Throughput Maximization in Wireless LAN with Load Balancing Approach and Cell Breathing

Transcription

1 Throughput Maximization in Wireless LAN with Load Balancing Approach and Cell Breathing Prof.Devesh Sharma Prof.MamtaSood Subhash patil Santosh Durugkar TIT, Bhopal TIT, Bhopal TIT, Bhopal LGNSCOE,Nasik Abstract Network load is one of the key tests in wireless LANs.This objective is typically achieved when the load of access points is well-adjusted. Recent studies on operational WLANs, shown that AP load is often uneven distribution. To correct such overload, several load balancing schemes have been suggested. These procedures are commonly required proprietary software or hardware at the user side for controlling the user-ap association. To achieve efficient resource usage without requiring changes to client software, we propose the use of cell breathing technique. In this paper we try present a new load balancing method by controlling the size of WLAN cells, which is conceptually similar to cell breathing in cellular networks. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. We also consider the problem of network-wide min-max load balancing. Simulation results show that the performance of the proposed method is comparable with or superior to the best existing association-based method. Keywords: Cell Breathing, WLAN, Load Balancing, receiving power 1. Introduction Wireless LAN A wireless local area network (WLAN) links two or more devices using some wireless distribution method (typically spread-spectrum or OFDM radio), and usually providing a connection through an access point to the wider Internet. This gives users the mobility to move around within alocal coverage area and still be connected to the network. Most modern WLANs are based on IEEE standards, marketed under the Wi-Fi brand name. Fig.1 Wirelesses LAN Wireless LANs have become popular in the home due to ease of installation, and in commercial complexes offering wireless access to their customers; often for free. Large wireless network projects are being put up in many major cities: New York City, for instance, has begun a pilot program to provide city workers in all five boroughs of the city with wireless Internet access Fig.2 Example of WLAN 92

2 2. Typesof Wireless LANs 2.1 Peer-to-peer An ad-hoc network also called WiFi Direct network is a network where stations communicate only peer to peer (P2P). There is no base and no one gives permission to talk. This is accomplished using the Independent Basic Service Set (IBSS). A peer-to-peer (P2P) network allows wireless devices to directly communicate with each other. Wireless devices within range of each other can discover and communicate directly without involving central access points. This method is typically used by two computers so that they can connect to each other to form a network. If a signal strength meter is used in this situation, it may not read the strength accurately and can be misleading, because it registers the strength of the strongest signal, which may be the closest computer. need for a wired backbone to link them, as is traditionally required. The notable advantage of WDS over other solutions is that it preserves the MAC addresses of client packets across links between access points 3. Load Balancing 3.1 Load Balancing Load balancing is a computer networking method to distribute workload across multiple computers or a computer cluster, network links, central processing units, disk drives, or other resources, to achieve optimal resource utilization, maximize throughput, minimize response time, and avoid overload. Using multiple components with load balancing, instead of a single component, may increase reliability through redundancy. Fig. 3 Peer to Peer Network 2.2 Bridge A bridge can be used to connect networks, typically of different types. A wireless Ethernet bridge allows the connection of devices on a wired Ethernet network to a wireless network. The bridge acts as the connection point to the Wireless LAN. 2.3 A Wireless Distribution System A Wireless Distribution System enables the wireless interconnection of access points in an IEEE network. It allows a wireless network to be expanded using multiple access points without the Fig.4 Example shows n/w load balancer balances the load A variety of scheduling algorithms are used by load balancers to determine which backend server to send a request to. Simple algorithms include random choice or round robin. More sophisticated load balancers may take into account additional factors, such as a server's reported load, recent response times, up/down status (determined by a monitoring poll of some kind), number of active connections, geographic location, capabilities, or how much traffic it has recently been assigned. 93

