FPGA Cloud Resource Allocation via Network Delay Measurement
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Solution Overview
Problem
In large-scale FPGA cloud platforms, the increasing number of acceleration cards causes varying network delays, leading to suboptimal performance and user experience due to inefficient resource allocation and management.
Innovation Solution
A method and system for allocating FPGA resources based on network delay, where a cloud monitoring management platform initiates a data packet broadcast to identify the FPGA with the minimum delay, allowing users to access the resource with the lowest latency, while ensuring secure access through an authorization file.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If the quantity of FPGA acceleration cards is continuously increased to meet computation demands, then the computing capacity is improved, but the network transmission delay increases and resource management becomes more difficult
Solution Approach 1:
The system performs preliminary network delay measurement before task allocation. The cloud platform sends test packets to all available FPGA cards and records their response times in advance, creating a delay profile database. This preliminary action enables the system to make informed allocation decisions without measuring delay at allocation time, reducing the impact of network latency on task execution.
Solution Approach 2:
The system implements feedback mechanisms where FPGA cards respond to test packets with their delay information, and the cloud platform uses this feedback to dynamically adjust allocation decisions. The feedback loop includes: (1) cloud platform sends test packets, (2) FPGA cards return delay measurements, (3) cloud platform processes feedback data to identify optimal cards, and (4) allocation decisions are made based on this feedback. This continuous feedback enables adaptive resource management.
2Adaptability or versatility
If FPGA acceleration cards are deployed in different enclosures and computer rooms to expand service coverage, then the adaptability is improved, but the network delay varies and allocation complexity increases
Solution Approach 1:
The system segments the FPGA resource pool by physical location (enclosure and computer room) and manages each segment independently. The cloud platform divides the large-scale deployment into manageable units, measuring and recording network delays for each segment separately. This segmentation allows the system to handle geographical distribution without overwhelming complexity, as each segment can be managed with standardized procedures.
Solution Approach 2:
The system changes the allocation parameter from random or first-come-first-served to delay-based selection. By measuring network delay as a key parameter and using it as the basis for allocation decisions, the system optimizes performance across distributed deployments. The parameter change transforms the allocation strategy to prioritize cards with minimum delay, thereby managing complexity through performance-driven decision-making.
3Ease of operation
If random allocation of FPGA cards is used to simplify management, then the ease of operation is improved, but the accelerating effect is reduced due to suboptimal network delay
Solution Approach 1:
The system implements self-service through automated delay measurement and allocation. The cloud platform automatically sends test packets to all FPGA cards, collects delay information, and performs allocation decisions without manual intervention. This automation maintains ease of operation while achieving optimal allocation, as the system serves itself by automatically identifying and assigning the best-suited cards based on measured network conditions.
Solution Approach 2:
The system replaces manual allocation mechanisms with automated software-based delay measurement and selection. Instead of relying on simple random assignment or manual selection, the system uses network packet broadcasting, automatic response time calculation, and algorithmic selection to determine optimal allocations. This substitution of mechanical/manual processes with automated electronic systems enables both ease of operation and optimized performance.
Data Source
AI summary
An FPGA cloud platform acceleration resource allocates and coordinates accelerator card resources according to delays between a host of a user and FPGA accelerator cards deployed at various network segments. Upon an FPGA usage request of the user, allocating an FPGA accelerator card in an FPGA resource pool that has a minimum delay to the host. A cloud monitoring management platform obtains_transmission delays to a virtual machine network according to different geographic locations of various FPGA cards in the FPGA resource pool, and allocating a card having a minimum delay to each user. The cloud monitoring management platform prevents unauthorized users from accessing acceleration resources in the resource pool. The invention protects FPGA accelerator cards that are not authorized for users, and ensures that the card allocated to a user has a minimum network delay, thereby optimizing acceleration performance, and improving user experience.