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.
