FPGA Control Logic for SAN Node Data Processing
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Solution Overview
Problem
In data centers, the transition from complex monolithic servers to modular services with numerous interactive components leads to increased latency due to frequent data interactions, which can result in bottlenecks with general-purpose processors handling large data volumes, especially when handling data streams from IoT devices or in scale-out architectures.
Innovation Solution
Implementing FPGA control logic within a SAN node to process data streams using dynamically configurable rules, allowing for efficient data storage and retrieval by selecting appropriate kernels for specific data streams, thereby offloading data transformations near memory stores and reducing network bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If general-purpose processors handle large data volumes in data centers, then data processing capability is maintained, but latency increases and performance bottlenecks occur
Solution Approach 1:
The patent extracts data processing functions from general-purpose processors and relocates them to dedicated FPGA accelerators within SAN nodes. This separation allows general-purpose processors to focus on higher-level computations while FPGAs handle data stream processing, storage operations, and data transformations, thereby reducing latency and eliminating performance bottlenecks.
Solution Approach 2:
The patent introduces FPGA accelerators as intermediary components between data storage devices and general-purpose processors. These FPGAs act as specialized mediators that perform data stream processing and transformations closer to the data source, reducing the data travel distance and processing time, thus lowering latency without compromising overall processing capability.
2Quantity of substance
If data streams from IoT devices are processed through general-purpose processors, then data volume handling is achieved, but network bandwidth consumption increases
Solution Approach 1:
The patent applies preliminary data processing actions directly at the SAN node using FPGA accelerators before data is transmitted through the network. Data transformations, filtering, and formatting operations are performed in advance, so that only processed and optimized data needs to be transmitted over the network, significantly reducing network bandwidth consumption while maintaining data volume handling capability.
Solution Approach 2:
The patent implements local data processing quality by placing FPGA accelerators within SAN nodes close to data storage devices. This local processing allows data to be transformed and optimized at the point of storage rather than being processed centrally, reducing the amount of data that needs to traverse the network and thereby reducing network bandwidth consumption.
3Adaptability or versatility
If modular services with numerous interactive components are implemented, then system flexibility is improved, but data interaction latency increases
Solution Approach 1:
The patent segments data processing functions across multiple independent components: SAN nodes with FPGA accelerators, storage devices, and general-purpose processors. This segmentation allows each component to handle specific processing tasks independently, enabling flexible modular service deployment while reducing data interaction latency through localized processing at each segment.
Solution Approach 2:
The patent uses FPGA accelerators as intermediary processing units within SAN nodes that mediate between different modular services and data storage components. These intermediaries perform data transformations and processing locally, reducing the need for data to travel between services and thereby reducing latency while maintaining system flexibility.
Data Source
AI summary
Technology for a controller in a storage area network (SAN) node operable to perform data requests is described. The controller can receive a data request from a remote node. The data request can specify a data payload and a type of operation associated with the data request. The controller can select a kernel from a kernel table stored in the memory based on a set of rules. The kernel can be matched to the data request in accordance with the set of rules. The kernel can be configured using a bit stream. The controller can execute the kernel in order to perform the data request in accordance with the data payload and the type of operation.