Workload Analysis System for Edge Network Resource Allocation
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Solution Overview
Problem
In edge networks, accurately estimating workload complexity is challenging due to diverse compute requirements and resource constraints, leading to inefficient resource allocation and potential violations of service level agreements (SLAs) as resources are often over- or under-allocated without precise complexity assessment.
Innovation Solution
A workload analysis system that employs pre-processing models and AI acceleration circuitry to determine workload complexity by parsing payload information and SLA criteria, selecting appropriate resources based on complexity metrics, and dynamically allocating resources to ensure optimized utilization and compliance with SLAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated without precise complexity assessment, then resource allocation speed is improved, but resource utilization efficiency deteriorates leading to over- or under-allocation
Solution Approach 1:
The system performs preliminary complexity assessment of incoming workloads by analyzing payload information and SLA criteria before resource allocation. Pre-processing models evaluate workload characteristics in advance, enabling informed resource selection that balances allocation speed with utilization efficiency.
Solution Approach 2:
The patent introduces an intermediary complexity assessment mechanism between workload arrival and resource allocation. This intermediary layer analyzes workload payload information and SLA criteria to generate complexity metrics, which then guide resource selection without significantly delaying allocation.
2Measurement precision
If pre-processing models and AI acceleration circuitry are used to assess workload complexity, then resource allocation accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complexity assessment function into separate pre-processing models that handle different aspects of workload analysis. AI acceleration circuitry is dedicated specifically to executing these models, separating the assessment function from general resource management and reducing overall system complexity through functional decomposition.
Solution Approach 2:
The patent introduces an intermediary complexity assessment mechanism between workload arrival and resource allocation. This intermediary layer analyzes workload payload information and SLA criteria to generate complexity metrics, which then guide resource selection without significantly delaying allocation.
3Reliability
If resources are over-allocated to ensure SLA compliance, then service level agreement reliability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system performs preliminary complexity assessment of incoming workloads by analyzing payload information and SLA criteria before resource allocation. Pre-processing models evaluate workload characteristics in advance, enabling informed resource selection that balances allocation speed with utilization efficiency.
Solution Approach 2:
The system uses feedback from complexity assessment results to dynamically adjust resource allocation decisions. By continuously evaluating workload complexity metrics against available resources and SLA requirements, the system optimizes allocation to achieve compliance while maximizing utilization efficiency.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to estimate workload complexity. An example apparatus includes processor circuitry to perform at least one of first, second, or third operations to instantiate payload interface circuitry to extract workload objective information and service level agreement (SLA) criteria corresponding to a workload, and acceleration circuitry to select a pre-processing model based on (a) the workload objective information and (b) feedback corresponding to workload performance metrics of at least one prior workload execution iteration, execute the pre-processing model to calculate a complexity metric corresponding to the workload, and select candidate resources based on the complexity metric.