How Computers Automatically Adjust Tasks to Run Faster in Data Centers
A method for cloud computers to monitor their own performance while processing massive data tasks and automatically changing their settings or resource levels to stay efficient.
Original patent title: “Dynamic parallel distributed job configuration in a shared-resource environment”
A method for cloud computers to monitor their own performance while processing massive data tasks and automatically changing their settings or resource levels to stay efficient. Granted to International Business Machines Corp in 2016 with 21 claims and 32 forward citations.
Key facts
Coverage
What does this patent actually cover?
This patent describes a system that acts like a smart thermostat for large-scale computing tasks, specifically MapReduce jobs. As a job runs, the system constantly builds a usage profile that tracks how much memory and processing power the cluster is consuming. If the system detects inefficiencies, it triggers a control loop to reconfigure parameters like input size, resource allocation, or the number of concurrent tasks. It can also dynamically add or remove physical computing resources from the cluster to ensure the job finishes as quickly as possible without wasting capacity.
The gap
What does this patent NOT cover?
- Does not cover static job configurations that are set once before the job begins.
- Does not cover manual intervention or human-triggered adjustments to job parameters.
- Does not cover non-distributed computing environments where a single machine processes the entire task.
- Does not cover general load balancing that does not involve reconfiguring specific job parameters like input size or concurrent component counts.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The innovation is the closed-loop feedback system that ties the job's internal configuration parameters directly to the external cluster's real-time resource availability.
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Large-scale data processing on Apache Hadoop clusters
Automated resource scaling in cloud-based big data analytics
IBM Cloud data processing services
Why it matters
The bigger picture
In data centers, running massive data analysis jobs is expensive and time-consuming. Before this, engineers often had to guess the best settings for a job before it started. This patent provides a way to make those systems self-optimizing, which is essential for cloud providers like IBM, AWS, and Google to maximize their hardware utilization and reduce costs.
Filed
June 20, 2014
Granted
August 2, 2016
Market context
Who's building on this
Companies in this space
IBM remains a primary holder of this technology, integrating these concepts into their hybrid cloud and AI infrastructure. Major cloud service providers like Amazon Web Services (AWS) and Google Cloud also utilize similar automated orchestration layers to manage their massive distributed computing clusters.
Market impact
This technology helped shift the industry toward 'serverless' and 'auto-scaling' computing models. By automating the tuning of distributed jobs, it reduced the need for specialized engineers to manually babysit long-running data pipelines, effectively lowering the barrier to entry for big data analytics.
Claim 1 — Plain English
What this patent covers
This patent describes a system that acts like a smart thermostat for large-scale computing tasks, specifically MapReduce jobs. As a job runs, the system constantly builds a usage profile that tracks how much memory and processing power the cluster is consuming. If the system detects inefficiencies, it triggers a control loop to reconfigure parameters like input size, resource allocation, or the number of concurrent tasks. It can also dynamically add or remove physical computing resources from the cluster to ensure the job finishes as quickly as possible without wasting capacity.
The clever bit
The innovation is the closed-loop feedback system that ties the job's internal configuration parameters directly to the external cluster's real-time resource availability.
What it does not cover
- Does not cover static job configurations that are set once before the job begins.
- Does not cover manual intervention or human-triggered adjustments to job parameters.
- Does not cover non-distributed computing environments where a single machine processes the entire task.
- Does not cover general load balancing that does not involve reconfiguring specific job parameters like input size or concurrent component counts.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Moderate
Citation count
30/40
Moderately cited
Claim breadth
14/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
10/20
Granted 5–10 years ago
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$187K – $599K
Midpoint $374K · 8.0 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
21 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Fuller, N. C. M., Naik, V. K., Zhang, L., & Zeng, L. (2016). How Computers Automatically Adjust Tasks to Run Faster in Data Centers (U.S. Patent No. 9,405,582). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/9405582/aws-elastic-beanstalk
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How Computers Automatically Adjust Tasks to Run Faster in Data Centers cover?
A method for cloud computers to monitor their own performance while processing massive data tasks and automatically changing their settings or resource levels to stay efficient.
Who owns patent US 9405582?
International Business Machines Corp owns this patent, granted in 2016.
When does this patent expire?
This patent is expected to expire on August 2, 2036, when the invention enters the public domain.
What is patent US 9405582 cited by?
This patent has been cited by 32 later patents that build on its ideas.
What problem does this patent solve?
In data centers, running massive data analysis jobs is expensive and time-consuming. Before this, engineers often had to guess the best settings for a job before it started. This patent provides a way to make those systems self-optimizing, which is essential for cloud providers like IBM, AWS, and Google to maximize their hardware utilization and reduce costs.
What does this patent NOT cover?
Does not cover static job configurations that are set once before the job begins.
Same assignee
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