Adaptive memory load management in cloud data centers
IBM Journal of Research and Development
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The anticipated uptake of Cloud computing, built on the well-established research fields of 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 customer base and the scale of cloud installations. This has implications for many technical issues in such Service Oriented Architectures and Internet of Services (IoS) type applications; fault tolerance, high availability and scalability for examples. Central to these issues is the establishment of effective load balancing techniques. It is clear that the scale and complexity of these systems makes centralized individual assignment of jobs to specific servers infeasible; leading to the need for an effective distributed solution. This paper investigates three possible distributed solutions, which have been proposed for load balancing: An approach inspired by the foraging behaviour of the Honeybee, Biased Random Sampling and Active Clustering.