QoS in Cellular Networks Based on MPT

[mithunk]

Quality of Service Support of Distributed Interactive
Virtual Environment Applications in IP Networks

ABSTRACT
This paper reports on the performance of Distributed Interactive Virtual Environment (DIVE) applications, deployed through Internet. Our goal is to understand the behaviour of a DIVE application, its interaction with competing traffic streams, as well as its network resource requirements for a satisfactory performance. As DIVE is becoming the building block of new applications and services, impacting several established or new and growing sectors of our economy (e.g. electronic commerce, tele-training, transportation, health), it is important that we understand the resource requirements of these applications, as well as the stress they will impose on the network.

INTRODUCTION
Distributed Interactive Virtual Environments (DIVE) [1, 2] are shared virtual worlds that could radically alter the way people work, play, learn, consume and collaborate. In DIVE applications, a simulated world runs not on one computer system, but on several, connected over a network (e.g. Internet). People who use those computers are able to interact in real time, sharing the same virtual world. Each of the machines participating in the simulation of the virtual word is called a host . On each host there is a number of entities (things in the virtual environment) that communicate their changing state by sending update messages . The specific entity that corresponds to a participant s virtual body is called avatar . Avatars are either included within the virtual world during initialization or dynamically created at a later time. The following are some of the key features of DIVE applications:

They are sensitive to packet delays. Any action issued
by any participant in the DIVE (transported through
packets) must reach the other participants within 100
ms.
They should be capable of scaling to large number of
participants. The number of participants should be
unlimited to allow everybody to enter the virtual world.
Messages are transported through short packets (few
tens of bytes) and frequently. This characteristic differs
from other multimedia application data like audio and
video.
They demonstrate high level of dynamicity in group
structure and topology. Participants should be able to
join and leave the session dynamically.
While DIVE applications will be used in services related to commerce, health, training, transportation and business, there is no adequate understanding on how they behave in modern networks. In [3], we presented the first (to the best of our knowledge) research results dealing with sensitivity analysis of DIVE applications in Ethernet and ATM networks, and modeling of DIVE traffic. The work was conducted under a CANARIE funded research project. For completeness, some of our results, reported in [3] will be included in this work. This paper, expands the work reported in [3], by testing a VR application in a Differentiated Services (Diffserv) network. Our objective is to assess the ability of DiffServ to support these delay, packet loss and performance sensitive applications, understand the effect main-stream applications , such as video, ftp etc., have on DIVE, when they co-exist in the same network, as well as fine tune the network s parameters and architecture, in order to provide acceptable performance levels to DIVE applications. We conducted our study through experimentation. A DIVE based application of tele-training nature was used, which was developed by researchers of the Multimedia Communications Research Laboratory (MCRLab) of the University of Ottawa. Its function is to enable instructors and technical support to train and guide remote users through Internet, on maters related to Newbridge s ATM
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[saksh_sagar]

QoS in Cellular Networks Based on MPT
In recent years, there has been a rapid increase in wireless network deployment and mobile device market penetration. With vigorous research that promises higher data rates, future wireless networks will likely become an integral part of the global communication infrastructure. Ultimately, wireless users will demand the same reliable service as today's wire-line telecommunications and data networks. However, there are some unique problems in cellular networks that challenge their service reliability.

In addition to problems introduced by fading, user mobility places stringent requirements on network resources. Whenever an active mobile terminal (MT) moves from one cell to another, the call needs to be handed off to the new base station (US), and network resources must be reallocated. Resource demands could fluctuate abruptly due to the movement of high data rate users. Quality of service (QoS) degradation or even forced termination may occur when there are insufficient resources to accommodate these handoffs.

If the system has prior knowledge of the exact trajectory of every MT, it could take appropriate steps to reserve resources so that QoS may be guaranteed during the MT's connection lifetime. However, such an ideal scenario is very unlikely to occur in real life. Instead, much of the work on resource reservation has adopted a predictive approach.

One approach uses pattern matching techniques and a self-adaptive extended Kalman filter for next-cell prediction based on cell sequence observations, signal strength measurements, and cell geometry assumptions. Another approach proposes the concept of a shadow cluster: a set of BSs to which an MT is likely to attach in the near future. The scheme estimates the probability of each MT being in any cell within the shadow cluster for future time intervals, based on knowledge about individual MTs' dynamics and call holding patterns.

[ashritha]

Definition

In recent years, there has been a rapid increase in wireless network deployment and mobile device market penetration. With vigorous research that promises higher data rates, future wireless networks will likely become an integral part of the global communication infrastructure. Ultimately, wireless users will demand the same reliable service as today's wire-line telecommunications and data networks. However, there are some unique problems in cellular networks that challenge their service reliability.

In addition to problems introduced by fading, user mobility places stringent requirements on network resources. Whenever an active mobile terminal (MT) moves from one cell to another, the call needs to be handed off to the new base station (US), and network resources must be reallocated. Resource demands could fluctuate abruptly due to the movement of high data rate users. Quality of service (QoS) degradation or even forced termination may occur when there are insufficient resources to accommodate these handoffs.

If the system has prior knowledge of the exact trajectory of every MT, it could take appropriate steps to reserve resources so that QoS may be guaranteed during the MT's connection lifetime. However, such an ideal scenario is very unlikely to occur in real life. Instead, much of the work on resource reservation has adopted a predictive approach.

One approach uses pattern matching techniques and a self-adaptive extended Kalman filter for next-cell prediction based on cell sequence observations, signal strength measurements, and cell geometry assumptions. Another approach proposes the concept of a shadow cluster: a set of BSs to which an MT is likely to attach in the near future. The scheme estimates the probability of each MT being in any cell within the shadow cluster for future time intervals, based on knowledge about individual MTs' dynamics and call holding patterns.

[Asmi.A]

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