Research Area: Edge Computing
In earlier works [1] , [2] , we proposed to utilize a centralized broker-node to perform task scheduling for the resource augmentation of a large number of mobile devices. The task scheduler model focused on energy optimization was proposed for the centralized task scheduling problem. In this paper, the model extends the optimization process by including an economic element to it. Thus, we propose an energy and monetary cost-aware mathematical task scheduler model. Compared to the previous model, this model, can allow mobile devices to offload multiple tasks to cloud resources. The results in this paper are more thorough and more aspects of task offloading have been analysed. For instance, the model is evaluated under two different resource augmentation environments for mobile cloud computing: a local private cloud and public clouds. More precisely, the task scheduling problem is optimally solved to minimize: (i) the total energy consumption when applied to a local private cloud, and (ii) the total energy consumption and monetary cost when applied to public clouds. Our proposed model at the centralized broker-node finds optimal solutions for task assignment problem, and provides a significant reduction in the total costs compared with the task assignment by the centralized scheduler without optimization.
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Author(s) Name: Manjinder Nir,Ashraf Matrawy and Marc St-Hilaire
Journal name: IEEE Transactions on Cloud Computing
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TCC.2015.2469665
Volume Information: Jan.-March 2018, pp. 99-113, vol. 6
