IP Library › Granted Patent US 11,232,009
Granted Patent B2
US 11,232,009 · App. 16/112,379 · Granted Jan 25, 2022

Model-based key performance indicator service for data analytics processing platforms

Inventors: Sudhir Vijendra (Westborough, MA); Shashidhar Krishnaswamy (White Plains, NY)
Assignee: EMC IP Holding Company LLC
G06F11/3409G06F11/3024
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Quick Facts
Patent No.
US 11,232,009
App. No.
16/112,379
Granted
Jan 25, 2022
Kind
B2
Abstract

A processing platform includes a plurality of processing devices each including a processor coupled to a memory, and is configured to communicate over at least one network with one or more data sources. The processing platform is further configured to receive input data from the one or more data sources, to identify an instance of a key performance indicator (KPI) management data model associated with the received input data, and to perform a particular KPI service on the received input data in accordance with the identified instance of the KPI management data model. Performing the particular KPI service illustratively includes: utilizing the identified instance of the KPI management data model to extract one or more metrics from the received input data and to compute one or more KPIs based at least in part on the one or more extracted metrics; and generating analytics output including the one or more computed KPIs for the received input data.

Claims (54)

1. An apparatus comprising:

a processing platform comprising a plurality of processing devices each comprising a processor coupled to a memory;

the processing platform being configured to communicate over a network with one or more data sources, the one or more data sources comprising a plurality of Internet of Things (IoT) sensors;

the processing platform being further configured:

to receive input data from the one or more data sources;

to identify first and second distinct instances of a key performance indicator (KPI) management data model associated with respective first and second portions of the received input data; and

to perform particular first and second distinct KPI services on the respective first and second portions of the received input data in accordance with the respective first and second identified instances of the KPI management data model;

wherein performing the particular first and second KPI services comprises:

utilizing the first identified instance of the KPI management data model to extract one or more first metrics from the first portion of the received input data and to compute one or more first KPIs based at least in part on the one or more extracted first metrics;

utilizing the second identified instance of the KPI management data model to extract one or more second metrics from the second portion of the received input data and to compute one or more second KPIs based at least in part on the one or more extracted second metrics;

generating analytics output comprising the one or more computed first KPIs and the one or more computed second KPIs for the received input data; and

automatically altering an allocation of one or more of network, storage and compute resources associated with the processing platform to obtain a particular configuration based at least in part on the analytics output;

wherein automatically altering an allocation of one or more of network, storage and compute resources associated with the processing platform to obtain a particular configuration based at least in part on the analytics output comprises one or more of adjusting a number of running containers or virtual machines for one or more applications, migrating containers or virtual machines for one or more applications from one host device to another, and modifying an amount of compute, storage and network resources that are allocated to a given application; and

wherein the one or more extracted first metrics and the one or more extracted second metrics each comprise one or more of a resource utilization metric and an application metric.

2. The apparatus of claim 1 wherein the processing platform is configured to perform the particular first and second KPI services in accordance with the respective first and second instances of the KPI management data model with each such first and second instances having a plurality of fields arranged in a predefined format common to the respective first and second instances.

3. The apparatus of claim 1 wherein at least one of the first and second identified instances of the KPI management data model comprises a plurality of fields including at least a type field specifying whether the corresponding first or second portion of the received input data comprises real-time data or batch data.

4. The apparatus of claim 1 wherein at least one of the first and second identified instances of the KPI management data model comprises a plurality of fields including at least a window field specifying a particular type of computational window.

5. The apparatus of claim 4 wherein the window field identifies utilization of a particular one of a sliding window and a tumbling window for computation of the one or more KPIs.

6. The apparatus of claim 1 wherein at least one of the first and second identified instances of the KPI management data model comprises a plurality of fields including at least a filters field specifying one or more filters to be applied to the corresponding first or second portion of the received input data.

