Headless resilient backup and restore software ecosystem selecting server based on historical client latency, workload and metadata presence in cache

• US 10,324,849 B2
• Filed: 02/13/2017
• Issued: 06/18/2019
• Est. Priority Date: 02/13/2017
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bode, Christopher C.
• Primary Examiner(s): Del Mar Perez-Velez, Rocio
• Assistant Examiner(s): Yew, Chie
• Application Number:
  US15/431,105
• Publication Number:
  US 20180232162A1
• Time in Patent Office: 855 Days
• CPC Class Codes: G06F 11/1446 Point-in-time backing up or restoration of persistent data
• CPC Class Codes: G06F 11/1456 Hardware arrangements for backup
• CPC Class Codes: G06F 11/1461 Backup scheduling policy
• CPC Class Codes: G06F 11/1464 for networked environments
• CPC Class Codes: G06F 11/1469 Backup restoration techniques
• CPC Class Codes: G06F 12/0862 with prefetch
• CPC Class Codes: G06F 2212/1016 Performance improvement
• CPC Class Codes: G06F 2212/154 Networked environment
• CPC Class Codes: G06F 2212/163 Server or database system
• CPC Class Codes: G06F 2212/263 Network storage, e.g. SAN or NAS
• CPC Class Codes: G06F 2212/286 Mirrored cache memory
• CPC Class Codes: G06F 2212/6024 History based prefetching
• CPC Class Codes: H04L 67/1008 based on parameters of servers, e.g. available memory or workload monitoring of computer activity G06F11/30
• CPC Class Codes: H04L 67/1095 Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
• CPC Class Codes: H04L 67/1097 for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]

Abstract

Provided are techniques for a headless resilient backup and restore software ecosystem. At a first backup server of a plurality of backup servers, a connection request is received. At the first backup server, a second backup server is identified by: determining a backup server score for each of the plurality of backup servers based on identification factors comprising historical client latency, scheduled backup server workload, and whether the metadata is already cached on any of the plurality of backup servers and identifying the second backup server as having a lowest backup server score. The identification of the second backup server is returned.

Claims

1. A computer program product, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by at least one processor to perform:

performing load balancing at each of a plurality of backup servers in a headless backup and restore software ecosystem without one of the backup servers being in charge of the load balancing by;

receiving, at a first backup server of the plurality of backup servers, a connection request;

identifying, at the first backup server, a second backup server of the plurality of backup servers by;

determining a backup server score for each of the plurality of backup servers based on a weighted sum of identification factors comprising historical client latency, scheduled backup server workload, and whether metadata is already cached on any of the plurality of backup servers; and

identifying the second backup server as having a lowest backup server score among the backup server score for each of the plurality of backup servers; and

in response to the connection request, returning identification of the second backup server.

2. The computer program product of claim 1, wherein the identification factors have associated weights that are used to determine the weighted sum of the identification factors.

3. The computer program product of claim 1, wherein the program code is executable by at least one processor to perform:

at each of the plurality of backup servers, periodically determining a total pre-fetch score by summing a pre-fetch score of each of the plurality of backup servers, wherein the pre-fetch score of each of the plurality of backup servers comprises the historical client latency, the scheduled backup server workload, and a failure percentage; and

in response to determining that the total pre-fetch score does not exceed a maximum threshold, pre-fetching metadata on a backup server of the plurality of backup servers having a lowest pre-fetch score from the pre-fetch score of each of the plurality of backup servers.

4. The computer program product of claim 1, wherein the metadata is pre-fetched from a metadata repository to metadata cache.

5. The computer program product of claim 1, wherein the second backup server provides a client with a list of backup severs for use for future requests.

6. The computer program product of claim 1, wherein the plurality of backup servers are nodes in a cloud infrastructure that includes a metadata cache for each of the plurality of backup servers, backup data storage for each of the plurality of backup servers, metadata repositories shared by each of the plurality of backup servers, and common storage shared by each of the plurality of backup servers.

