Dynamic Hardware Accelerator Allocation for Edge IoT Latency
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Solution Overview
Problem
Current systems fail to optimize Mobile Edge Computing (MEC) resources supporting IoT services, leading to inefficiencies in IoT traffic aggregation, processing, and storage, and increased latency due to inadequate hardware acceleration at edge devices.
Innovation Solution
Dynamic hardware acceleration is assigned to IoT gateways or MEC servers based on specific needs, utilizing hardware accelerators like FPGAs to optimize processing tasks closer to IoT data sources, reducing latency and improving resource utilization through a hierarchical edge network topology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hardware accelerators are statically allocated to edge devices, then device complexity is reduced and ease of operation is improved, but resource utilization efficiency deteriorates and productivity decreases
Solution Approach 1:
The patent implements dynamic hardware accelerator allocation where the computing resource manager continuously monitors workload demands and reassigns hardware accelerators to different edge devices based on real-time needs. This dynamic allocation mechanism resolves the contradiction by allowing high resource utilization efficiency while managing complexity through automated software-controlled assignment rather than static hardware configuration.
Solution Approach 2:
The patent creates a universal hardware accelerator pool that can be allocated to multiple different edge devices and task types. The same hardware accelerators can serve multiple functions and multiple devices sequentially, achieving multi-functionality that improves resource utilization while the computing resource manager handles the complexity of allocation decisions.
2Speed
If more hardware accelerators are deployed at edge devices, then processing speed is improved and latency is reduced, but device complexity increases and cost increases
Solution Approach 1:
The patent merges multiple hardware accelerator resources into a shared pool managed by the computing resource manager. Instead of each edge device having dedicated accelerators, the system combines resources so that accelerators can be dynamically assigned to different devices needing high-speed processing, achieving fast processing when needed without permanently increasing device complexity.
Solution Approach 2:
The computing resource manager acts as an intermediary between hardware accelerators and edge devices. It manages the allocation, assignment, and reassignment of accelerators, allowing edge devices to access high-speed processing capabilities on-demand without permanently integrating complex hardware acceleration components into each device.
3Reliability
If hardware resources are over-provisioned at edge devices, then reliability is improved and service availability is enhanced, but loss of energy increases and cost increases
Solution Approach 1:
The patent ensures continuous useful action by dynamically allocating hardware accelerators to edge devices that currently need them for processing tasks. Rather than having accelerators idle at devices where they are not needed, the system continuously matches accelerator availability with actual processing demands, maintaining service reliability while eliminating wasted energy consumption from idle hardware.
Solution Approach 2:
The computing resource manager discards (reassigns) hardware accelerators from devices that no longer need them and recovers them for allocation to devices with current processing demands. This dynamic recovery and reallocation mechanism maintains service availability by ensuring accelerators are always available when needed while preventing energy waste from permanent over-provisioning.
Data Source
AI summary
Embodiments of a system and method for dynamic hardware acceleration are generally described herein. A method may include identifying a candidate task from a plurality of tasks executing in an operating environment, the operating environment within a hardware enclosure, the candidate task amenable to hardware optimization, instantiating, in response to identifying the candidate task, a hardware component in the operating environment to perform hardware optimization for the task, the hardware component being previously inaccessible to the operating environment, and executing, by the operating environment, a class of tasks amenable to the hardware optimization on the hardware component.