Method to optimize restore based on data protection workload prediction
AssigneeEMC IP Holding Company LLC
InventorsBing Liu, George Mathew
Life of this patent
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Abstract
An intelligent method of selecting a data recovery site upon receiving a data recovery request. The backup system collects historical activity data of the storage system to identify work load of every data recovery site. A predicted activity load for each data recovery site is then generated using the collected data. When a request for data recovery is received, the system first identifies which data recovery site has copies of the files to be recovered. Then it uses the predicted work load for these data recovery sites to determine whether to use a geographically local site or a site that may be remote geographically, but has a lower work load.
Claims20
In a backup system comprising a backup manager and a plurality of data recovery (DR) sites, each DR site storing backup files, a method of directing restore requests, the method comprising:
- collecting backup activity data of each site for a period of time including read, write, replication, stream, and usage of CPU and I/O load on every replication set from each of the DR sites to generate a prediction of future activity load for each DR site by using K-means method and a machine learning (ML) by splitting a total data set into a test training set and a test testing set with a ratio of 7:3;
- generating the prediction of future activity load for each DR site by using the activity data to predict future activity load for each site, wherein the prediction is to predict workload status for each site for a set period time;
- receiving a restore request;
- determining which of the sites stores required backup files by using the future activity load based on the prediction to determine which of the sites has low activity load when no preferred DR site is selected by a user;
- and
- forwarding the restore request to the site having the low activity load.
A system comprising:
- a plurality of storage systems distributed in a plurality of data recovery (DR) sites;
- an analytical engine;
- a backup manager;
- a load balancer;
- wherein the analytical engine, backup manager, and load balancer employ one or more processors;
- and
- a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to perform the steps:
- collect backup activity data of each of the DR sites for a period of time including read, write, replication, stream, and usage of CPU and I/O load on every replication set from each of the DR sites to generate a prediction of future activity load for each DR site by using K-means method and a machine learning (ML) by splitting a total data set into a test training set and a test testing set with a ratio of 7:3;
- generate the prediction of future activity load for each DR site by using the activity data to predict future activity load for each of the DR sites, wherein the prediction is to predict workload status for each site for a set period time;
- upon receiving a restore request, determine which of the DR sites stores required backup files by using the future activity load based on the prediction to determine which of the sites has low activity load when no preferred DR site is selected by a user;
- and
- forward the restore request to the DR site having the low activity load.
A computer program product comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein to be executed by one or more processors, the program code operating in a computing cluster and including instructions to:
- collect backup activity data of each of the DR sites for a period of time including read, write, replication, stream, and usage of CPU and I/O load on every replication set from each of the DR sites to generate a prediction of future activity load for each DR site by using K-means method and a machine learning (ML) by splitting a total data set into a test training set and a test testing set with a ratio of 7:3;
- generate the prediction of future activity load for each DR site by using the activity data to predict future activity load for each of the DR sites, wherein the prediction is to predict workload status for each site for a set period time;
- upon receiving a restore request, determine which of the DR sites stores required backup files by using the future activity load based on the prediction to determine which of the sites has low activity load when no preferred DR site is selected by a user;
- and
- forward the restore request to the DR site having the low activity load.
Dependencies are inferred from the claim text.