Optimization of Cloud-Based Multi-Agent System for Trade-Off Between Trustworthiness of Data and Cost of Data Usage

This paper considers the cloud-based multi-agent system (MAS) in which for any agent to obtain the most trustworthy data (MTD) that best matches the agents' personalized demands on the trusted attributes of data, the agents' data usage often comes with cost, and the better the trustworthiness of data (TOD), the higher the cost of data usage (CDU). Therefore, under the environments where the budgets for data usage are constrained, there may exist a trade-off between the TOD and CDU for the cloud-based MAS, and how to optimally make such trade-off arises as an interesting issue in practice. To address this issue, this paper first formulates the trade-off as a mathematical constrained optimization problem, then explores out the theoretical foundations, both using the fuzzy similarity computing, and finally proposes an algorithm to guarantee the agents to obtain the MTD within the acceptable level of CDU. The experiment results illustrate its effectiveness. Note to Practitioners - This paper addresses the interesting trade-off between TOD and CDU for the cloud-based MAS that operates in the cloud environments where the TOD and CDU are positively correlated with each other. By establishing the mathematical constrained optimization model and designing the algorithm for the MTD recommendation within the acceptable CDU based on the theoretical foundations using fuzzy similarity computing, it helps the MAS with cloud settings to achieve the MTD that best matches their personalized demands on the trusted attributes within the acceptable CDU, which could push the development of cloud-based MAS by benefiting from the optimal trade-off between TOD and CDU. The experiments show that the solution proposed here outperforms existing solutions.