CLOUDFLARE, INC. (20240264877). DYNAMIC SELECTION OF WHERE TO EXECUTE APPLICATION CODE IN A DISTRIBUTED CLOUD COMPUTING NETWORK simplified abstract

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DYNAMIC SELECTION OF WHERE TO EXECUTE APPLICATION CODE IN A DISTRIBUTED CLOUD COMPUTING NETWORK

Organization Name

CLOUDFLARE, INC.

Inventor(s)

Michael Hart of New York NY (US)

Alyson Cabral of Austin TX (US)

Kenton Taylor Varda of Austin TX (US)

DYNAMIC SELECTION OF WHERE TO EXECUTE APPLICATION CODE IN A DISTRIBUTED CLOUD COMPUTING NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240264877 titled 'DYNAMIC SELECTION OF WHERE TO EXECUTE APPLICATION CODE IN A DISTRIBUTED CLOUD COMPUTING NETWORK

The abstract describes a patent application where a client device sends a request to a distributed cloud computing network, triggering the execution of code with multiple functions across different datacenters. The first function is executed at the first datacenter, while the second function is executed at a second datacenter. The first datacenter receives the results from the second datacenter and transmits a response to the client device based on these results.

  • The patent application involves a distributed cloud computing network with multiple datacenters.
  • The code triggered by a client request includes a first function and a second function.
  • The first function is executed at the first datacenter, while the second function is executed at a different datacenter.
  • Results from the second function executed at the second datacenter are used to generate a response to the client device.
  • The innovation optimizes the execution of functions across different datacenters in a distributed cloud computing network.
      1. Potential Applications:

The technology can be applied in various industries such as e-commerce, finance, and healthcare where distributed cloud computing is utilized for processing client requests efficiently.

      1. Problems Solved:

This technology addresses the challenge of optimizing the execution of functions in a distributed cloud computing network across multiple datacenters to improve response times and resource utilization.

      1. Benefits:

- Enhanced performance and efficiency in processing client requests. - Improved scalability and resource management in distributed cloud computing environments. - Streamlined communication between datacenters for executing functions.

      1. Commercial Applications:

The technology can be leveraged by cloud service providers, online platforms, and enterprises with distributed computing needs to enhance their infrastructure's performance and scalability, leading to better user experiences and cost savings.

      1. Prior Art:

Prior research in distributed computing, cloud networking, and function execution optimization can provide insights into similar technologies and approaches in the field.

      1. Frequently Updated Research:

Stay informed about the latest advancements in distributed cloud computing, function execution optimization, and network communication protocols to enhance the technology's capabilities and applications.

        1. Questions about Distributed Cloud Computing:

1. How does the technology optimize function execution across different datacenters? 2. What are the key considerations for implementing distributed cloud computing networks effectively?


Original Abstract Submitted

a request is received from a client device at a first datacenter a distributed cloud computing network. the distributed cloud computing network includes multiple datacenters. the received request triggers execution of code at the distributed cloud computing network. the code includes a first function and a second function. a determination is made to execute the first function at the first datacenter and to execute the second function at a second datacenter of the distributed cloud computing network. the first function is executed at the first datacenter to get a first result. the first datacenter causes the second function to be executed at the second datacenter. the first datacenter receives, from the second datacenter, a second result from the execution of the second function. the first datacenter transmits a response to the client device that is based at least in part on the first result and the second result.

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