7. A computer system, comprising:

one or more processors, one or more memories and one or more computer readable storage media; and

program instructions, stored on at least one of the one or more computer readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to perform operations comprising;

performing load balancing at each of a plurality of backup servers in a headless backup and restore software ecosystem without one of the backup servers being in charge of the load balancing by;

receiving, at a first backup server of the plurality of backup servers, a connection request;

identifying, at the first backup server, a second backup server of the plurality of backup servers by;

determining a backup server score for each of the plurality of backup servers based on a weighted sum of identification factors comprising historical client latency, scheduled backup server workload, and whether metadata is already cached on any of the plurality of backup servers; and

identifying the second backup server as having a lowest backup server score among the backup server score for each of the plurality of backup servers; and

in response to the connection request, returning identification of the second backup server.

8. The computer system of claim 7, wherein the identification factors have associated weights that are used to determine the weighted sum of the identification factors.

9. The computer system of claim 7, wherein the operations further comprise:

at each of the plurality of backup servers, periodically determining a total pre-fetch score by summing a pre-fetch score of each of the plurality of backup servers, wherein the pre-fetch score of each of the plurality of backup servers comprises the historical client latency, the scheduled backup server workload, and a failure percentage; and

in response to determining that the total pre-fetch score does not exceed a maximum threshold, pre-fetching metadata on a backup server of the plurality of backup servers having a lowest pre-fetch score from the pre-fetch score of each of the plurality of backup servers.

10. The computer system of claim 7, wherein the metadata is pre-fetched from a metadata repository to metadata cache.

11. The computer system of claim 7, wherein the second backup server provides a client with a list of backup severs for use for future requests.

12. The computer system of claim 7, wherein the plurality of backup servers are nodes in a cloud infrastructure that includes a metadata cache for each of the plurality of backup servers, backup data storage for each of the plurality of backup servers, metadata repositories shared by each of the plurality of backup servers, and common storage shared by each of the plurality of backup servers.

13. A computer-implemented method, comprising:

performing load balancing at each of a plurality of backup servers in a headless backup and restore software ecosystem without one of the backup servers being in charge of the load balancing by;

receiving, at a first backup server of the plurality of backup servers, a connection request;

identifying, at the first backup server, a second backup server of the plurality of backup servers by;

determining a backup server score for each of the plurality of backup servers based on a weighted sum of identification factors comprising historical client latency, scheduled backup server workload, and whether metadata is already cached on any of the plurality of backup servers; and

identifying the second backup server as having a lowest backup server score among the backup server score for each of the plurality of backup servers; and

in response to the connection request, returning identification of the second backup server.

14. The computer-implemented method of claim 13, wherein the identification factors have associated weights that are used to determine the weighted sum of the identification factors.

15. The computer-implemented method of claim 13, further comprising:

at each of the plurality of backup servers, periodically determining a total pre-fetch score by summing a pre-fetch score of each of the plurality of backup servers, wherein the pre-fetch score of each of the plurality of backup servers comprises the historical client latency, the scheduled backup server workload, and a failure percentage; and

in response to determining that the total pre-fetch score does not exceed a maximum threshold, pre-fetching metadata on a backup server of the plurality of backup servers having a lowest pre-fetch score from the pre-fetch score of each of the plurality of backup servers.

16. The computer-implemented method of claim 13, wherein the metadata is pre-fetched from a metadata repository to metadata cache.

17. The computer-implemented method of claim 13, wherein the second backup server provides a client with a list of backup severs for use for future requests.

18. The computer-implemented method of claim 13, wherein the plurality of backup servers are nodes in a cloud infrastructure that includes a metadata cache for each of the plurality of backup servers, backup data storage for each of the plurality of backup servers, metadata repositories shared by each of the plurality of backup servers, and common storage shared by each of the plurality of backup servers.