7. The apparatus of claim 1 wherein at least one of the first and second identified instances of the KPI management data model comprises a plurality of fields including at least an input field identifying the one or more sources of the corresponding first or second portion of the received input data and an output field identifying at least one destination for the analytics output comprising the corresponding one or more computed first or second KPIs.

8. The apparatus of claim 1 wherein the processing platform comprises an analytics engine configured to perform the particular first and second KPI services on the respective first and second portions of the received input data in accordance with the respective first and second identified instances of the KPI management data model.

9. The apparatus of claim 1 wherein the processing platform comprises a time-series database configured to perform the particular first and second KPI services on the respective first and second portions of the received input data in accordance with the respective first and second identified instances of the KPI management data model.

10. The apparatus of claim 1 wherein the processing platform is configured to provide the analytics output comprising the one or more computed first and second KPIs to a message broker.

11. The apparatus of claim 1 wherein the processing platform is configured to provide an application programming interface through which at least portions of at least one instance of the KPI management data model are configurable by a system user.

12. The apparatus of claim 1 wherein the processing platform is configured to permit at least one instance of the KPI management data model to be shared by multiple distinct cloud infrastructure tenants.

13. The apparatus of claim 1 wherein the received input data comprises at least one of real-time streaming data received from one or more real-time data sources and batch data retrieved from one or more databases or data lakes.

14. A method comprising:

receiving input data from one or more data sources, the one or more data sources comprising a plurality of Internet of Things (IoT) sensors;

identifying first and second distinct instances of a key performance indicator (KPI) management data model associated with respective first and second portions of the received input data; and

performing particular first and second distinct KPI services on the respective first and second portions of the received input data in accordance with the respective first and second identified instances of the KPI management data model;

wherein performing the particular first and second KPI services comprises:

utilizing the first identified instance of the KPI management data model to extract one or more first metrics from the first portion of the received input data and to compute one or more first KPIs based at least in part on the one or more extracted first metrics;

utilizing the second identified instance of the KPI management data model to extract one or more second metrics from the second portion of the received input data and to compute one or more second KPIs based at least in part on the one or more extracted second metrics;

generating analytics output comprising the one or more computed first KPIs and the one or more computed second KPIs for the received input data; and

automatically altering an allocation of one or more of network, storage and compute resources associated with the processing platform to obtain a particular configuration based at least in part on the analytics output;

wherein automatically altering an allocation of one or more of network, storage and compute resources associated with the processing platform to obtain a particular configuration based at least in part on the analytics output comprises one or more of adjusting a number of running containers or virtual machines for one or more applications, migrating containers or virtual machines for one or more applications from one host device to another, and modifying an amount of compute, storage and network resources that are allocated to a given application;

wherein the one or more extracted first metrics and the one or more extracted second metrics each comprise one or more of a resource utilization metric and an application metric; and

wherein the method is implemented by a processing platform comprising a plurality of processing devices each comprising a processor coupled to a memory.

15. The method of claim 14 further comprising performing the particular first and second KPI services in accordance with the respective first and second instances of the KPI management data model with each such first and second instances having a plurality of fields arranged in a predefined format common to the respective first and second instances.

16. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by a processing platform comprising a plurality of processing devices, each such processing device comprising a processor coupled to a memory, causes the processing platform:

to receive input data from one or more data sources, the one or more data sources comprising a plurality of Internet of Things (IoT) sensors;

to identify first and second distinct instances of a key performance indicator (KPI) management data model associated with respective first and second portions of the received input data; and

to perform particular first and second distinct KPI services on the respective first and second portions of the received input data in accordance with the respective first and second identified instances of the KPI management data model;

wherein performing the particular first and second KPI services comprises:

utilizing the first identified instance of the KPI management data model to extract one or more first metrics from the first portion of the received input data and to compute one or more first KPIs based at least in part on the one or more extracted first metrics;

utilizing the second identified instance of the KPI management data model to extract one or more second metrics from the second portion of the received input data and to compute one or more second KPIs based at least in part on the one or more extracted second metrics;

