Techniques for service execution and monitoring for run-time service composition

Patent metadata

Field	Value
Publication number	US-12039004-B2
Application number	US-202017019254-A
Country	US
Kind code	B2
Filing date	Sep 12, 2020
Priority date	Sep 14, 2019
Publication date	Jul 16, 2024
Grant date	Jul 16, 2024

Abstract

A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.

First claim

What is claimed is:

1. A method for automating a run-time adaption of a multi-objective optimization model in a software architecture, the method comprising:
accessing the multi-objective optimization model, wherein the multi-objective optimization model is configured to access one or more library components that are configured based on a user data schema and one or more data ontologies;
receiving two or more Quality of Service (QoS) dimensions for the multi-objective optimization model at run-time,
wherein QoS dimensions include a response time, latency, throughput, availability, or success rate,
wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension;

maximizing the multi-objective optimization model along the first QoS dimension at run-time,
wherein the maximizing includes selecting two or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension,
wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, the ordering of the pipelines includes ranking the two or more pipelines,
wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and
whereby there is a tradeoff between the first QoS dimension and the second QoS dimension;

detecting a predicted change in performance or a predicted output of the multi-objective optimization model; and
transmitting a notification indicating the predicted change.

2. The method of claim 1 , further comprising:
retrieving data associated with a historical output of the multi-objective optimization model;
receiving one or more inputs from an environment-monitoring agent, wherein the environment-monitoring agent receives information on at least one of: resources of a system, concepts of the multi-objective optimization model, data corruption, and data availability to the multi-objective optimization model;
determining a change in at least one of: the resources of the system, the concepts of the multi-objective optimization model, the data corruption, and the data availability to the multi-objective optimization model;
determining whether the change in the at least one of the resources of the system, the concepts of the multi-objective optimization model, the data corruption, and the data availability to the multi-objective optimization model will cause a predicted output of the multi-objective optimization model to vary more than a predetermined amount;
when the change in the at least one of the resources of the system, the concepts of the multi-objective optimization model, the data corruption, and the data availability to the multi-objective optimization model cause the predicted output of the multi-objective optimization model to vary more than a predetermined amount, identifying one or more remedial measures to the multi-objective optimization model to correct for the change; and
displaying an alert to notify a user of the change in the at least one of the resources of the system, the concepts of the multi-objective optimization model, the data corruption, and the data availability to the multi-objective optimization model and the one or more remedial measures.

3. The method of claim 2 , wherein the predicted output includes at least one of first metrics related to a performance of the multi-objective optimization model in relation to Quality of Service parameters and second metrics related to predictions of the multi-objective optimization model as compared with the historical output of the multi-objective optimization model.

4. The method of claim 2 , further comprising executing the one or more remedial measures to the multi-objective optimization model to correct for the change.

5. The method of claim 2 , wherein the resources of the system comprises at least one of available memory, processing nodes, and network bandwidth.

6. The method of claim 2 , wherein the concepts measure a statistical distribution of a performance of the multi-objective optimization model.

7. The method of claim 2 , wherein the one or more remedial measures to the multi-objective optimization model includes reducing a complexity of the multi-objective optimization model.

8. The method of claim 2 , wherein the one or more remedial measures to the multi-objective optimization model includes eliminating one or more features affected by the data corruption.

9. The method of claim 2 , wherein the one or more remedial measures to the multi-objective optimization model includes evaluating impact of new features on the predicted output.

10. The method of claim 2 , wherein the one or more remedial measures to the multi-objective optimization model includes rolling back the multi-objective optimization model to a previous version.

11. A non-transitory computer-readable medium storing instructions for automating a run-time adaption of a multi-objective optimization model in a software architecture, the instructions comprising:
one or more instructions that, when executed by one or more processors, cause the one or more processors to:
access the multi-objective optimization model, wherein the multi-objective optimization model is configured to access one or more library components that are configured based on a user data schema and one or more data ontologies;
receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model at run-time,
wherein QoS dimensions include a response time, latency, throughput, availability, or success rate,
wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension;

maximize the multi-objective optimization model along the first QoS dimension at run-time,
wherein the maximizing includes selecting two or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension,
wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, the ordering of the pipelines includes ranking the two or more pipelines,
wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and
whereby there is a tradeoff between the first QoS dimension and the second QoS dimension;

detect a predicted change in performance or a predicted output of the multi-objective optimization model; and
transmit a notification indicating the predicted change.

12. The non-transitory computer-readable medium of claim 11 , further comprising instructions that, when executed by one or more processors, cause the one or more processors to:
retrieve data associated with a historical output of the multi-objective optimization model;
receive one or more inputs from an environment-monitoring agent, wherein the environment-monitoring agent receives information on at least one of: resources of a system, concepts of the multi-objective optimization model, data corruption, and data availability to the multi-objective optimization model;
determine a change in at least one of: the resources of the system, the concepts of the multi-objective optimization model, the data corruption, and the data availability to the multi-objective optimization model;
determine whether the change in the at least one of the resources of the system, the concepts o

Assignees

• Oracle Int Corp

Inventors

• Polleri Alberto
• Lopez Sergio Aldea
• Bron Marc Michiel
• Golding Dan David
• Ioannides Alexander
• Mestre Maria Del Rosario
• Monteiro Hugo Alexandre Pereira
• Shevelev Oleg Gennadievich
• Suzuki Larissa Cristina Dos Santos Romualdo
• Zhao Xiaoxue
• Rowe Matthew Charles

Classifications

• H04L9/0894 Primary

  Escrow, recovery or storing of secret information, e.g. secret key escrow or cryptographic key storage · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06F18/2155

  characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling · CPC title

• G06F18/2115

  by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination · CPC title

• G06F18/10

  Pre-processing; Data cleansing · CPC title