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Heterogeneous wireless networking technologies such as satellite, UMTS, WiMax and WLAN are being used to provide network access for both voice and data services. In big cities, the densely populated areas like town centres, shopping centres and train stations may have coverage of multiple wireless networks. Traditional Radio Access Technology (RAT) selection algorithms are mainly based on the ¿Always Best Connected¿ paradigm whereby the mobile nodes are always directed towards the available network which has the strongest and fastest link. Hence a large number of mobile users may be connected to the more common UMTS while the other networks like WiMax and WLAN would be underutilised, thereby creating an unbalanced load across these different wireless networks. This high variation among the load across different co-located networks may cause congestion on overloaded network leading to high call blocking and call dropping probabilities. This can be alleviated by moving mobile users from heavily loaded networks to least loaded networks. This thesis presents a novel framework for load balancing in heterogeneous wireless networks incorporating the IEEE 802.21 Media Independent Handover (MIH). The framework comprises of novel load-aware RAT selection techniques and novel network load balancing mechanism. Three new different load balancing algorithms i.e. baseline, fuzzy and neural-fuzzy algorithms have also been presented in this thesis that are used by the framework for efficient load balancing across the different co-located wireless networks. A simulation model developed in NS2 validates the performance of the proposed load balancing framework. Different attributes like load distribution in all wireless networks, handover latencies, packet drops, throughput at mobile nodes and network utilization have been observed to evaluate the effects of load balancing using different scenarios. The simulation results indicate that with load balancing the performance efficiency improves as the overloaded situation is avoided by load balancing.

Content hosted on the Internet must appear robust and reliable to clients relying on such content. As more clients come to rely on content from a source, that source can be subjected to high levels of load. There are a number of solutions, collectively called load balancers, which try to solve the load problem through various means. All of these solutions are workarounds for dealing with problems inherent in the medium by which content is served thereby limiting their effectiveness. HTTP, or Hypertext Transport Protocol, is the dominant mechanism behind hosting content on the Internet through websites. The entirety of the Internet has changed drastically over its history, with the invention of new protocols, distribution methods, and technological improvements. However, HTTP has undergone only three versions since its inception in 1991, and all three versions serve content as a text stream that cannot be interrupted to allow for load balancing decisions. We propose a solution that takes existing portions of HTTP, augments them, and includes some new features in order to increase usability and management of serving content over the Internet by allowing redirection of content in-stream. This in-stream redirection introduces a new step into the client-server connection where servers can make decisions while continuing to serve content to the client. Load balancing methods can then use the new version of HTTP to make better decisions when applied to multi-server systems making load balancing more robust, with more control over the client-server interaction.

No / Over the last few years, cloud computing has become quite popular. It offers Web-based companies the advantage of scalability. However, this scalability adds complexity which makes analysis and predictable performance difficult. There is a growing body of research on load balancing in cloud data centres which studies the problem from the perspective of the cloud provider. Nevertheless, the load balancing of scalable web servers deployed on the cloud has been subjected to less research. This paper introduces a simple queueing model to analyse the performance metrics of web server under varying traffic loads. This assists web server managers to manage their clusters and understand the trade-off between QoS and cost. In this proposed model two thresholds are used to control the scaling process. A discrete-event simulation (DES) is presented and validated via an analytical solution.

