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Task allocation systems in the Cloud have been recently proposed so that their performance is optimised in real-time based on reinforcement learning with spiking Random Neural Networks (RNN). In this paper, rather than reinforcement learning, we suggest the use of multi-layer neural network architectures to infer the state of servers in a dynamic networked Cloud environment, and propose to select...
Cloud systems include both locally based servers at user premises and remote servers and multiple Clouds that can be reached over the Internet. This paper describes a smart distributed system that combines local and remote Cloud facilities. It operates with a task allocation system that takes decisions to allocate tasks dynamically to the service that offers the best overall Quality of Service and...
The Cloud supports diverse workloads and simple schemes are needed to allocate jobs with satisfactory QoS and low overhead. This paper presents a further study on the potential of an online work distribution approach in adaptively distributing workloads under variable load conditions for optimizing the two contradictory criteria: reducing the energy consumption per job while maintaining the best possible...
We present experiments that compare three on-line real time techniques for task allocation to different cloud servers: an adaptive random neural network (RNN) based on reinforcement algorithm, an algorithm based on ``sensible routing'', one which uses a simple analytical model to select the server is estimated to give the best response as a function of workload, and round-robin task allocation. Measurements...
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