Edge Network Workload Distribution via Dynamic Resource Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Edge computing networks face challenges in optimizing resource allocation due to heterogeneous computational devices and resource constraints, leading to inefficiencies in processing workloads and meeting latency requirements.
Innovation Solution
Implementing a framework that optimizes workload distribution across heterogeneous devices using machine learning models, such as neural networks, to balance latency, accuracy, and resource utilization, and employing techniques like model compression and quantization to enhance performance on resource-constrained Edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workload is distributed across heterogeneous edge devices, then resource utilization is improved, but processing latency increases due to device heterogeneity and constraints
Solution Approach 1:
The patent implements dynamic workload distribution that adapts to real-time device states and workload characteristics. The system continuously monitors device performance metrics and adjusts task allocation dynamically, allowing the edge computing platform to optimize for either latency or resource utilization based on current conditions. This dynamic approach resolves the contradiction by making the system flexible rather than static in its load balancing strategy.
Solution Approach 2:
The system changes operational parameters such as task partitioning granularity, device selection criteria, and resource allocation weights based on workload type and device capabilities. By adjusting these parameters dynamically, the system can optimize performance metrics for different scenarios - prioritizing latency for time-sensitive tasks and resource utilization for batch processing, thus resolving the fundamental trade-off.
2Productivity
If machine learning models are deployed on edge devices, then workload processing capability is improved, but device resource constraints are exceeded
Solution Approach 1:
The patent segments machine learning models into smaller components or layers that can be distributed across multiple edge devices. Instead of deploying entire complex models on single resource-constrained devices, the system partitions model inference tasks across device clusters, reducing the computational burden on individual devices while maintaining overall processing capability.
Solution Approach 2:
The system creates simplified versions or copies of machine learning models optimized for specific edge device capabilities. Lightweight model variants are deployed to resource-constrained devices, while more complex models run on devices with greater capacity. This copying strategy allows the system to leverage multiple devices with different resource profiles to collectively handle diverse workload requirements.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to optimize resources in edge networks. An example apparatus includes agent managing circuitry to invoke an exploration agent to identify platform resource devices, select a first one of the identified platform resource devices, and generate first optimization metrics for the workload corresponding to the first one of the identified platform resource devices, the first optimization metrics corresponding to a first path. The example agent is to further select a second one of the identified platform resource devices, generate second optimization metrics for the workload corresponding to the second one of the identified platform resource devices, the second optimization metrics corresponding to a second path. The example apparatus also includes benchmark managing circuitry to embed second semantic information to the workload, the second semantic information including optimized graph information and platform structure information corresponding to the second one of the identified platform resource devices, and reconfiguration managing circuitry to select the first path or the second path during runtime based on (a) service level agreement (SLA) information and (b) utilization information corresponding to the first and second identified platform resource devices.