We developed a diffusive load balancing scheme that equalizes the available capacities of nodes in a peer-to-peer (P2P) system. These nodes may have different resource capacities, geographic locations, or availabilities (i.e., length of time being part of the peer-to-peer system). The services on these nodes may have different service times and arrival rates of requests. Using the diffusive scheme, the system is able to maintain similar response times for its services. Our scheme is a modification of the diffusive load balancing algorithms proposed for parallel computing systems. This scheme is able to handle services with heterogeneous resource requirements and P2P nodes with heterogeneous capacities. We also adapted the diffusive scheme to clustered peer-to-peer system, where a load balancing operation may move services or nodes between clusters. After a literature survey of this field, this thesis investigates the following issues using analytical reasoning and extensive simulation studies. The load balancing operations equalize the available capacities of the nodes in a neighborhood to their averages. As a result, the available capacities of all nodes in the P2P system converge to a global average. We found that this convergence is faster when the scheme uses neighborhoods defined by the structure of the structured P2P overlay network rather than using randomly selected neighbors. For a system with churn (i.e. nodes joining and leaving), the load balancing operations maintain the standard deviation of the available capacities of nodes within a bound. This bound depends on the amount of churn and the frequency of load balancing operations, as well as on the capacities of the nodes. However, the sizes of the services have little impact on this bound. In a clustered peer-to-peer system, the size of the bound largely depends on the average cluster size. When nodes are moved among clusters for load balancing, the numbers of cluster splits and merges are reduced. This may reduce the maintenance cost of the overlay network.

We developed a diffusive load balancing scheme that equalizes the available capacities of nodes in a peer-to-peer (P2P) system. These nodes may have different resource capacities, geographic locations, or availabilities (i.e., length of time being part of the peer-to-peer system). The services on these nodes may have different service times and arrival rates of requests. Using the diffusive scheme, the system is able to maintain similar response times for its services. Our scheme is a modification of the diffusive load balancing algorithms proposed for parallel computing systems. This scheme is able to handle services with heterogeneous resource requirements and P2P nodes with heterogeneous capacities. We also adapted the diffusive scheme to clustered peer-to-peer system, where a load balancing operation may move services or nodes between clusters. After a literature survey of this field, this thesis investigates the following issues using analytical reasoning and extensive simulation studies. The load balancing operations equalize the available capacities of the nodes in a neighborhood to their averages. As a result, the available capacities of all nodes in the P2P system converge to a global average. We found that this convergence is faster when the scheme uses neighborhoods defined by the structure of the structured P2P overlay network rather than using randomly selected neighbors. For a system with churn (i.e. nodes joining and leaving), the load balancing operations maintain the standard deviation of the available capacities of nodes within a bound. This bound depends on the amount of churn and the frequency of load balancing operations, as well as on the capacities of the nodes. However, the sizes of the services have little impact on this bound. In a clustered peer-to-peer system, the size of the bound largely depends on the average cluster size. When nodes are moved among clusters for load balancing, the numbers of cluster splits and merges are reduced. This may reduce the maintenance cost of the overlay network.

We developed a diffusive load balancing scheme that equalizes the available capacities of nodes in a peer-to-peer (P2P) system. These nodes may have different resource capacities, geographic locations, or availabilities (i.e., length of time being part of the peer-to-peer system). The services on these nodes may have different service times and arrival rates of requests. Using the diffusive scheme, the system is able to maintain similar response times for its services. Our scheme is a modification of the diffusive load balancing algorithms proposed for parallel computing systems. This scheme is able to handle services with heterogeneous resource requirements and P2P nodes with heterogeneous capacities. We also adapted the diffusive scheme to clustered peer-to-peer system, where a load balancing operation may move services or nodes between clusters. After a literature survey of this field, this thesis investigates the following issues using analytical reasoning and extensive simulation studies. The load balancing operations equalize the available capacities of the nodes in a neighborhood to their averages. As a result, the available capacities of all nodes in the P2P system converge to a global average. We found that this convergence is faster when the scheme uses neighborhoods defined by the structure of the structured P2P overlay network rather than using randomly selected neighbors. For a system with churn (i.e. nodes joining and leaving), the load balancing operations maintain the standard deviation of the available capacities of nodes within a bound. This bound depends on the amount of churn and the frequency of load balancing operations, as well as on the capacities of the nodes. However, the sizes of the services have little impact on this bound. In a clustered peer-to-peer system, the size of the bound largely depends on the average cluster size. When nodes are moved among clusters for load balancing, the numbers of cluster splits and merges are reduced. This may reduce the maintenance cost of the overlay network.