Intelligent Real-time Optimization
In one embodiment, a method determines real-time information regarding changes to input data used to run an optimization. The optimization is run using a first computing system to generate a first optimization result within a first time window and the first computing system is configured to run the optimization periodically within subsequent time windows. The method determines when the changes to the input data indicate the optimization should be rerun. When the optimization should be rerun, the method causes a re-running of the optimization after the time window ends using a second computing system different from the first computing system. The re-running of the optimization using the changes and generating a second optimization result before a next time window for the first computing system to periodically run the optimization starts.
1 . A method comprising:
determining, by a computing device, real-time information regarding changes to input data used to run an optimization, the optimization being run using a first computing system to generate a first optimization result within a first time window and the first computing system configured to run the optimization periodically within subsequent time windows;
determining, by the computing device, when the changes to the input data indicate the optimization should be rerun; and
when the optimization should be rerun, causing, by the computing device, a re-running of the optimization after the first time window ends using a second computing system different from the first computing system, the re-running of the optimization using the changes and generating a second optimization result before a next time window for the first computing system to periodically run the optimization starts.
2 . The method of claim 1 , wherein the second computing system is dynamically expandable by including more computing resources in the second computing system.
3 . The method of claim 2 , wherein the second computing system, when dynamically expanded, incurs an increased cost than running the optimization using the first computing system.
4 . The method of claim 2 , wherein the second computing system, when dynamically expanded, runs the optimization faster than the first computing system.
5 . The method of claim 2 , wherein the first computing system is not dynamically expandable.
6 . The method of claim 1 , wherein determining when the changes to the input data indicate the optimization should be rerun comprise:
quantifying an impact of the changes; and
determining when the impact violates a threshold; and
when the impact violates the threshold, causing the re-running of the optimization after the first time window ends.
7 . The method of claim 6 , further comprising:
when the impact violates the threshold, determining whether re-running of the optimization after the first time window ends is approved or disapproved;
when the rerunning is disapproved, not re-running of the optimization after the first time window ends; and
when the rerunning is approved, re-running of the optimization after the first time window ends.
8 . The method of claim 1 , further comprising communicating a portion of the second optimization result to one or more clients that received the first optimization result.
9 . The method of claim 1 , further comprising calculating an amount of computing resources needed in the second computing system to rerun the optimization based on the changes.
10 . A non-transitory computer-readable storage medium containing instructions, that when executed, control a computer system to be configured for:
determining real-time information regarding changes to input data used to run an optimization, the optimization being run using a first computing system to generate a first optimization result within a first time window and the first computing system configured to run the optimization periodically within subsequent time windows;
determining when the changes to the input data indicate the optimization should be rerun; and
when the optimization should be rerun, causing a re-running of the optimization after the first time window ends using a second computing system different from the first computing system, the re-running of the optimization using the changes and generating a second optimization result before a next time window for the first computing system to periodically run the optimization starts.
11 . The non-transitory computer-readable storage medium of claim 10 , wherein the second computing system is dynamically expandable by including more computing resources in the second computing system.
12 . The non-transitory computer-readable storage medium of claim 11 , wherein the second computing system, when dynamically expanded, incurs an increased cost than running the optimization using the first computing system.
13 . The non-transitory computer-readable storage medium of claim 11 , wherein the second computing system, when dynamically expanded, runs the optimization faster than the first computing system.
14 . The non-transitory computer-readable storage medium of claim 11 , wherein the first computing system is not dynamically expandable.
15 . The non-transitory computer-readable storage medium of claim 10 , wherein determining when the changes to the input data indicate the optimization should be rerun comprise:
quantifying an impact of the changes; and
determining when the impact violates a threshold; and
when the impact violates the threshold, causing the re-running of the optimization after the first time window ends.
16 . The non-transitory computer-readable storage medium of claim 10 , further configured for:
when the impact violates the threshold, determining whether re-running of the optimization after the first time window ends is approved or disapproved;
when the rerunning is disapproved, not re-running of the optimization after the first time window ends; and
when the rerunning is approved, re-running of the optimization after the first time window ends.
17 . The non-transitory computer-readable storage medium of claim 10 , further configured for communicating a portion of the second optimization result to one or more clients that received the first optimization result.
18 . The non-transitory computer-readable storage medium of claim 10 , further configured for calculating an amount of computing resources needed in the second computing system to rerun the optimization based on the changes.
19 . An apparatus comprising:
one or more computer processors; and
a non-transitory computer-readable storage medium comprising instructions, that when executed, control the one or more computer processors to be configured for:
determining real-time information regarding changes to input data used to run an optimization, the optimization being run using a first computing system to generate a first optimization result within a first time window and the first computing system configured to run the optimization periodically within subsequent time windows;
quantifying an impact to the changes to the input data;
determining when the impact violates a threshold; and
when the impact violates the threshold, causing a re-running of the optimization after the first time window ends using a second computing system different from the first computing system and including more computing resources than the first computing system, the re-running of the optimization using the changes and generating a second optimization result before a next time window for the first computer system to periodically run the optimization starts.
20 . The apparatus of claim 19 , wherein determining when the changes to the input data indicate the optimization should be rerun comprise:
when the impact violates the threshold, determining whether re-running of the optimization after the first time window ends is approved or disapproved;
when the rerunning is disapproved, not re-running of the optimization after the first time window ends; and
when the rerunning is approved, re-running of the optimization after the first time window ends.