US-12625731-B2 - Systems and methods for hypergraph edge resource demand knowledge management

Abstract

Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. A time period in which an application or task is operating based on initial demand resource load values that are provided by a customer may be inaccurate, which may expose sub-standard execution. Embodiments herein seek to significantly mitigate the potential of sub-standard execution. Embodiments collect a repository of resource demand load usage data over a time period that can be used to accurately determine the statistical moments of uncertain resource demand load. In one or more embodiments, a repository of hypervector and/or hyperspace representations may be generated and used to help with resource demand load estimation.

Inventors

• William Jeffery White
• Said Tabet

Assignees

• DELL PRODUCTS L.P.

Dates

Publication Date

20260512

Application Date

20230807

Claims (20)

1. 1 . A processor-implemented method comprising: for each edge site from a set of edge sites, obtaining an edge hypervector representation for handling a task at the edge site by performing steps comprising: collecting resource-related data associated with handling the task at the edge site; using a dataset comprising the resource-related data associated with handling the task to determine resource statistics for one or more edge resources for the task; and forming the edge hypervector representation comprising values related to one or more of the resource statistics for the one or more edge resources for handling the task; storing the edge hypervector representation in a hypergraph; and indexing the edge hypervector representation of the hypergraph to facilitate searching.
2. 2 . The processor-implemented method of claim 1 wherein each edge hypervector representation comprises stationary drift data related to at least one of the one or more resource statistics.
3. 3 . The processor-implemented method of claim 1 wherein the task is one of a plurality of tasks and the method further comprises: forming an edge hypervector representation for each task from the plurality of tasks; and storing the edge hypervector representations for the plurality of tasks in the hypergraph.
4. 4 . The processor-implemented method of claim 3 further comprising: clustering the edge hypervector representations into a plurality of clusters.
5. 5 . The processor-implemented method of claim 4 wherein the edge hypervector representations are clustered based upon one or more factors and the method further comprises: generating a hyperspatial representation to represent the clustered edge hypervector representations; and indexing the hyperspatial representation.
6. 6 . The processor-implemented method of claim 5 wherein the one or more factors used for clustering comprises task, similarity measure of representations, one or more edge site attributes, or a combination thereof.
7. 7 . The processor-implemented method of claim 4 further comprising: forming one or more higher tier hyperspatial representations by performing steps comprising: clustering hyperspatial representations into a plurality of clusters; generating a hyperspatial representation to represent the clustered hyperspatial representations; and indexing the generated hyperspatial representation.
8. 8 . The processor-implemented method of claim 1 wherein the step of using a dataset comprising the resource-related data associated with handling the task to determine resource statistics for one or more edge resources for the task comprises: using the dataset comprising the resource-related data associated with handling the task and a M-PCM-OFFD (Multivariate Probabilistic Collocation Method-Orthogonal Fractional Factorial Design) methodology to obtain the resource statistics for the one or more edge resources for the task.
9. 9 . The processor-implemented method of claim 1 further comprising: given a query related to a new task to be performed by an edge site: using the index of the hypergraph to obtain search results comprising a set of representations that comprises one or more candidate edge hypervector representations, one or more candidate hyperspatial representations, or both that are matched or closely matched to the query; selecting one of the representations in the search results to use as a proxy edge hypervector representation for the new task; and using one or more resource demand values associated with the proxy edge hypervector representation for an edge operational process for the new task.
10. 10 . One or more information handling systems collectively comprising: one or more processors; and one or more non-transitory computer-readable media comprising one or more sets of instructions which, when executed by at least one of the one or more processors, causes steps to be performed comprising: for each edge site from a set of edge sites, obtaining an edge hypervector representation for handling a task at the edge site by performing steps comprising: collecting resource-related data associated with handling the task at the edge site; using a dataset comprising the resource-related data associated with handling the task to determine resource statistics for one or more edge resources for the task; and forming the edge hypervector representation comprising values related to one or more of the resource statistics for the one or more edge resources for handling the task; storing the edge hypervector representation in a hypergraph; and indexing the edge hypervector representation of the hypergraph to facilitate searching.
