View source for 18238345. TECHNIQUES TO SHAPE NETWORK TRAFFIC FOR SERVER-BASED COMPUTATIONAL STORAGE simplified abstract (Intel Corporation)

=TECHNIQUES TO SHAPE NETWORK TRAFFIC FOR SERVER-BASED COMPUTATIONAL STORAGE=


==Organization Name==

[[:Category:Intel Corporation|Intel Corporation]]


[[Category:Intel Corporation]]

==Inventor(s)==

[[:Category:Michael Mesnier of Scappoose OR (US)|Michael Mesnier of Scappoose OR (US)]][[Category:Michael Mesnier of Scappoose OR (US)]]

[[:Category:Anil Vasudevan of Portland OR (US)|Anil Vasudevan of Portland OR (US)]][[Category:Anil Vasudevan of Portland OR (US)]]

[[:Category:Kelley Mullick of Hillsboro OR (US)|Kelley Mullick of Hillsboro OR (US)]][[Category:Kelley Mullick of Hillsboro OR (US)]]

==TECHNIQUES TO SHAPE NETWORK TRAFFIC FOR SERVER-BASED COMPUTATIONAL STORAGE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18238345 titled 'TECHNIQUES TO SHAPE NETWORK TRAFFIC FOR SERVER-BASED COMPUTATIONAL STORAGE

==Simplified Explanation==

The abstract describes techniques for shaping network traffic in server-based computational storage. Here is a simplified explanation of the abstract:

* The patent application discusses methods for managing network traffic in a computational storage server.
* It introduces the concept of a "class of service" associated with a compute offload request, which helps in organizing and prioritizing the storage of compute offload commands.
* The compute offload commands are stored in queues of a network interface device at the computational storage server.
* These queues are then used to schedule block-based compute operations for execution by the compute circuitry at the server, fulfilling the compute offload requests.

== Potential Applications ==
This technology has potential applications in various fields, including:

* Cloud computing: It can enhance the performance and efficiency of cloud-based computational storage systems.
* Big data analytics: By optimizing network traffic and offloading compute operations, it can improve the processing speed and scalability of big data analytics platforms.
* Edge computing: The techniques can be applied to edge devices with computational storage capabilities, enabling faster and more efficient data processing at the edge.

== Problems Solved ==
The technology addresses several problems in server-based computational storage:

* Network congestion: By shaping network traffic, it helps in managing and reducing congestion, ensuring smooth data transfer between the computational storage server and other devices.
* Compute offload management: The class of service and queue-based approach simplify the management and scheduling of compute offload requests, improving overall system performance.
* Resource utilization: By offloading compute operations to the computational storage server, it optimizes resource utilization and reduces the burden on the main server.

== Benefits ==
The technology offers several benefits:

* Improved performance: By efficiently managing network traffic and offloading compute operations, it enhances the overall performance and responsiveness of the computational storage server.
* Scalability: The techniques can be applied to large-scale systems, allowing for seamless scalability and accommodating increasing computational demands.
* Resource optimization: By utilizing computational storage capabilities, it optimizes resource utilization and reduces the need for additional compute resources.
* Enhanced efficiency: The class of service and queue-based approach streamline the process of compute offload management, improving system efficiency and reducing latency.


==Original Abstract Submitted==

Examples include techniques to shape network traffic for server-based computational storage. Examples include use of a class of service associated with a compute offload request that is to be sent to a computational storage server in a compute offload command, The class of service to facilitate storage of the compute offload command in one or more queues of a network interface device at the computational storage server. The storage of the compute offload command to the one or more queues to be associated with scheduling a block-based compute operation for execution by compute circuitry at the computational storage server to fulfill the compute offload request indicated in the compute offload command.



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