Dynamic SLA negotiation and conflict resolution in multi-cloud infrastructures using reinforcement learning and Nash bargaining

Key Points

• The aim is to explore how reinforcement learning and Nash bargaining can optimize SLA negotiations and conflict resolution in multi-cloud environments.
• Utilized reinforcement learning algorithms to model SLA negotiation processes.
• Implemented Nash bargaining to facilitate conflict resolution in multi-cloud scenarios.
• Conducted simulations to evaluate the efficiency of proposed strategies.
• Significant improvement in SLA adherence observed with the proposed approach, achieving a 20% increase in efficiency (p<0.05).
• Reduced conflict resolution time by 30% using Nash bargaining methods.
• Higher user satisfaction scores were reported, indicating effective SLA management.

Abstract