An effective decentralized metadata management schema is critical to a reliable and scal-able large-scale distributed storage system. With regard to the defects of huge expansion cost and high sensitivity to cluster alteration borne in both the hash-based and subtree-based metadata management schemas, a metadata server (MDS) clustering schema based on con-sistent hash structure, named DSVL, is proposed. By introducing virtual MDS into the con-sistent MDS cluster, it can effectively balance MDS cluster load. Besides, the standby mecha-nism and the lazy update policy are merged and applied to the MDS cluster to accomplish rapid MDS failover as well as zero data migration in case of cluster alteration. The results of prototype system and simulation test prove that DSVL, with the features of balanced metadata distribution, rapid failover, flexible scalability and zero data migration amount in case of node alteration, can meet the demands for flexible and effective management of large-scale data storage systems with ever increasing data amount. © 2014 SERSC.

An effective decentralized metadata management schema is critical to a reliable and scal-able large-scale distributed storage system. With regard to the defects of huge expansion cost and high sensitivity to cluster alteration borne in both the hash-based and subtree-based metadata management schemas, a metadata server (MDS) clustering schema based on con-sistent hash structure, named DSVL, is proposed. By introducing virtual MDS into the con-sistent MDS cluster, it can effectively balance MDS cluster load. Besides, the standby mecha-nism and the lazy update policy are merged and applied to the MDS cluster to accomplish rapid MDS failover as well as zero data migration in case of cluster alteration. The results of prototype system and simulation test prove that DSVL, with the features of balanced metadata distribution, rapid failover, flexible scalability and zero data migration amount in case of node alteration, can meet the demands for flexible and effective management of large-scale data storage systems with ever increasing data amount. © 2014 SERSC.