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Performance-aware cost-effective resource provisioning for future grid iot-cloud system
Li, Weiling Liao, Kewen He, Qiang Xia, Yunni
Li, Weiling
Liao, Kewen
He, Qiang
Xia, Yunni
Abstract
The rise of the future grid (FG) largely depends on the efficient integration of Internet of Things (IoT) and Cloud computing technologies. By utilizing information and control flows, FG can deliver power more effectively and be capable to handle events occurring anywhere in the grid network. However, maintaining such functions consumes a great deal of computational resource which brings an enormous operational cost to the grid owner. In this paper, we propose an integrated task scheduling and resource provisioning model for dynamically operating an IoT-Cloud system to reduce the overall operational cost. Our proposed approach uses a bipartite graph to model the communication pattern between sensor groups and decentralized cloud data centers and a Pareto distribution-based method to estimate the required resources considering capacity limitation and failure of the system in each data center. We formulate the integrated model as a constraint optimization problem over all sensor groups and data centers. We solve the problem with genetic algorithms due to problem complexity, and our extensive computer simulations and comparisons demonstrate the correctness and effectiveness of the proposed model in minimizing operational cost while satisfying system performance requirements.
Keywords
morphodynamics, bed slope, bedload, morphological diffusion
Date
2019
Type
Journal article
Journal
Journal of Energy Engineering
Book
Volume
145
Issue
5
Page Range
2-13
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled