS framework is piecewise and scalable to fit into
the scenario of heterogeneous data networks with mobile devices of very reduced capabilities.
The framework provides for both proactive and reactive deployment.
VI.
CONCLUSIONS
As the era of wireless Internet enabling “anyone, anytime, anywhere” communication is fast
approaching, the ability of applications to adapt to the dynamic networking environment
becomes ever more important. Adaptive network applications both deliver higher performance
and use the heterogeneous data networks’ resources more economically.
This paper presents a piecewise framework for network awareness service (NAS). By
implementing a push/pull service distribution model in the NAS framework, network
applications can take advantage of knowledge about the real-time performance of the
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heterogeneous data networks. Thus the communication can be dynamically customized by user
specifications, device types, and heterogeneous-network performances.
Piecewise techniques that comprise the NAS framework, such as piecewise measurement of
end-to-end available bandwidth, round-trip time, and packet loss rate are presented and evaluated
in this paper. The analytical results about the performance of the NAS framework, compared to
the unitary framework, show that: (i) the piecewise framework can help applications reduce
wireless bandwidth consumption and save limited battery energy of mobile devices, and (ii) it
does not reduce the agility of awareness mechanism. The experimental results demonstrate that
network applications can efficiently adapt to the heterogeneous networking environment
proactively and reactively by deploying NAS and achieve a near-optimal data-volume
transmission. As a result, applications can use the network and computing resources more
economically while yielding improved performance.
Acknowledgments
This research is supported by NSF KDI Contract No. IIS-98-72995, DARPA Contract No.
N66001-96-C-8510 and by the Rutgers Center for Advanced Information Processing (CAIP).
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