3 3.2 Load Balance Problem One AP may support many WS, while some neighbor AP may support few or no stations. Fig 5 Load Balance Problem before balancing This asymmetry in load between AP causes a high probability of packet loss in AP 1 and thus an overall network degradation compared to AP 2, 3. Fig 6 Load Balance Problem after balancing Whether applying a WS distribution algorithm results in a load balance for each AP depends on the nature of the wireless LAN Problem becomes more difficult due to dynamic network topology changes when the WS are roaming around. As according with the increasing demand of various services of wireless LAN, there are large numbers of users that rely on wireless network for many access points. Wireless networks are challenging systems because of the complex nature of signal propagation for both wired and wireless networks; they need to deal with links going up and down. 4. Implementation Issues for Proposed System 4.1 Various Issues to be Discussed 1. Finding the suitable power assignment at APs to routinely achieve load balancing is a stimulating problem. 2. When client demands are same we can always calculate such a power assignment, we can set the powers of all APs in such a way that, after all the clients choose their AP. 3. We only assume that the received power is proportional to the transmission power, but do not assume any relationship between the received power and the distance. 4. We start by setting the powers of all APs to the highest value and then we choose the best power configuration resulting from iteratively decreasing the power of overloaded APs. This approach is intuitive and easy to implement. 5. It only requires knowledge of the APs Load, which is easy to obtain. We show that if there exists a power assignment such that each AP has capacity to accommodate the demands assigned to it, our algorithm can find the solution in a polynomial time. 4.2 Dynamic Power Assignment We inspect how to control power to optimize throughput based on given client demands. When clients demands are usually changing, it is often desirable to find an assignment without requiring many clients to handoff to different APs, since the overhead of handoff is non-negligible. 94

4 4.3 MaximizingThroughput By Using Cell Breathing Technique Cell breathing in CDMA Cell breathing is a term used to reflect the fact that the coverage area of a base station in a CDMA system will become smaller if there are more subscribers. If, for example, a total power of 20 watts is available, the network can feed 10 watts to each of two subscribers, or, 2 watts to each of 10 subscribers. The maximum output power available for an individual subscriber depends on the number of active subscribers in each radio cell. The more subscribers logged on to the cell, the lower the power available for aindividual subscriber and hence the lower its range. 4.4 ControllingPower of Access Points Cell breathing can be implemented by controlling the transmission power of an AP s beacon packets. When the SNR of data packets reduces, the AP may see higher data packet losses or even adapt to a lowersending rate, both of which degrade the client s performance. Clients continue to associate with an AP with the strongest beacons. APs manage their load by adjusting the beacon packets Transmission power. In this way, the AP s coverage area is shrunk or expanded transparently, adapting to client demands and balancing the traffic load across the network. In this way we are achieving the load balancing approach. Only the difficult thing is that finding the appropriate power assignment at APs to automatically achieve load balancing. Challenging task is to change the demands assigned to an AP by adjusting its power. If some request are Homogenous If there are no. of access points Aps present and if many requests given by clients are homogenous (similar requests) then we can set the powers of all APs in such a way that, after all the clients choose their AP, either all the clients can be served by the APs or all the APs are fully utilized. Means at this stage we can achieve full utilization of Aps. If some request are Heterogeneous If still some clients are requests are heterogeneous (different request) then also we apply the same approach and prove that it can completely satisfy at least N _ K clients, where N is the number of clients and K is the number of APs. Power Configuration We start by setting the powers of all APs to the highest value and then we choose the best power configuration resulting from iteratively decreasing the power of overloaded APs. But the constraint is only we should know the actual load of Aps Power Optimization Need arise to develop power controlalgorithms that maximize system throughput. Power minimizationis helpful to reduce interference among different APs.We consider the problem of optimizing the powerfor a given mapping of clients to APs. Initially All APs are assigned the samefixed power.the APs power is determined byproposed power control algorithm. A higher throughput indicates more efficientresource utilization Need arises to focus on following issues 01. Dynamic adjustment of the APs power in response to changes in clients load while limiting the number of hand-offs 02. Minimizing the APs transmission power to reduce interference. 95

5 Few assumptions about the proposed system: Number of Aps Number of Clients Capacity of Each AP Distance between AP and Client Client s demands The total load served at AP Received Power from AP CONCLUSION We will prove the efficiency of cell breathing technique to show that how itsignificantly out performs the popular fixed power schemes and performs comparably to the sophisticated load balancing techniques where the client and the APs are required to cooperate with one another. REFERENCES Cell Breathing Techniques for Load Balancing in Wireless LANs Yigal Bejerano, Member, IEEE, and Seung-Jae Han, Member, IEEE 3. I Paccess pointanikos and M. Logothetis, A Study on Dynamic Load Balance for IEEE b Wireless Lan, Proc. Int l conf. comm. Control. 4. Load Balancing in IEEE Networks by Li- Hsing Yen, Tse-Tsung Yeh, and Kuang - Hui Chi. 5. Cell Breathing in Wireless LANs: Algorithms and Evaluation by Paramvir (Victor) Bahl, Senior Member, IEEE Computer Society, Mohammad T. Hajiaghayi, Kamal Jain, Member, IEEE, Sayyed Vahab Mirrokni, Lili Qiu, Member, IEEE, and Amin Saberi. 96