Open AccessQoS and QoE aware multi objective resource allocation algorithm for cloud gaming
Author(s) -
Koné Kigninman Désiré,
Eya Dhib,
Nabil Tabbane,
Olivier Asseu
Publication year - 2021
Publication title -
journal of high speed networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 21
eISSN - 1875-8940
pISSN - 0926-6801
DOI - 10.3233/jhs-210655
Subject(s) - computer science , harmony search , cloud computing , quality of experience , resource allocation , quality of service , algorithm , fitness function , distributed computing , computer network , artificial intelligence , machine learning , genetic algorithm , operating system
Cloud gaming is an innovative model that congregates video games. The user may have different Quality-of-Experience (QoE), which is a term used to measure a user’s level of satisfaction and enjoyment for a particular service. To guarantee general satisfaction for all users with limited cloud resources, it becomes a major issue in the cloud. This paper leverages a game theory in the cloud gaming model with resource optimization to discover optimal solutions to resolve resource allocation. The Rider-based harmony search algorithm (Rider-based HSA), which is the combination of Rider optimization algorithm (ROA) and Harmony search algorithm (HSA), is proposed for resource allocation to improve the cloud computing system’s efficiency. The fitness function is newly devised considering certain QoE parameters, which involves fairness index, Quantified experience of players (QE), and Mean Opinion Score (MOS). The proposed Rider-based HSA showed better performance compared to Potential game-based optimization algorithm, Proactive resource allocation algorithm, QoE-aware resource allocation algorithm, Distributed algorithm, and Yusen Li et al., with maximal fairness of 0.999, maximal MOS of 0.873, and maximal QE of 1.