Towards improved load balancing for data intensive distributed computing
Proceedings of the 2011 ACM Symposium on Applied Computing
Load splitting in clusters of video servers
Computer Communications
An improved biased random sampling algorithm for load balancing in cloud based systems
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
A PSO-Based algorithm for load balancing in virtual machines of cloud computing environment
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Optimal resource provisioning for cloud computing environment
The Journal of Supercomputing
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Applied Soft Computing
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International Journal of Web and Grid Services
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UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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The anticipated uptake of Cloud computing, built on well-established research in Web Services, networks, utility computing, distributed computing and virtualisation, will bring many advantages in cost, flexibility and availability for service users. These benefits are expected to further drive the demand for Cloud services, increasing both the Cloud's customer base and the scale of Cloud installations. This has implications for many technical issues in Service Oriented Architectures and Internet of Services (IoS)-type applications; including fault tolerance, high availability and scalability. Central to these issues is the establishment of effective load balancing techniques. It is clear the scale and complexity of these systems makes centralized assignment of jobs to specific servers infeasible; requiring an effective distributed solution. This paper investigates three possible distributed solutions proposed for load balancing; approaches inspired by Honeybee Foraging Behaviour, Biased Random Sampling and Active Clustering.