HYPERPARAMETER AND NETWORK TOPOLOGY SELECTION IN NETWORK DEMAND FORECASTING

• US 20150288573A1
• Filed: 04/08/2014
• Published: 10/08/2015
• Est. Priority Date: 04/08/2014
• Status: Active Grant

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: BAUGHMAN, Aaron K., O'CONNELL, Brian M., PERLITZ, Michael, VAN DER STOCKT, Stefan A. G.
• Granted Patent: US 10,439,891 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: H04L 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 41/147 for predicting network behaviour

Abstract

Approaches for optimizing network demand forecasting models and network topology using hyperparameter selection are provided. An approach includes defining a pool of features that are usable in models that predict demand of network resources, wherein the pool of features includes at least one historical forecasting feature and at least one event forecasting feature. The approach also includes generating, using a computer device, an optimal model using a subset of features selected from the pool of features. The approach further includes predicting future demand on a network using the optimal model. The approach additionally includes allocating resources in the network based on the predicted future demand.

Claims

1. A method of allocating resources in a networked environment, comprising:

defining a pool of features that are usable in models that predict demand of network resources, wherein the pool of features includes at least one historical forecasting feature and at least one event forecasting feature;

generating, using a computer device, an optimal model using a subset of features selected from the pool of features;

predicting future demand on a network using the optimal model; and

allocating resources in the network based on the predicted future demand.

2. The method of claim 1, wherein the defining the pool of features comprises:

defining a set of preprocessing features;

defining a set of historical forecasting features including the at least one historical forecasting feature;

defining a set of post-processing features; and

defining a set of event forecasting features including the at least one event forecasting feature.

3. The method of claim 2, wherein:

the set of preprocessing features includes a plurality of preprocessing functions;

the set of historical forecasting features includes a plurality of historical forecasters;

the set of post-processing features includes a plurality of post-processing functions; and

the set of event forecasting features includes a plurality of event forecasters.

4. The method of claim 3, wherein the subset of features selected includes:

one of the plurality of preprocessing functions, one of the plurality of historical forecasters, one of the plurality of post-processing functions, and one of the plurality of event forecasters.

5. The method of claim 3, wherein:

the plurality of historical forecasters predict cyclic demand; and

the plurality of event forecasters predict spike demand.

6. The method of claim 1, further comprising identifying the subset of features using an evolutionary algorithm process.

7. The method of claim 6, wherein the evolutionary algorithm process comprises:

defining a plurality of chromosomes based on combinations of features from the pool of features;

evaluating a fitness of each one of the plurality of chromosomes; and

ranking the plurality of chromosomes based on the evaluated fitness.

8. The method of claim 7, wherein, based on the ranking, a highest ranked one of the plurality of chromosomes includes a combination of features that defines the subset of features used in the optimal model.

9. The method of claim 1, wherein:

the resources in the network are website servers; and

the predicted future demand is predicted traffic on a website.

10. The method of claim 1, wherein the allocating the resources in the network comprises sending at least one instruction to at least one of the website servers to be available for a predicted increase in traffic on the website.

11. The method of claim 1, wherein a service provider at least one of creates, maintains, deploys and supports computer infrastructure that includes the computer device.

12. The method of claim 1, wherein steps of claim 1 are provided by a service provider on a subscription, advertising, and/or fee basis.

13. A computer program product for allocating resources in a networked environment, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions being executable by a computer device to cause the computer device to:

select, by the computer device, a subset of features from a pool of features that includes at least one historical forecasting feature and at least one event forecasting feature;

generate, by the computer device, an optimal model using the subset of features;

predict, by the computer device, future demand on a network using the optimal model; and

allocate, by the computer device, resources in the network based on the predicted future demand.

14. The computer program product of claim 13, wherein the pool of features includes:

a plurality of preprocessing functions;

a plurality of historical forecasters including the at least one historical forecasting feature;

a plurality of post-processing functions; and

a plurality of event forecasters including the at least one event forecasting feature.

15. The computer program product of claim 14, wherein:

the plurality of historical forecasters predict cyclic demand; and

the plurality of event forecasters predict spike demand.

16. The computer program product of claim 13, wherein the program instructions cause the computer device to identify the subset of features using an evolutionary algorithm process.

17. The computer program product of claim 13, wherein:

the resources in the network are website servers;

the predicted future demand is predicted traffic on a website during a sporting event; and

the allocating the resources comprises sending at least one instruction to at least one of the website servers to be available for a predicted increase in traffic on the website.

18. A system for determining an optimal resource allocation in a networked environment, comprising:

a CPU, a computer readable memory and a computer readable storage medium;

program instructions to define a plurality of logical partitions of network resources;

program instructions to determine the optimal resource allocation using an evolutionary algorithm process and the plurality of logical partitions; and

program instructions to allocate network resources based on the determined optimal resource allocation,

wherein the program instructions are stored on the computer readable storage medium for execution by the CPU via the computer readable memory.

19. The system of claim 18, wherein the plurality of logical partitions are defined based on historic data of a real cloud network.

20. The system of claim 18, wherein:

the evolutionary algorithm process includes defining chromosomes;

each one of the chromosomes represents a respective cloud with a unique network topology; and

respective ones of the plurality of logical partitions and type of probabilistic events are genes on the chromosomes.