GRAPHICAL REPRESENTATION OF CLASSIFICATION OF WORKLOADS

• US 20150026582A1
• Filed: 07/18/2013
• Published: 01/22/2015
• Est. Priority Date: 07/18/2013
• Status: Active Grant

• Current Assignee: NetApp, Inc.
• Original Assignee: NetApp, Inc.
• Inventors: Basak, Jayanta
• Granted Patent: US 9,276,821 B2
• Time in Patent Office: Days
• US Class Current: 715/734
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 43/045 for graphical visualisation of monitoring data

Abstract

Technology is disclosed for graphically representing classification of workloads in a storage system. Workload classification is graphically represented to the user by mapping workload signatures of the workloads to a grid. When the workload signatures are mapped to the grid, a number of clusters are formed in the grid. Each of the clusters represents workloads of a particular category. Mapping the workload signature to the grid includes mapping a high-dimensionality workload signature vector to a low-dimensionality grid.

Claims

1. A method, comprising:

receiving, at a processing device in a network storage system, workload signatures corresponding to workloads in the network storage system, each of the workload signatures being a vector having a first dimensionality;

mapping, by the processing device, each of the workload signatures to a grid of a second dimensionality, the second dimensionality being lower than the first dimensionality, the mapping including

identifying, using a topography mapping technique and based on the vector of a particular workload signature, a particular cell of the grid that matches with the vector, and

assigning the particular workload signature to the particular cell; and

generating, by the processing device, a graphical representation of classification of the workloads using the grid, the grid containing clusters, the clusters formed by mapping the workload signatures, each of the clusters including a group of cells of the grid and identifying a category of the workloads whose workload signatures are assigned to the group of cells one of the clusters.

2. The method of claim 1, wherein mapping the workload signatures includes mapping the workload signatures with similar vectors to adjacent cells in a common region of the grid, the region forming one of the clusters.

3. The method of claim 2 further comprising:

receiving, at regular intervals of time, a new set of workload signatures of the workloads; and

updating, at regular intervals of time, the graphical representation to indicate the classification of the workloads at a given interval of time.

4. The method of claim 2, further comprising:

generating, by the processing device, a report including analytics of the workloads in the network storage system.

5. The method of claim 1, wherein mapping the workload signature to the grid of a second dimensionality includes:

calibrating, by the processing device and using the predefined topographic mapping technique, the grid using a sample data set, the sample data set including multiple sample workload signatures of multiple sample workloads, the sample workloads categorized into predefined categories, the sample data set including at least one sample workload signature in each of the categories, the calibrating including

for each of the categories,

assigning the sample workload signatures to cells in the grid, the cells forming a first cluster in the grid, and

identifying the first cluster as representing the corresponding category of the sample workload.

6. The method of claim 5, wherein calibrating the grid includes calibrating the grid before mapping the workload signatures to the grid.

7. The method of claim 1, wherein receiving the workload signatures includes

sampling, by the processing device and for a specified duration, values for parameters of a workload of the workloads,

determining, based at least in part on the sampled values, a subset of the parameters for predicting a response time of a network storage controller executing the workload, the response time being the time required for the network storage controller to respond to a service request from the particular workload, and

generating, by the processing device, the vector of the workload signature as a function of the values of the parameters, the response time, and the subset of the parameters.

8. The method of claim 7, wherein the parameters include at least one of

a percentage of read operations of a total operation performed by the corresponding workload;

a percentage of write operations of the total operations performed by the corresponding workload;

a percentage of the total operations that are neither read nor write performed by the corresponding workload;

an average size of data read by the corresponding workload;

an average size of data written by the corresponding workload;

a percentage of random read operations of a total real operation performed by the corresponding workload;

or

a percentage of random write operations of a total write operation performed by the corresponding workload.

9. The method of claim 8, wherein at least some of the parameters of the workload signatures mapped to the same cluster have similar values.

