Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24574
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning
Authors: Ullah, Amjad
Li, Jingpeng
Hussain, Amir
Yang, Erfu
Contact Email: jli@cs.stir.ac.uk
Keywords: Cloud elasticity
Dynamic resource provisioning
Fuzzy logic
Basal ganglia
Soft switching
Auto-scaling
Elastic feedback controller
Issue Date: Oct-2016
Citation: Ullah A, Li J, Hussain A & Yang E (2016) Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning, Cognitive Computation, 8 (5), pp. 992-1005.
Abstract: Cloud elasticity augments applications to dynamically adapt to changes in demand by acquiring or releasing computational resources on the fly. Recently, we developed a framework for cloud elasticity utilizing multiple feedback controllers simultaneously, wherein, each controller determines the scaling action with different intensity, and the selection of an appropriate controller is realized with a fuzzy inference system. In this paper, we aim to identify the similarities between cloud elasticity and action selection mechanism in the animal brain. We treat each controller in our previous framework as an action, and propose a novel bioinspired, soft switching approach. The proposed methodology integrates a basal ganglia computational model as an action selection mechanism. Initial experimental results demonstrate the improved potential of the basal ganglia-based approach by enhancing the overall system performance and stability.
DOI Link: http://dx.doi.org/10.1007/s12559-016-9391-y
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Publisher policy allows this work to be made available in this repository; Published in Cognitive Computation, October 2016, Volume 8, Issue 5, pp 992–1005. The original publication is available at Springer via http://dx.doi.org/10.1007/s12559-016-9391-y

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