generating analytics output comprising the one or more computed first KPIs and the one or more computed second KPIs for the received input data; and

automatically altering an allocation of one or more of network, storage and compute resources associated with the processing platform to obtain a particular configuration based at least in part on the analytics output;

wherein automatically altering an allocation of one or more of network, storage and compute resources associated with the processing platform to obtain a particular configuration based at least in part on the analytics output comprises one or more of adjusting a number of running containers or virtual machines for one or more applications, migrating containers or virtual machines for one or more applications from one host device to another, and modifying an amount of compute, storage and network resources that are allocated to a given application; and

wherein the one or more extracted first metrics and the one or more extracted second metrics each comprise one or more of a resource utilization metric and an application metric.

17. The computer program product of claim 16 wherein the processing platform is configured to perform the particular first and second KPI services in accordance with the respective first and second instances of the KPI management data model with each such first and second instances having a plurality of fields arranged in a predefined format common to the respective first and second instances.

18. The computer program product of claim 16 wherein the processing platform comprises an analytics engine configured to perform the particular first and second KPI services on the respective first and second portions of the received input data in accordance with the respective first and second identified instances of the KPI management data model.

19. The computer program product of claim 16 wherein the processing platform comprises a time-series database configured to perform the particular first and second KPI services on the respective first and second portions of the received input data in accordance with the respective first and second identified instances of the KPI management data model.

20. The computer program product of claim 16 wherein at least one of the first and second identified instances of the KPI management data model comprises a plurality of fields including at least a metrics field specifying the one or more first or second metrics to be extracted in performing the corresponding particular first or second KPI services and a calculation field specifying the manner in which the one or more first or second KPIs are to be computed based at least in part on the one or more extracted first or second metrics.

Assignments (4)
RELEASE OF SECURITY INTEREST IN PATENTS PREVIOUSLY RECORDED AT REEL/FRAME (053546/0001) Recorded Jun 23, 2022
From: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A., AS NOTES COLLATERAL AGENT
To: DELL MARKETING L.P. (ON BEHALF OF ITSELF AND AS SUCCESSOR-IN-INTEREST TO CREDANT TECHNOLOGIES, INC.); DELL INTERNATIONAL L.L.C.; DELL PRODUCTS L.P.; DELL USA L.P.; EMC CORPORATION; DELL MARKETING CORPORATION (SUCCESSOR-IN-INTEREST TO FORCE10 NETWORKS, INC. AND WYSE TECHNOLOGY L.L.C.); EMC IP HOLDING COMPANY LLC
Reel/Frame 071642/0001 →
SECURITY AGREEMENT Recorded Apr 22, 2020
From: CREDANT TECHNOLOGIES INC.; DELL INTERNATIONAL L.L.C.; DELL MARKETING L.P.; DELL PRODUCTS L.P.; DELL USA L.P.; EMC CORPORATION; FORCE10 NETWORKS, INC.; WYSE TECHNOLOGY L.L.C.; EMC IP HOLDING COMPANY LLC
To: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A.
Reel/Frame 053546/0001 →
SECURITY AGREEMENT Recorded Mar 21, 2019
From: CREDANT TECHNOLOGIES, INC.; DELL INTERNATIONAL L.L.C.; DELL MARKETING L.P.; DELL PRODUCTS L.P.; DELL USA L.P.; EMC CORPORATION; FORCE10 NETWORKS, INC.; WYSE TECHNOLOGY L.L.C.; EMC IP HOLDING COMPANY LLC
To: THE BANK OF NEW YORK MELLON TRUST COMPANY, N.A.
Reel/Frame 049452/0223 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 24, 2018
From: VIJENDRA, SUDHIR; KRISHNASWAMY, SHASHIDHAR
To: EMC IP HOLDING COMPANY LLC
Reel/Frame 046701/0310 →
Continuity (1)
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