20-01-2010, 03:06 PM
Cell breathing techniques, Abstract ,Base paper
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Cell breathing techniques full report
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20-01-2010, 03:31 PM
submitted by: louis
Cell Breathing Techniques for Load Balancing in Wireless LANs
Abstract
Maximizing network throughput while providing fairness is one of the key challenges in wireless LANs (WLANs). This goal is typically achieved when the load of access points (APs) is balanced. Recent studies on operational WLANs, however, have shown that AP load is often substantially uneven. To alleviate such imbalance of load, several load balancing schemes have been proposed. These schemes commonly require proprietary software or hardware at the user side for controlling the user-AP association. In this paper we present a new load balancing technique by controlling the size of WLAN cells (i.e., AP's coverage range), which is conceptually similar to cell breathing in cellular networks. The proposed scheme does not require any modification to the users neither the IEEE 802.11 standard. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. We develop a set of polynomial time algorithms that find the optimal beacon power settings which minimize the load of the most congested AP. We also consider the problem of network-wide min-max load balancing.
Algorithm / Technique used:
Min-Max Algorithm.
Algorithm Description:
The algorithm iteratively finds a minmax priority-load-balanced state that yields the optimal load vector ~Y. At any iteration m, m 2 ½1::jAj , we call a routine to calculate a network state that minimizes the priority load of the mth coordinate of the load vector. The routine needs to satisfy two requirements:
Requirement 1. The initial state of each iteration, m, must dominate the optimal state.
Requirement 2. The calculated network state at the mth iteration should not affect (increase) the load of the APs that their load have already been determined by the previous iterations.
Existing System
Several studies have proposed a variety of association metrics instead of using the RSSI as the sole criterion. These metrics typically take into account such factors as the number of users currently associated with an AP, the mean RSSI of users currently associated with an AP, and the bandwidth that a new user can get if it is associated with an AP, e.g., Balachandran et al. proposed to associate a user with an AP that can provide a minimal bandwidth required by the user. In Velayos et al. introduced a distributed
load balancing architecture were the AP load is defined as the aggregated downlink and uplink traffic through the AP. In Kumar and coworkers proposed association selection algorithms which are based on the concept of proportional fairness to balance between throughput and fairness. In Kauffmann et al. provided a mathematical foundation for distributed frequency allocation and user association for efficient resource sharing. Recently, in Shakkottai et al. considered a no cooperative multihoming approach and showed that under appropriate pricing, the system throughput is maximized. In a strong relation between fairness and load balancing is shown. Most of these works determine only the association of newly arrived users. Tsai et al. is an exception, in which Tsai and Lien proposed to reassociate users when the total load exceeds a certain threshold or the bandwidth allocated to usersâ„¢ drops below a certain threshold. While the existing load balancing schemes achieved considerable improvement in terms of throughput and fairness, they require certain support from the client side. In contrast, the proposed scheme does not require any proprietary client support.
Proposed System
We address the problem of min-max load balancing. This is a strong NP-hard problem. In it is proved that there exists no algorithm that guarantees any coordinate wise approximation ratio, and the approximation ratio of any prefix-sum approximation algorithm is at least (logn), where n is the number of APs. In this paper, we solve a variant of this min-max problem, termed min-max priority load balancing, whose optimal solution can be calculated in polynomial time for both knowledge models. Here, the AP load is defined as an ordered pair of the aggregated load contributions of its associated users and a unique AP priority.
Hardware Requirements
¢ System : Pentium IV 2.4 GHz.
¢ Hard Disk : 40 GB.
¢ Floppy Drive : 1.44 Mb.
¢ Monitor : 15 VGA Colour.
¢ Mouse : Logitech.
¢ Ram : 256 Mb.
Software Requirements
¢ Operating System : - Windows Xp Professional.
¢ Front End :-Visual Studio Dot Net 2005.
¢ Coding Language : - C#.
¢ Database :-Sql 2000.
Cell Breathing Techniques for Load Balancing in Wireless LANs
Abstract
Maximizing network throughput while providing fairness is one of the key challenges in wireless LANs (WLANs). This goal is typically achieved when the load of access points (APs) is balanced. Recent studies on operational WLANs, however, have shown that AP load is often substantially uneven. To alleviate such imbalance of load, several load balancing schemes have been proposed. These schemes commonly require proprietary software or hardware at the user side for controlling the user-AP association. In this paper we present a new load balancing technique by controlling the size of WLAN cells (i.e., AP's coverage range), which is conceptually similar to cell breathing in cellular networks. The proposed scheme does not require any modification to the users neither the IEEE 802.11 standard. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. We develop a set of polynomial time algorithms that find the optimal beacon power settings which minimize the load of the most congested AP. We also consider the problem of network-wide min-max load balancing.
