Smart Scheduler Engine for Networked Device Update Rollouts
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Solution Overview
Problem
In media content delivery systems, the diversity of electronic devices with different operating systems and software configurations complicates the process of software updates, leading to potential adverse effects and increased customer support issues due to unforeseen failures and performance issues post-upgrade.
Innovation Solution
A smart scheduler engine within an update server system schedules updates based on predefined criteria such as geographic region, device type, and operating system version, using protocols like TR-069 and SNMP, to minimize failures and optimize the update process by rolling out updates in subgroups and prioritizing groups based on historical data, with the ability to roll back or cancel updates if anomalies exceed a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software updates are deployed to diverse electronic devices, then system performance and security are improved, but device failures and adverse effects increase
Solution Approach 1:
The patent segments the update deployment process into distinct phases: a test group receives updates first, followed by additional test groups, and finally the general population. This segmentation allows systematic monitoring of device failures at each stage, enabling early detection of update-related issues before widespread deployment, thus improving reliability while controlling harmful effects.
Solution Approach 2:
The patent implements preliminary actions by conducting update testing on specific device types and configurations before general deployment. The system identifies representative device profiles, tests updates on these profiles first, and uses the results to inform broader deployment decisions. This preliminary testing prevents catastrophic failures across the entire device ecosystem.
2Productivity
If updates are deployed to all devices simultaneously, then update speed and productivity are improved, but system stability and error detection capability worsen
Solution Approach 1:
The patent divides the device population into multiple deployable groups based on device characteristics, update risk profiles, and operational criticality. This segmentation enables controlled rollout where each group receives updates at staggered intervals, maintaining productivity through systematic progression while ensuring stability through progressive validation at each deployment stage.
Solution Approach 2:
The patent implements continuous feedback mechanisms that monitor device performance, error rates, and user experiences after update deployment. This feedback informs subsequent deployment decisions, allowing the system to adjust rollout speed and target groups dynamically. The feedback loop maintains productivity by preventing unnecessary delays while ensuring stability through data-driven deployment control.
3Measurement precision
If comprehensive device monitoring is implemented, then failure detection precision is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent applies local quality by implementing differentiated monitoring strategies tailored to specific device types, update risk levels, and operational contexts. High-risk devices receive intensive monitoring with granular metrics, while low-risk devices use simplified monitoring. This targeted approach achieves high failure detection precision for critical devices without uniformly increasing system complexity across the entire device ecosystem.
Data Source
AI summary
A system (100) includes an update server (110) in communication with one or more client devices (106,107,108) across a network (102). A smart scheduler engine (118) schedules deployment of an update (117) to at least a first group of client devices. A monitoring auto configuration server interface (111) deploys the update to the first group of client devices. An analytics engine (114) identifies one or more anomalies occurring in the first group of client devices resulting from the upgrade. When this occurs, the scheduling engine cancels future updates to at least a second group of client devices where the later group includes devices having one or more device characteristics that correlate with other devices of the first group of client devices experiencing the one or more anomalies.