Software Patch Difference Device for DaaS Update Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing software patches and updates across numerous devices in large-scale environments, such as Device-as-a-Service (DaaS) settings, becomes unwieldy due to the complexity of tracking and applying patches to millions of devices with varying software versions, often leading to CPU bursts and inefficient resource utilization when polled too frequently.
Innovation Solution
A system comprising a polling computing device that adjusts its polling rate to align with a minimum interval, a software patch difference device that filters updates using cryptographic functions, and a software patch analytics device that generates reports for updated devices, optimizing data transmission and processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If polling rate is increased to track software updates more frequently, then update detection speed is improved, but CPU burst and resource overload occur
Solution Approach 1:
The patent introduces a polling rate adjustment mechanism that acts as an intermediary between the update detection requirement and the device processing capacity. The polling computing device dynamically adjusts the polling rate based on device responses and processing loads, preventing direct CPU bursts while maintaining effective update tracking. This mediator approach resolves the contradiction by decoupling the need for frequent updates from the need for high polling frequency.
2Loss of time
If polling is performed frequently to ensure timely patch detection, then patch update timeliness is improved, but device processing load increases
Solution Approach 1:
The patent implements dynamic polling rate adjustment where the polling frequency is not fixed but adapts based on device processing load and update criticality. The system can increase polling frequency for critical devices or during low-load periods, and decrease it during high-load periods, thereby maintaining patch update timeliness while optimizing device processing load and energy consumption.
3Measurement precision
If all device data is transmitted and processed centrally, then comprehensive tracking accuracy is improved, but data transmission volume and processing complexity increase
Solution Approach 1:
The patent extracts and processes only the essential update detection data at the polling computing device, rather than transmitting and processing all device data centrally. The system focuses on extracting software version information and update status, filtering out unnecessary data, thereby maintaining comprehensive tracking accuracy for patch management while reducing data transmission volume and processing complexity.
Data Source
AI summary
An example software patch difference device includes a processor to: receive current software version indicators of software installed at monitored devices; generate, using a cryptographic function, respective identifiers of the current software version indicators for the monitored devices; retrieve, from storage, respective previous identifiers of previous software version indicators of the software installed at the monitored devices, the respective previous identifiers generated using the cryptographic function; compare, for the current software version indicators, a respective identifier with a respective previous identifier; when a difference is determined therebetween for a given device: replace, at the storage, respective previous software version indicators for the given device with respective current software version indicators; and transmit, to an analytics device, a respective software change indicator of the given device, to trigger the analytics device to generate a report indicating statistics for respective software versions installed at the monitored devices.