Algorithm / Technique used:
Min-Max Algorithm.
Algorithm Description:
The algorithm iteratively finds a minmax priority-load-balanced state that yields the optimal load vector ~Y. At any iteration m, m 2 ½1::jAj , we call a routine to calculate a network state that minimizes the priority load of the mth coordinate of the load vector. The routine needs to satisfy two requirements:
Requirement 1. The initial state of each iteration, m, must dominate the optimal state.
Requirement 2. The calculated network state at the mth iteration should not affect (increase) the load of the APs that their load have already been determined by the previous iterations.
Existing System
Several studies have proposed a variety of association metrics instead of using the RSSI as the sole criterion. These metrics typically take into account such factors as the number of users currently associated with an AP, the mean RSSI of users currently associated with an AP, and the bandwidth that a new user can get if it is associated with an AP, e.g., Balachandran et al. proposed to associate a user with an AP that can provide a minimal bandwidth required by the user. In Velayos et al. introduced a distributed
load balancing architecture were the AP load is defined as the aggregated downlink and uplink traffic through the AP. In Kumar and coworkers proposed association selection algorithms which are based on the concept of proportional fairness to balance between throughput and fairness. In Kauffmann et al. provided a mathematical foundation for distributed frequency allocation and user association for efficient resource sharing. Recently, in Shakkottai et al. considered a no cooperative multihoming approach and showed that under appropriate pricing, the system throughput is maximized. In a strong relation between fairness and load balancing is shown. Most of these works determine only the association of newly arrived users. Tsai et al. is an exception, in which Tsai and Lien proposed to reassociate users when the total load exceeds a certain threshold or the bandwidth allocated to usersâ„¢ drops below a certain threshold. While the existing load balancing schemes achieved considerable improvement in terms of throughput and fairness, they require certain support from the client side. In contrast, the proposed scheme does not require any proprietary client support.
Proposed System
We address the problem of min-max load balancing. This is a strong NP-hard problem. In it is proved that there exists no algorithm that guarantees any coordinate wise approximation ratio, and the approximation ratio of any prefix-sum approximation algorithm is at least (logn), where n is the number of APs. In this paper, we solve a variant of this min-max problem, termed min-max priority load balancing, whose optimal solution can be calculated in polynomial time for both knowledge models. Here, the AP load is defined as an ordered pair of the aggregated load contributions of its associated users and a unique AP priority.
Hardware Requirements
¢ System : Pentium IV 2.4 GHz.
¢ Hard Disk : 40 GB.
¢ Floppy Drive : 1.44 Mb.
¢ Monitor : 15 VGA Colour.
¢ Mouse : Logitech.
¢ Ram : 256 Mb.
Software Requirements
¢ Operating System : - Windows Xp Professional.
¢ Front End :-Visual Studio Dot Net 2005.
¢ Coding Language : - C#.
¢ Database :-Sql 2000.
28-08-2010, 12:55 PM
hi guys,..
its very nice work to start thread about Cell breathing techniques full report,..
its really really nice sharing i really appreciate it ,.. thanks
its very nice work to start thread about Cell breathing techniques full report,..
its really really nice sharing i really appreciate it ,.. thanks
29-08-2010, 10:48 AM
hai guys...i need full documentation of cell breathing techniques for load balancing in wireless LANs...if u hav plz send it
04-10-2010, 03:11 PM
i need full report for Cell breathing techniques for loadbalancing in wireless LAN..
Please help me.....
Thanks in advance
Reference: http://studentbank.in/report-cell-breath...z11NiYkDWm
Please help me.....
Thanks in advance
Reference: http://studentbank.in/report-cell-breath...z11NiYkDWm
22-02-2011, 11:16 AM
[attachment=8933]
Cell Breathing Techniques for Load Balancing in Wireless LANs
Abstract
Maximizing network throughput while providing fairness is one of the key challenges in wireless LANs (WLANs). This goalis typically achieved when the load of access points (APs) is balanced. Recent studies on operational WLANs, however, have shownthat AP load is often substantially uneven. To alleviate such imbalance of load, several load balancing schemes have been proposed.These schemes commonly require proprietary software or hardware at the user side for controlling the user-AP association. In thispaper we present a new load balancing technique by controlling the size of WLAN cells (i.e., AP’s coverage range), which isconceptually similar to cell breathing in cellular networks. The proposed scheme does not require any modification to the users neitherthe IEEE 802.11 standard. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. Wedevelop a set of polynomial time algorithms that find the optimal beacon power settings which minimize the load of the most congestedAP. We also consider the problem of network-wide min-max load balancing. Simulation results show that the performance of theproposed method is comparable with or superior to the best existing association-based methods.
