EXPLOITING NODE-LOCAL DEDUPLICATION IN DISTRIBUTED STORAGE SYSTEM
Data deduplication is carried out in a storage system in which a set of volumes of data is distributed among a plurality of servers. The technique comprises computing a similarity metric among volumes of the set, making a determination that a difference in the similarity metric is less than a predetermined threshold value. Responsively to the determination there is a migration of the data of the volumes of the set within their respective servers to distribute the migrated data in like manner in the respective servers. Thereafter data deduplication is performed on the respective servers.
1 . A method of data deduplication comprising the steps of:
in a storage system comprising a plurality of servers having a set of volumes of data distributed therein, computing a similarity metric among volumes of the set;
making a determination that a difference in the similarity metric is less than a predetermined threshold value;
responsively to the determination migrating the data of the volumes of the set within their respective servers to distribute the migrated data in like manner in the respective servers; and
thereafter performing data deduplication on the respective servers.
2 . The method according to claim 1 , wherein the volumes of data are distributed among the volumes according to a pseudorandom striping scheme, wherein the volumes of data have respective seeds, wherein migrating the data comprises the steps of:
changing the seeds of the volumes of the set to new seeds; and
redistributing the data of the volumes of the set according to the new seeds.
3 . The method according to claim 2 , further comprising creating thin-provisioned copies of the volumes by copying local deduplication metadata, wherein the volumes and the copies have a common routing.
4 . The method according to claim 1 , wherein computing a similarity metric comprises determining that I/O requests to the volumes of the set have been made within a time interval that is shorter than a predetermined threshold.
5 . The method according to claim 1 , wherein computing a similarity metric comprises determining that data in I/O requests to the volumes of the set have a difference in a data similarity metric that is less than a predetermined data similarity threshold value.
6 . The method according to claim 1 , further comprising performing a cost-benefit analysis of deduplicating the volumes of the set, wherein the step of migrating the data is performed responsively to the cost-benefit analysis.
7 . The method according to claim 6 , wherein the cost-benefit analysis comprises calculating a deduplication ratio resulting from deduplication of the volumes of the set.
8 . A data processing apparatus comprising:
a storage system comprising a plurality of servers having a set of volumes of data distributed therein, wherein at least one of the servers is configured for performing the steps of:
computing a similarity metric among volumes of the set;
making a determination that a difference in the similarity metric is less than a predetermined threshold value;
responsively to the determination migrating the data of the volumes of the set within their respective servers to distribute the migrated data in like manner in the respective servers; and
thereafter performing data deduplication on the respective servers.
9 . The apparatus according to claim 8 , wherein the volumes of data are distributed among the volumes according to a pseudorandom striping scheme, wherein the volumes of data have respective seeds, wherein migrating the data comprises the steps of:
changing the seeds of the volumes of the set to new seeds; and
redistributing the data of the volumes of the set according to the new seeds.
10 . The apparatus according to claim 8 , wherein the at least one of the servers is operative for creating thin-provisioned copies of the volumes by copying local deduplication metadata, wherein the volumes and the copies have a common routing.
11 . The apparatus according to claim 8 , wherein computing a similarity metric comprises determining that I/O requests to the volumes of the set have been made within a time interval that is shorter than a predetermined threshold.
12 . The apparatus according to claim 8 , wherein computing a similarity metric comprises determining that data in I/O requests to the volumes of the set have a difference in a data similarity metric that is less than a predetermined data similarity threshold value.
13 . The apparatus according to claim 8 , further comprising performing a cost-benefit analysis of deduplicating the volumes of the set, wherein the step of migrating the data is performed responsively to the cost-benefit analysis.
14 . The apparatus according to claim 13 , wherein the cost-benefit analysis comprises calculating a deduplication ratio resulting from deduplication of the volumes of the set.