Adaptive rebuilding of encoded data slices in a storage network

1. A method for execution by a computing device of a storage network comprises:

obtaining, by the computing device, scoring information for a rebuilding of encoded data slices for one or more storage units of a set of storage units of the storage network, wherein the scoring information includes two or more of a plurality of rebuilding rates, a plurality of input/output rates, a plurality of scores, and a plurality of selection rates, wherein a selection rate of the plurality of selection rates includes a historic use of a particular rebuilding rate of the plurality of rebuilding rates for the set of storage units;

determining, by the computing device, a rebuilding rate of the plurality of rebuilding rates to utilize for the rebuilding based on the scoring information; and

implementing, by the computing device, the rebuilding of the encoded data slices in accordance with the rebuilding rate.

2. The method of claim 1 further comprises:

determining based on the scoring information, by the computing device, that a first input/output rate of the plurality of input/output rates for a first rebuilding rate of the plurality of rebuilding rates exceeds a difference threshold compared to an initial first input/output rate for the first rebuilding rate, wherein the initial first input/output rate has a corresponding first score and a first selection rate of the plurality of selection rates;

adjusting, by the computing device, a plurality of initial input/output rates based on the first input/output rate to produce a plurality of updated input/output rates; and

generating, by the computing device, an updated plurality of scores for the plurality of rebuilding rates based on the plurality of updated input/output rates to produce updated scoring information.

3. The method of claim 2 further comprises:

determining, by the computing device, an updated rebuilding rate of the plurality of rebuilding rates based on the updated scoring information; and

implementing, by the computing device, the rebuilding of the encoded data slices in accordance with the updated rebuilding rate.

4. The method of claim 2 , wherein the adjusting comprises:

applying a learning rate function to the plurality of initial input/output rates and the plurality of input/output rates to produce the plurality of updated input/output rates.

5. The method of claim 2 , wherein the adjusting comprises:

applying a first learning rate function for the first rebuilding rate to a first input/output rate of the plurality of initial input/output rates to produce a first updated input/output rate of the plurality of updated input/output rates; and

applying a second learning rate function for a second rebuilding rate to a second input/output rate of the plurality of initial input/output rates to produce a second updated input/output rate of the plurality of updated input/output rates.

6. The method of claim 2 further comprises:

calculating, by the computing device, an initial input/output rate for each rebuilding rate of the plurality of rebuilding rates to produce the plurality of initial input/output rates.

7. The method of claim 1 further comprises:

a first plurality of input/output rates correspond to a first storage unit of the set of storage units; and

a second plurality of input/output rates correspond to a second storage unit of the set of storage units.

8. The method of claim 1 , wherein the plurality of scores are based on a prioritization scheme of a plurality of prioritization schemes, and wherein a first plurality of scores for first scoring information is based on a first prioritization scheme and a second plurality of scores for second scoring information is based on a second prioritization scheme.

9. The method of claim 8 , wherein the plurality of prioritization schemes comprises:

a slice access priority scheme;

a compromise priority scheme; and

a rebuilding priority scheme.

10. A computing device of a storage network comprises:

memory;

an interface; and

a processing module, wherein the processing module is operably coupled to the memory and the interface, and wherein the processing module is operable to:

obtain scoring information for a rebuilding of encoded data slices for one or more storage units of a set of storage units of the storage network, wherein the scoring information includes two or more of a plurality of rebuilding rates, a plurality of input/output rates, a plurality of scores, and a plurality of selection rates, wherein a selection rate of the plurality of selection rates includes a historic use of a particular rebuilding rate of the plurality of rebuilding rates for the set of storage units;

determine a rebuilding rate of the plurality of rebuilding rates to utilize for the rebuilding based on the scoring information; and

implement the rebuilding of the encoded data slices in accordance with the rebuilding rate.

11. The computing device of claim 10 , wherein the processing module is further operable to:

determine based on the scoring information, that a first input/output rate of the plurality of input/output rates for a first rebuilding rate of the plurality of rebuilding rates exceeds a difference threshold compared to an initial first input/output rate for the first rebuilding rate, wherein the initial first input/output rate has a corresponding first score and a first selection rate of the plurality of selection rates;

adjust a plurality of initial input/output rates based on the first input/output rate to produce a plurality of updated input/output rates; and

generate an updated plurality of scores for the plurality of rebuilding rates based on the plurality of updated input/output rates to produce updated scoring information.

12. The computing device of claim 11 , wherein the processing module is further operable to:

determine an updated rebuilding rate of the plurality of rebuilding rates based on the updated scoring information; and

implement the rebuilding in accordance with the updated rebuilding rate.

13. The computing device of claim 11 , wherein the processing module is further operable to:

perform the adjusting by applying a learning rate function to the plurality of initial input/output rates and the plurality of input/output rates to produce the plurality of updated input/output rates.

14. The computing device of claim 11 , wherein the processing module is further operable to perform the adjusting by:

apply a first learning rate function for the first rebuilding rate to a first input/output rate of the plurality of initial input/output rates to produce a first updated input/output rate of the plurality of updated input/output rates; and

apply a second learning rate function for a second rebuilding rate to a second input/output rate of the plurality of initial input/output rates to produce a second updated input/output rate of the plurality of updated input/output rates.

15. The computing device of claim 11 , wherein the processing module is further operable to:

calculate an initial input/output rate for each rebuilding rate of the plurality of rebuilding rates to produce the plurality of initial input/output rates.

16. The computing device of claim 11 , wherein the processing module is further operable to:

a first plurality of input/output rates corresponding to a first storage unit of the set of storage units; and

a second plurality of input/output rates corresponding to a second storage unit of the set of storage units.

17. The computing device of claim 10 , wherein the processing module is further operable to:

calculate the plurality of scores based on a prioritization scheme of a plurality of prioritization schemes; and

calculate a first plurality of scores for first scoring information based on a first prioritization scheme and calculate a second plurality of scores for second scoring information based on a second prioritization scheme.

18. The computing device of claim 17 , wherein the plurality of prioritization schemes comprises:

a slice access priority scheme;

a compromise priority scheme; and

a rebuilding priority scheme.