Modules:
Client Model
Server Model
Network Model
Cell Breathing Approach
Congestion Load Minimization
Module Description
Client Model
A client is an application or system that accesses a remote service on another computer system, known as a server, by way of a network. The term was first applied to devices that were not capable of running their own stand-alone programs, but could interact with remote computers via a network. These dumb terminals were clients of the time-sharing mainframe computer
Server model
In computing, a server is any combination of hardware or software designed to provide services to clients. When used alone, the term typically refers to a computer which may be running a server operating system, but is commonly used to refer to any software or dedicated hardware capable of providing services.
Network Model
Generally, the channel quality is time-varying. For the ser-AP association decision, a user performs multiple samplings of the channel quality, and only the signal attenuation that results from long-term channel condition changes are utilized our load model can accommodate various additive load definitions such as the number of users associated with an AP. It can also deal with the multiplicative user load contributions.
Cell Breathing Approach
We reduce the load of congested APs by reducing the size of the corresponding cells. Such cell dimensioning can be obtained, for instance, by reducing the transmission power of the congested APs. This forces users near the congested cells’ boundaries to shift to adjacent (less congested) APs. The separation between the transmission power of the data traffic and that of the AP beacon messages. On one hand, the transmission bit rate between a user and its associated AP is determined by the quality of the data traffic channel. Transmitting the data traffic with maximal power3 maximizes the AP-user SNR and the bit rate. On the other hand, each user determines its association by performing a scanning operation, in which itevaluates the quality of the beacon messages of the APs in its vicinity.
Congestion Load Minimization
The algorithms presented in Section 4 minimize the load of the congested AP, but they do not necessarily balance the load of the no congested APs, as demonstrated in Examples 4 and 5. In this section, we consider min-max load balancing approach that not only minimizes the network congestion load but also balances the load of the no congested APs. As mentioned earlier, the proposed approach can be used for obtaining various max-min fairness objectives by associating each users with appropriate load contributions. Unfortunately, min-max load balancing is NP-hard problem and it is hard to find even an approximated solution. In this paper, we solve a variant of the min-max problem, termed min-max priority-load balancing problem, whose optimal solution can be found in polynomial time
Cell Breathing Techniques for Load Balancing in Wireless LANs
Abstract
Maximizing network throughput while providing fairness is one of the key challenges in wireless LANs (WLANs). This goalis typically achieved when the load of access points (APs) is balanced. Recent studies on operational WLANs, however, have shownthat AP load is often substantially uneven. To alleviate such imbalance of load, several load balancing schemes have been proposed.These schemes commonly require proprietary software or hardware at the user side for controlling the user-AP association. In thispaper we present a new load balancing technique by controlling the size of WLAN cells (i.e., AP’s coverage range), which isconceptually similar to cell breathing in cellular networks. The proposed scheme does not require any modification to the users neitherthe IEEE 802.11 standard. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. Wedevelop a set of polynomial time algorithms that find the optimal beacon power settings which minimize the load of the most congestedAP. We also consider the problem of network-wide min-max load balancing. Simulation results show that the performance of theproposed method is comparable with or superior to the best existing association-based methods.
Modules:
Client Model
Server Model
Network Model
Cell Breathing Approach
Congestion Load Minimization
Module Description
Client Model
A client is an application or system that accesses a remote service on another computer system, known as a server, by way of a network. The term was first applied to devices that were not capable of running their own stand-alone programs, but could interact with remote computers via a network. These dumb terminals were clients of the time-sharing mainframe computer
Server model
In computing, a server is any combination of hardware or software designed to provide services to clients. When used alone, the term typically refers to a computer which may be running a server operating system, but is commonly used to refer to any software or dedicated hardware capable of providing services.
Network Model
Generally, the channel quality is time-varying. For the ser-AP association decision, a user performs multiple samplings of the channel quality, and only the signal attenuation that results from long-term channel condition changes are utilized our load model can accommodate various additive load definitions such as the number of users associated with an AP. It can also deal with the multiplicative user load contributions.
