Machine Learning Gateway Caching for Redundant Software Downloads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for managing software updates in IT infrastructure environments require significant bandwidth usage and manual effort due to redundant downloads and lack an efficient method for prioritizing and caching software packages across multiple IT assets.
Innovation Solution
Implementing a gateway device with machine learning-based software code caching logic that determines the priority of software packages based on factors like installation frequency, asset type, and criticality, allowing intelligent caching and reducing redundant downloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software packages are downloaded for each IT asset individually, then each asset receives its required software code, but significant bandwidth is consumed due to redundant downloads
Solution Approach 1:
The patent merges multiple identical software download requests into a single download operation at the gateway device. When multiple IT assets require the same software package, the gateway device downloads it once and caches it locally, then serves all requesting assets from the cache. This combining approach eliminates redundant network transmissions and significantly reduces bandwidth consumption while ensuring reliable software delivery to all assets.
Solution Approach 2:
The gateway device performs preliminary downloading and caching of software packages before they are actually needed by IT assets. By anticipating software update requirements and pre-caching packages in the gateway's storage, the system prepares software in advance, eliminating the need for repeated downloads when assets request updates and reducing overall bandwidth usage.
2Productivity
If a cache replacement algorithm is implemented without priority determination, then cache management is simpler, but software updates may not be timely for critical assets
Solution Approach 1:
The system implements feedback mechanisms where the gateway device monitors software requests from multiple IT assets, analyzes usage patterns and asset criticality levels, and uses this information to dynamically adjust cache replacement decisions. The feedback loop continuously optimizes which software packages are retained in cache, ensuring that critical assets receive updates timely while the system adapts to changing requirements without manual intervention.
Solution Approach 2:
The cache replacement algorithm transitions from static to dynamic operation by incorporating real-time information about asset criticality, software usage frequency, and request patterns. The system dynamically adjusts cache retention priorities based on current conditions, allowing critical software updates to be preserved in cache longer while less important packages are replaced, thereby improving update speed for important assets without requiring overly complex manual management.
3Reliability
If manual software management is used across multiple IT assets, then precise control over each asset is achieved, but significant manual effort is required
Solution Approach 1:
The gateway device is equipped with intelligent caching logic that autonomously manages software package downloads, caching, and distribution to multiple IT assets. The system automatically monitors software requests, determines which packages to cache based on usage patterns and asset priorities, and serves software updates without requiring manual intervention. This self-service capability maintains precise control over software deployment while dramatically reducing the manual effort required for managing updates across numerous assets.
Solution Approach 2:
The gateway device acts as an intermediary between the external network and multiple internal IT assets, centralizing software management functions. By positioning the gateway as the intermediate point that handles all software download and distribution operations, the system achieves centralized control over software deployment while simplifying the operational burden on users, as the gateway automatically manages the complexity of serving multiple assets.
Data Source
AI summary
An apparatus includes at least one processing device configured to receive, at a gateway device deployed in an information technology (IT) infrastructure environment from a given IT asset, a request for software code. The processing device is also configured to download the requested software code from a backend server responsive to determining that it is not cached locally in a software cache of the gateway device, and to provide the requested software code to the given IT asset. The processing device is further configured to determine, utilizing one or more machine learning algorithms, a priority of the requested software code relative to other software code cached locally in the software cache. The processing device is further configured to implement a cache replacement algorithm for the software cache of the gateway device based at least in part on the determined priority of the requested software code.