10. The method of claim 7, wherein the first dimensionality of the vector is of size sixty four.

11. The method of claim 10, wherein the parameters are represented in the vector as sixty four bits.

12. The method of claim 1, wherein the grid of the second dimensionality is a two-dimensional grid of a predefined size.

13. The method of claim 1, wherein each of the workloads is executing on at least one of a disk of a network storage controller, an aggregate of the network storage controller, or a volume of the aggregate.

14. The method of claim 1 further comprising:

receiving a new set of workload signatures of a new set of workloads;

mapping, using the predefined topography mapping technique, the new set of workload signatures to the grid;

determining if a new cluster is formed by mapping the new set of workload signatures; and

responsive to a determination that the new cluster is formed in the grid, identifying the new cluster as representing a new category of the new set of workloads, the new category being different from categories of previously mapped workload signatures.

15. A computer-readable storage medium storing instructions for classifying workloads, comprising:

instructions for generating, at a processing device in a network storage system, workload signatures for workloads executing in the network storage system, each of the workload signatures being a vector of a first dimensionality, the generating including;

for each of the workloads,

sampling, for a specified duration, values for parameters of a workload of the workloads,

determining, based at least in part on the sampled values, a subset of the parameters that is significant for predicting a response time of a network storage controller executing the workload, the response time being the time required to respond to a service request from the workload, and

generating the vector of the workload signature as a function of the values of the parameters, the response time, and the subset of the parameters.

16. The computer-readable storage medium of claim 15 further comprising:

instructions for mapping, by the processing device, the workload signatures to a grid of a second dimensionality, the second dimensionality being lower than the first dimensionality, the mapping including;

assigning, using a topography mapping technique, each of the workload signatures to a cell of the grid, and

forming clusters in the grid, each of the clusters including a group of cells of the grid, and each of the clusters identifying a category of the workloads whose workload signatures are assigned to the group of cells of the cluster.

17. The computer-readable storage medium of claim 16, wherein assigning each of the workload signatures to one of the cells includes

identifying, based on the vector of the workload signature, a particular cell of the grid that matches with the vector, and

assigning the workload signature to the particular cell.

18. The computer-readable storage medium of claim 16 further comprising:

creating, by the processing device, a graphical representation of a classification of the workloads using the grid; and

transmitting, by the processing device, the graphical representation to another processing device in the network storage system requesting the graphical representation.

19. The computer-readable storage medium of claim 15, wherein the parameters of each of the workload signatures includes at least one of:

a percentage of read operations of a total operation performed by the corresponding workload;

a percentage of write operations of the total operations performed by the corresponding workload;

a percentage of the total operations that are neither read nor write performed by the corresponding workload;

an average size of data read by the corresponding workload;

an average size of data written by the corresponding workload;

a percentage of random read operations of a total real operation performed by the corresponding workload;

or

a percentage of random write operations of a total write operation performed by the corresponding workload.

20. A system, comprising:

a storage system having a storage device;

a component configured to receive workload signatures of workloads in the network storage system, each of the workload signatures being a vector of a first dimensionality;

a component configured to map, each of the workload signatures to a grid of a second dimensionality, the second dimensionality being lower than the first dimensionality, the mapping including

identifying, based on the vector of a particular workload signature and using a predefined topography mapping technique, a particular cell of the grid that matches with the vector, and

assigning the particular workload signature to the particular cell; and

a component configured to generate, by the processing device, a graphical representation of classification of the workloads using the grid, the grid containing clusters, the clusters formed as a result of mapping the workload signatures, each of the clusters including a group of cells of the grid, and each of the clusters identifying a category of the workloads whose workload signatures are assigned to the group of cells of in the corresponding cluster.

21. The system of claim 20, wherein the memory further includes instructions, which, when executed by the processor, causes the processor to execute the method including

receiving a new set of workload signatures;

mapping, using the topography mapping technique, the new set of workload signatures to the grid;

determining if a new cluster is formed by mapping the new set of workload signatures; and

responsive to a determination that the new cluster is formed in the grid, identifying the new cluster as representing a new category of the workload, the new category of the workload being different from categories for previously mapped workload signatures.