Adaptive rebuilding rates based on sampling and inference

1. A method for execution by one or more processing modules of one or more computing devices of a dispersed storage network (DSN), the method comprises:

monitoring an encoded data slice access rate to produce an observed encoded data slice access rate for an associated rebuilding rate of a set of rebuilding rates;

applying a learning rate function to the observed encoded data slice access rate based on a previous observed encoded data slice access rate associated with the rebuilding rate to produce an updated previous observed encoded data slice access rate of a set of previous observed encoded data slice access rates, where the set of previous observed encoded data slice access rates corresponds to the set of rebuilding rates;

updating a score value associated with the updated previous observed encoded data slice access rate and the rebuilding rate, wherein the score value is calculated as: ((N*rebuild rate)+encoded data slice access rate) 2 , where N is a multiplier of the rebuild rate; and

selecting an encoded data slice access scheme based on the updated score value where a rebuild rate selection will maximize a score value associated with an expected encoded data slice access rate; and

rebuilding the encoded data slice in DSN storage using the encoded data slice access scheme.

2. The method of claim 1 further comprising updating the rebuilding rate by determining an encoded data slice access demand rate and a rebuilding access demand rate.

3. The method of claim 1 , wherein the encoded data slice access scheme is a priority encoded data slice access when an encoded data slice access demand rate is much greater than a rebuilding access demand rate.

4. The method of claim 1 , wherein the encoded data slice access scheme is a rebuilding priority scheme when a rebuilding access demand rate is much greater than an encoded data slice access demand rate.

5. The method of claim 1 , wherein the encoded data slice access scheme is a compromise scheme when a rebuilding access demand rate is substantially similar to an encoded data slice access demand rate.

6. The method of claim 5 further comprising selecting a rebuilding rate of the set of rebuilding rates that is less than a rebuilding access demand rate and maximizes a score value associated with an expected slice access rate.

7. The method of claim 1 , wherein the score value is calculated as:

((3*rebuild rate)+encoded data slice access rate) 2 .

8. The method of claim 1 , wherein the learning rate function is calculated as:

updated score=(old score)*(1−learning rate)+(new score*learning rate).

9. The method of claim 1 , wherein rebuilding access requests includes one or more of a list range request, a list digest of a range request, a read slice request, or a write rebuilt slice request.

10. The method of claim 1 , wherein slice access requests include at least one of a read request, a write request, a delete request, or a list request.

11. The method of claim 1 , wherein the monitoring includes at least one of: performing a test, initiating a query, or receiving access rate information.

12. The method of claim 1 , wherein determining to update the rebuilding rate for a storage unit is based on one or more of: detecting an end of a time interval, receiving a request, receiving an error message, or detecting an unfavorable slice access rate.

13. The method of claim 1 , wherein determining a slice access demand rate and rebuilding access demand rate is based on one or more of: interpreting a queue, receiving a request, or accessing a historical record.

14. The method of claim 1 , wherein selecting an encoded data slice access scheme is based on one or more of: a predetermination, detecting that a demand rate is much greater than a demand threshold level, or receiving a request.

15. A computing device of a group of computing devices of a dispersed storage network (DSN), the computing device comprises:

an interface;

a local memory; and

a processing module operably coupled to the interface and the local memory, wherein the processing module functions to:

monitor an encoded data slice access rate to produce an observed encoded data slice access rate for an associated rebuilding rate of a set of rebuilding rates;

apply a learning rate function to the observed encoded data slice access rate based on a previous observed encoded data slice access rate associated with the rebuilding rate to produce an updated previous observed encoded data slice access rate of a set of previous observed encoded data slice access rates, where the set of previous observed encoded data slice access rates corresponds to the set of rebuilding rates;

update a score value associated with the updated previous observed encoded data slice access rate and the rebuilding rate, wherein the score value is calculated as: ((N*rebuild rate)+encoded data slice access rate) 2 , where N is a multiplier of the rebuild rate; and

select an encoded data slice access scheme based on the updated score value where a rebuild rate selection will maximize a score value associated with an expected encoded data slice access rate; and

rebuild the encoded data slice in DSN storage using the encoded data slice access scheme.

16. The computing device of claim 15 , wherein the encoded data slice access scheme is any of: a priority encoded data slice access when an encoded data slice access demand rate is much greater than a rebuilding access demand rate; a rebuilding priority scheme when a rebuilding access demand rate is much greater than an encoded data slice access demand rate; or a compromise scheme when a rebuilding access demand rate is substantially similar to an encoded data slice access demand rate.

17. The computing device of claim 15 , wherein the learning rate function is calculated as:

updated score=(old score)*(1−learning rate)+(new score*learning rate).

18. An integrity processing unit for determining a rate to rebuild encoded data slices, stored within a dispersed storage network (DSN), the integrity processing unit comprises:

an interface;

a local memory; and

a processing module operably coupled to the interface and the local memory, wherein the processing module functions to:

at each time interval T, select a rebuild rate at which to rebuild data from a range of possible rebuild rates while monitoring an aggregate rate of I/O operations processed in T;

calculate a score value based on the rebuild rate and an I/O rate, the calculating providing a score value for each of a set of possible rebuild rates, the score value calculated as ((N*rebuild rate)+I/O rate) 2 , where N is a multiplier of the rebuild rate;

find a sum total of all the score values for each score value in the set;

divide each score value by this sum total to get weighted probabilities;

wherein, for each time interval T, the integrity processing unit uses the weighted probabilities for a selection of a next rebuild rate to use; and

rebuild the data in DSN storage using the next rebuild rate.