22-02-2011, 11:16 AM
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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
