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Information can be shared across the Internet using cloud computing, a powerful paradigm for meeting the needs of individuals and organizations. To minimize access time and maximize load balancing for data nodes (DNs), a dynamic data replication algorithm is necessary. Even so, few of the existing algorithms consider each objective holistically during replication. An improved ant lion optimizer (ALO) algorithm and a fuzzy system are used in this paper to determine dynamically the number of replicas and the DNs for replication. Further, it balances the trade-offs among different objectives (e.g., service time, system availability, load, and monetary cost). The ALO algorithm has been widely applied to solve complex optimization problems due to its simplicity in implementation. However, ALO has premature convergence and can thus easily get trapped into the local optimum solution. In this paper, to overcome the …