11. 11 . The one or more information handling systems of claim 10 wherein the task is one of a plurality of tasks and at least one of the one or more non-transitory computer-readable media further comprises one or more sets of instructions which, when executed by at least one of the one or more processors, causes steps to be performed comprising: forming an edge hypervector representation for each task from the plurality of tasks; and storing the edge hypervector representations for the plurality of tasks in the hypergraph.
12. 12 . The one or more information handling systems of claim 11 wherein at least one of the one or more non-transitory computer-readable media further comprises one or more sets of instructions which, when executed by at least one of the one or more processors, causes steps to be performed comprising: clustering the edge hypervector representations into a plurality of clusters.
13. 13 . The one or more information handling systems of claim 12 wherein the edge hypervector representations are clustered based upon one or more factors and at least one of the one or more non-transitory computer-readable media further comprises one or more sets of instructions which, when executed by at least one processor, causes steps to be performed comprising: generating a hyperspatial representation to represent the clustered edge hypervector representations; and indexing the hyperspatial representation.
14. 14 . The one or more information handling systems of claim 10 wherein the step of using a dataset comprising the resource-related data associated with handling the task to determine resource statistics for one or more edge resources for the task comprises: using the dataset comprising the resource-related data associated with handling the task and a M-PCM-OFFD (Multivariate Probabilistic Collocation Method-Orthogonal Fractional Factorial Design) methodology to obtain the resource statistics for the one or more edge resources for the task.
15. 15 . The one or more information handling systems of claim 10 further comprising: given a query related to a new task to be performed by an edge site: using the index of the hypergraph to obtain search results comprising a set of representations that comprises one or more candidate edge hypervector representations, one or more candidate hyperspatial representations, or both that are matched or closely matched to the query; selecting one of the representations in the search results to use as a proxy edge hypervector representation for the new task; and using one or more resource demand values associated with the proxy edge hypervector representation for an edge operational process for the new task.
16. 16 . A non-transitory computer-readable medium or media comprising one or more sequences of instructions which, when executed by at least one processor, causes steps to be performed comprising: for each edge site from a set of edge sites, obtaining an edge hypervector representation for handling a task at the edge site by performing steps comprising: collecting resource-related data associated with handling the task at the edge site; using a dataset comprising the resource-related data associated with handling the task to determine resource statistics for one or more edge resources for the task; and forming the edge hypervector representation comprising values related to one or more of the resource statistics for the one or more edge resources for handling the task; storing the edge hypervector representation in a hypergraph; and indexing the edge hypervector representation of the hypergraph to facilitate searching.
17. 17 . The non-transitory computer-readable medium or media of claim 16 wherein the task is one of a plurality of tasks and at least one of the one or more non-transitory computer-readable media further comprises one or more sets of instructions which, when executed by at least one processor, causes steps to be performed comprising: forming an edge hypervector representation for each task from the plurality of tasks; and storing the edge hypervector representations for the plurality of tasks in the hypergraph.
18. 18 . The non-transitory computer-readable medium or media of claim 16 wherein the edge hypervector representations are clustered based upon one or more factors and at least one of the one or more non-transitory computer-readable media further comprises one or more sets of instructions which, when executed by at least one processor, causes steps to be performed comprising: generating a hyperspatial representation to represent the clustered edge hypervector representations; and indexing the hyperspatial representation.
