Data correlation and fuzzy inference system-based data replication in federated cloud systems

Federated cloud is a promising solution for cloud providers that enables collaboration and leasing resources among multiple cloud providers. However, resource management in such system is very challenging, as each cloud provider must maintain its own economic profit while meeting the requirement of service level agreement (SLA). In this respect, we propose a dynamic and periodic data replication strategy in federated cloud systems. It aims to guarantee the monetary profit of a cloud provider while satisfying its users’ requirements in terms of response time and minimum availability. To identify replicas, we perform a periodical analysis of the users’ tasks using the spectral clustering technique to extract the existing correlations between remote data related to SLA violations. The adaptation of such correlations can significantly reduce data transfer amount and the time required to transfer it, thereby reducing tasks’ response time and the number of future SLA violations. Then, we rely on a fuzzy inference system to place the groups of replicas considering four main parameters to choose replicas placements among his owned or leased resources from other providers. Furthermore, a replicas number adjustment is performed when SLA is satisfied over time. To demonstrate the efficiency of our strategy, performance of the proposed strategy are compared alongside existing single-clouds-based and interconnected-clouds-based data replication strategies. The obtained results indicate that our strategy decreases the amount of SLA violations while preserving the monetary profit of providers.