Cell Breathing Approach
We reduce the load of congested APs by reducing the size of the corresponding cells. Such cell dimensioning can be obtained, for instance, by reducing the transmission power of the congested APs. This forces users near the congested cells’ boundaries to shift to adjacent (less congested) APs. The separation between the transmission power of the data traffic and that of the AP beacon messages. On one hand, the transmission bit rate between a user and its associated AP is determined by the quality of the data traffic channel. Transmitting the data traffic with maximal power3 maximizes the AP-user SNR and the bit rate. On the other hand, each user determines its association by performing a scanning operation, in which itevaluates the quality of the beacon messages of the APs in its vicinity.
Congestion Load Minimization
The algorithms presented in Section 4 minimize the load of the congested AP, but they do not necessarily balance the load of the no congested APs, as demonstrated in Examples 4 and 5. In this section, we consider min-max load balancing approach that not only minimizes the network congestion load but also balances the load of the no congested APs. As mentioned earlier, the proposed approach can be used for obtaining various max-min fairness objectives by associating each users with appropriate load contributions. Unfortunately, min-max load balancing is NP-hard problem and it is hard to find even an approximated solution. In this paper, we solve a variant of the min-max problem, termed min-max priority-load balancing problem, whose optimal solution can be found in polynomial time
09-03-2011, 03:24 PM
i need the full document and explanation in detail..........plz help me as soon as possible
15-03-2011, 08:57 PM
i need ppt on this topic. can u plz help me????????
18-03-2011, 07:53 PM
i have uploaded the ppt for cell breathing techniques...
31-03-2011, 10:27 AM
presented by
G.Swetha
Srinivas
Sd.Syed
[attachment=11384]
Cell Breathing Techniques for Load Balancing in Wireless LANs
Maximizing network throughput while providing fairness is one of the key challenges in wireless LANs (WLANs).
Modules:
• Client Model
• Server Model
• Network Model
• Cell Breathing Approach
• Congestion Load Minimization
Module Description
Client Model
• A client is an application or system that accesses a remote service on another computer system, known as a server, by way of a network
Server model
• In computing, a server is any combination of hardware or software designed to provide services to clients
Network Model
• It can also deal with the multiplicative user load contributions.
Cell Breathing Approach
• We reduce the load of congested APs by reducing the size of the corresponding cells.
Congestion Load Minimization
• The algorithms representes In minimize the load of the congested AP, but they do not necessarily balance the load of the no congested APs.
G.Swetha
Srinivas
Sd.Syed
[attachment=11384]
Cell Breathing Techniques for Load Balancing in Wireless LANs
Maximizing network throughput while providing fairness is one of the key challenges in wireless LANs (WLANs).
Modules:
• Client Model
• Server Model
• Network Model
• Cell Breathing Approach
• Congestion Load Minimization
Module Description
Client Model
• A client is an application or system that accesses a remote service on another computer system, known as a server, by way of a network
Server model
• In computing, a server is any combination of hardware or software designed to provide services to clients
Network Model
• It can also deal with the multiplicative user load contributions.
Cell Breathing Approach
• We reduce the load of congested APs by reducing the size of the corresponding cells.
Congestion Load Minimization
• The algorithms representes In minimize the load of the congested AP, but they do not necessarily balance the load of the no congested APs.
17-12-2011, 05:08 PM
hello sir/madam
i need a project report about cell breathing technique for load balancing in wireless lans
so plz kindly help me...
thank u sir/ for ur valuable support
i need a project report about cell breathing technique for load balancing in wireless lans
so plz kindly help me...
thank u sir/ for ur valuable support
19-12-2011, 09:40 AM
to get information about the topic"Cell breathing techniques full report" refer the page link bellow
http://studentbank.in/report-cell-breath...ull-report
http://studentbank.in/report-cell-breath...ort?page=2
http://studentbank.in/report-cell-breath...5#pid60005
01-03-2012, 01:35 PM
plz can anyone mail me full documentation of cell brething technique for wireless lans..
mail add:manai.akshatha[at]gmail.com
mail add:manai.akshatha[at]gmail.com
01-03-2012, 02:58 PM
to get information about the topic cell breathing technique for load balancing in wireless LANs full report ppt and related topic refer the page link bellow
http://studentbank.in/report-cell-breath...eless-lans
http://studentbank.in/report-cell-breath...-computing
http://studentbank.in/report-cell-breath...ull-report
http://studentbank.in/report-cell-breath...eless-lans
http://studentbank.in/report-cell-breath...-computing
http://studentbank.in/report-cell-breath...ull-report
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