19. 19 . The non-transitory computer-readable medium or media of claim 18 , wherein at least one of the one or more non-transitory computer-readable media further comprises one or more sets of instructions which, when executed by at least one processor, causes steps to be performed comprising: forming one or more higher tier hyperspatial representations by performing steps comprising: clustering hyperspatial representations into a plurality of clusters; generating a hyperspatial representation to represent the clustered hyperspatial representations; and indexing the generated hyperspatial representation.
20. 20 . The non-transitory computer-readable medium or media of claim 16 , wherein at least one of the one or more non-transitory computer-readable media further comprises one or more sets of instructions which, when executed by at least one processor, causes steps to be performed comprising: given a query related to a new task to be performed by an edge site: using the index of the hypergraph to obtain search results comprising a set of representations that comprises one or more candidate edge hypervector representations, one or more candidate hyperspatial representations, or both that are matched or closely matched to the query; selecting one of the representations in the search results to use as a proxy edge hypervector representation for the new task; and using one or more resource demand values associated with the proxy edge hypervector representation for an edge operational process for the new task.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This patent application is related to and claims priority benefit under 35 USC § 119(e) to commonly-owned U.S. Pat. App. No. 63/450,237, filed on 6 Mar. 2023, entitled “EDGE RESOURCE UTILIZATION,” and listing William Jeffery White and Said Tabet as inventors, which patent document is incorporated by reference herein in its entirety and for all purposes. This patent application is a continuation-in-part of and claims priority benefit under 35 USC § 120 to co-pending and commonly-owned U.S. patent application Ser. No. 18/355,351, filed on 19 Jul. 2023, entitled “EDGE DOMAIN-SPECIFIC ACCELERATOR VIRTUALIZATION AND SCHEDULING,” and listing William Jeffery White and Said Tabet as inventors, which patent document is incorporated by reference herein in its entirety and for all purposes. This patent application is also related to the following commonly-owned patent documents: U.S. patent application Ser. No. 18/366,461, filed on 7 Aug. 2023, entitled “SYSTEMS AND METHODS FOR EDGE SYSTEM RESOURCE CAPACITY PERFORMANCE PREDICTION,” and listing William Jeffery White and Said Tabet as inventors, which patent document is incorporated by reference herein in its entirety and for all purposes;U.S. patent application Ser. No. 18/366,490, filed on 7 Aug. 2023, entitled “SYSTEMS AND METHODS FOR EDGE SYSTEM RESOURCE CAPACITY DYNAMIC POLICY PLANNING FRAMEWORK,” and listing William Jeffery White and Said Tabet as inventors, which patent document is incorporated by reference herein in its entirety and for all purposes;U.S. patent application Ser. No. 18/366,507, filed on 7 Aug. 2023, entitled “SYSTEMS AND METHODS FOR HYPERGRAPH EDGE RESOURCE DEMAND LOAD REPRESENTATION,” and listing William Jeffery White and Said Tabet as inventors, which patent document is incorporated by reference herein in its entirety and for all purposes;U.S. patent application Ser. No. 18/366,538, filed on 7 Aug. 2023, entitled “SYSTEMS AND METHODS FOR CONTINUED EDGE RESOURCE DEMAND LOAD ESTIMATION,” and listing William Jeffery White and Said Tabet as inventors, which patent document is incorporated by reference herein in its entirety and for all purposes;U.S. patent application Ser. No. 18/366,549, filed on 7 Aug. 2023, entitled “SYSTEMS AND METHODS FOR EDGE RESOURCE DEMAND LOAD ESTIMATION,” and listing William Jeffery White and Said Tabet as inventors, which patent document is incorporated by reference herein in its entirety and for all purposes; andU.S. patent application Ser. No. 18/366,555, filed on 7 Aug. 2023, entitled “SYSTEMS AND METHODS FOR EDGE RESOURCE DEMAND LOAD SCHEDULING,” and listing William Jeffery White and Said Tabet as inventors, which patent document is incorporated by reference herein in its entirety and for all purposes. BACKGROUND A. Technical Field The present disclosure relates generally to information handling systems. More particularly, the present disclosure relates to edge platforms. B. Background The subject matter discussed in the background section shall not be assumed to be prior art merely as a result of its mention in this background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also be inventions. As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to users is information handling systems. An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, information handling systems may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in information handling systems allow for information handling systems to be general or configured for a specific user or specific use, such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, information handling systems may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems. Multi-cloud edge platforms are large-scale distributed systems that enable organizations to manage and optimize their computing resources across multiple cloud environments and edge devices. Typically, these p