Big data is an emerging term in the storage industry, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale storage systems, load balancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improving quality of services. Many good
approaches have been proposed for load balancing in distributed storage systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request distributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication. In this paper, we propose Cloud Cache (C2), an adaptive load balancing scheme for metadata server cluster in Cloud-scale storage systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balancing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. By conducting a performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C2.