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Fault tolerance optimization using game theory approach in cloud computing
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DOI：10.1007/s10586-026-06280-w.png)
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Abstract
En 中文
Cloud computing has become an essential technology across many domains because of its flexible pay-per-use model and ability to offer scalable storage and services. Its main goal is to ensure quality of service while meeting service level agreements. However, as demand grows, the risk of node failures and service disruption also increases, affecting overall availability. To address this challenge, this paper proposes a novel approach called Bayesian-Trust and Stackelberg-Driven Genetic Migration (BTS-GM). The method combines trust evaluation, pricing strategy, and a hybrid game-theory model to improve fault tolerance and reduce failure risk. Historical performance is analyzed through a trust model to minimize completion time, while CPU downtime is monitored during simulation to avoid interruptions. The proposed method was tested using workload-trace-based simulation and compared with PSO-GTA, FP-TOSM, and ISMO under the same conditions. Results show that BTS-GM performs better in fault tolerance, node failure risk, and SLA violations, thereby improving service availability and system reliability.
Keywords:
Cloud computing
Virtual machine migration
Fault tolerance
SLA violations
Failure risk
BTS-GM (Bayesian-Trust and Stackelberg-Driven Genetic Migration)
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