Open AccessStochastic QoE-Aware Optimization in Cloud-Based Content Delivery Networks
Author(s) -
Ali A. Haghighi,
Shahram Shahbazpanahi,
Shahram Shah Heydari
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2845740
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The problem of cloud resource optimization is examined, while the uncertainty in demand and user feedback is considered. We propose a Markov decision process model for resource assignment in cloud-based content delivery networks. Furthermore, we include a feedback-based probabilistic model for quality of experience in the resource assignment problem. We apply dynamic programming to solve this stochastic optimization problem. In order to address the challenges regarding the computational complexity of the problem, we first present an optimal solution with linear complexity for a special case of unlimited bandwidth cloud sites. Then, we propose a sub-optimal algorithm for the generic bandwidth-constrained problem with significantly reduced complexity and quasi-optimal performance. Simulation results are presented to corroborate the merits of the proposed algorithms.
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