US 12,386,918 B2
Techniques for service execution and monitoring for run-time service composition
Alberto Polleri, London (GB); Sergio Aldea Lopez, London (GB); Marc Michiel Bron, London (GB); Dan David Golding, London (GB); Alexander Ioannides, London (GB); Maria del Rosario Mestre, London (GB); Hugo Alexandre Pereira Monteiro, London (GB); Oleg Gennadievich Shevelev, London (GB); Larissa Cristina Dos Santos Romualdo Suzuki, London (GB); Xiaoxue Zhao, London (GB); and Matthew Charles Rowe, London (GB)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on May 31, 2024, as Appl. No. 18/680,987.
Application 18/680,987 is a continuation of application No. 17/019,254, filed on Sep. 12, 2020, granted, now 12,039,004.
Claims priority of provisional application 62/900,537, filed on Sep. 14, 2019.
Prior Publication US 2024/0320303 A1, Sep. 26, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G06F 8/75 (2018.01); G06F 8/77 (2018.01); G06F 11/30 (2006.01); G06F 11/34 (2006.01); G06F 16/21 (2019.01); G06F 16/23 (2019.01); G06F 16/2457 (2019.01); G06F 16/28 (2019.01); G06F 16/36 (2019.01); G06F 16/901 (2019.01); G06F 16/9035 (2019.01); G06F 16/907 (2019.01); G06F 18/10 (2023.01); G06F 18/2115 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06N 3/082 (2023.01); G06N 5/01 (2023.01); G06N 5/025 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); H04L 9/08 (2006.01); H04L 9/32 (2006.01)
CPC G06F 18/213 (2023.01) [G06F 8/75 (2013.01); G06F 8/77 (2013.01); G06F 11/3003 (2013.01); G06F 11/3409 (2013.01); G06F 11/3433 (2013.01); G06F 11/3452 (2013.01); G06F 11/3466 (2013.01); G06F 16/211 (2019.01); G06F 16/2365 (2019.01); G06F 16/24573 (2019.01); G06F 16/24578 (2019.01); G06F 16/285 (2019.01); G06F 16/367 (2019.01); G06F 16/9024 (2019.01); G06F 16/9035 (2019.01); G06F 16/907 (2019.01); G06F 18/10 (2023.01); G06F 18/2115 (2023.01); G06F 18/2155 (2023.01); G06N 3/082 (2013.01); G06N 5/01 (2023.01); G06N 5/025 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); H04L 9/088 (2013.01); H04L 9/0894 (2013.01); H04L 9/3236 (2013.01)] 20 Claims
OG exemplary drawing
 
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;
receiving two or more Quality of Service (QOS) dimensions for the multi-objective optimization model at run-time,
wherein at least one of the two or more QoS dimensions correspond to 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 based on the first QoS dimension and the second QoS dimension,
wherein the maximizing further includes ordering the two or more pipelines based on at least one of the two or more QoS dimensions,
wherein the multi-objective optimization model is partially de-optimized along the second 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.