# US 2014/0237210 A1 · Capacity forecasting for backup storage

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- Number: US 2014/0237210 A1
- Document ID: US20140237210A1
- Status: Published application
- Kind: A1
- Application number: 14/175,328
- Filed: 2014-02-07
- Published: 2014-08-21
- Earliest claimed: 2011-12-22
- Assignees: EMC Corporation
- Applicants: EMC Corporation
- Inventors: Mark Chamness
- Web page: https://patentgrep.com/patent/US20140237210A1

## CPC

- G06F 12/023: {Free address space management}
- G06F 3/06: Digital input from, or digital output to, record carriers {, e.g. RAID, emulated record carriers or networked record carriers}
- G06F 3/0605: {by facilitating the interaction with a user or administrator}
- G06F 3/065: {Replication mechanisms}
- G06F 3/067: {Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]}
- G06F 11/1446: Point-in-time backing up or restoration of persistent data
- G06F 11/1448: {Management of the data involved in backup or backup restore}
- G06F 11/1451: {by selection of backup contents}
- G06F 11/1453: {using de-duplication of the data}
- G06F 11/3034: {where the computing system component is a storage system, e.g. DASD based or network based (digital input from or digital output to record carriers G06F3/06; digital recording or reproducing G11B20/18; 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], H04L67/1097)}
- G06F 11/3452: {Performance evaluation by statistical analysis}
- G06F 12/00: Accessing, addressing or allocating within memory systems or architectures (digital input from, or digital output to record carriers, e.g. to disk storage units, G06F3/06)
- G06F 12/02: Addressing or allocation; Relocation (program address sequencing G06F9/00; arrangements for selecting an address in a digital store G11C8/00)
- G06N 5/048: Fuzzy inferencing
- G06F 2201/84: Using snapshots, i.e. a logical point-in-time copy of the data
- G06F 2212/1044: Space efficiency improvement
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A system for capacity forecasting for backup storage comprises a processor and a memory. The processor is configured to calculate a set of statistical analyses for subsets of a set of capacities at points in time. The processor is further configured to determine a selected statistical analysis from the set of statistical analyses. The processor is further configured to forecast a full capacity time based at least in part of the selected statistical analysis. The memory is coupled to the processor and configured to provide the processor with instructions.

## Claims

### Claim 1

A system for capacity forecasting, comprising:
a processor configured to:
calculate a set of statistical analyses for subsets of a set of capacities at points in time;
determine a selected statistical analysis from the set of statistical analyses; and
forecast a full capacity time based at least in part on the selected statistical analysis; and;
a memory coupled to the processor and configured to provide the processor with instructions.

### Claim 2 (depends on 1)

A system as in claim 1, wherein the set of capacities at points in time comprise a set of capacities that are moving averages of raw capacities over time.

### Claim 3 (depends on 1)

A system as in claim 1, wherein the processor is further configured to receive a set of capacities at points in time.

### Claim 4 (depends on 1)

A system as in claim 1, wherein the capacities comprise deduplicating system capacities.

### Claim 5 (depends on 1)

A system as in claim 1, wherein subsets comprise differently sized sets of capacities extending into the past from the current time.

### Claim 6 (depends on 1)

A system as in claim 1, wherein the statistical analysis comprises a linear regression, and wherein calculating a set of statistical analysis for subsets of a set of capacities at points in time additionally comprises calculating R squared for each statistical analysis of the set of statistical analyses.

### Claim 7 (depends on 1)

A system as in claim 1, wherein the statistical analysis comprises a linear regression, and wherein determining comprises selecting the statistical analysis associated with a peak value of R squared.

### Claim 8 (depends on 1)

A system as in claim 1, wherein the statistical analysis comprises a linear regression, and wherein determining comprises selecting the statistical analysis associated with a first local maximum value of R squared.

### Claim 9 (depends on 1)

A system as in claim 1, wherein the processor is further configured to determine whether the full capacity time is qualified.

### Claim 10 (depends on 8)

A system as in claim 8, wherein the statistical analysis comprises a linear regression, and wherein determining whether the full capacity time is qualified comprises determining whether the peak value of R squared is greater than a predetermined threshold.

### Claim 11 (depends on 10)

A system as in claim 10, wherein the predetermined threshold comprises 0.9.

### Claim 12 (depends on 8)

A system as in claim 8, wherein the statistical analysis comprises a linear regression, and wherein determining whether the full capacity time is qualified comprises determining whether the selected statistical analysis has a positive slope.

### Claim 13 (depends on 8)

A system as in claim 8, wherein determining whether the full capacity time is qualified comprises determining whether the full capacity time is less than N years in the future.

### Claim 14 (depends on 8)

A system as in claim 8, wherein determining whether the full capacity time is qualified comprises determining whether the points in time comprise at least M days.

### Claim 15 (depends on 8)

A system as in claim 8, wherein determining whether the full capacity time is qualified comprises determining whether the points in time comprise at least P data points.

### Claim 16 (depends on 8)

A system as in claim 8, wherein determining whether the full capacity time is qualified is comprises determining whether each capacity of the set of capacities indicates at least L percentage used.

### Claim 17 (depends on 8)

A system as in claim 8, wherein the statistical analysis comprises a linear regression, and wherein determining whether the full capacity time is qualified comprises determining whether a most recent one or more capacity points falls close to the selected statistical analysis.

### Claim 18 (depends on 16)

A system as in claim 16, wherein the most recent one or more capacity points comprises the most recent one capacity point.

### Claim 19

A method for capacity forecasting comprising:
calculating, using a processor, a set of statistical analyses for subsets of a set of capacities at points in time;
determining a selected statistical analysis from the set of statistical analysis; and
forecasting a full capacity time based at least in part on the selected statistical analysis.

### Claim 20

A computer program product for capacity forecasting, the computer program product being embedded in a non-transitory computer readable storage medium and comprising computer instructions for:
calculating, using a processor, a set of statistical analyses for subsets of a set of capacities at points in time;
determining a selected statistical analysis from the set of statistical analysis; and
forecasting a full capacity time based at least in part on the selected statistical analysis.