Palo Alto Networks has patented a system and method for provisioning code snippets to program a content delivery network. The method involves configuring nodes based on client code snippets, monitoring resource usage, buffering network requests, and responding to requests based on the snippets. GlobalData’s report on Palo Alto Networks gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Palo Alto Networks, IoT network security was a key innovation area identified from patents. Palo Alto Networks's grant share as of February 2024 was 63%. Grant share is based on the ratio of number of grants to total number of patents.
Provisioning code snippets for programming a content delivery network
A recently granted patent (Publication Number: US11930092B2) discloses a method for configuring nodes in a delivery network for content delivery based on client code snippets. The method involves configuring nodes to monitor anomalous resource usage, buffering network requests for content, and reconfiguring nodes based on received responses from origin servers. The client code snippets uploaded from devices of a company play a crucial role in this process, allowing for efficient content delivery and response to network requests.
Furthermore, the patent also covers the configuration of multiple sets of nodes for different companies, monitoring resource consumption, and utilizing machine learning models to detect anomalous resource usage. The programmable delivery network described in the patent allows for the efficient handling of network traffic, prefetching content, and temporary storage based on client code snippets. The nodes are programmed to test client code snippets to ensure safety thresholds are met before increasing the number of nodes, showcasing a systematic approach to network optimization and resource management. Overall, the patent highlights a sophisticated method for optimizing content delivery networks through the intelligent configuration of nodes based on client code snippets and monitoring for anomalous resource usage using machine learning models and trend information.
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