Hypergraph Edge Load Estimation for Uncertain Resource Scheduling
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Solution Overview
Problem
Edge systems face challenges in managing machine-to-machine workloads with limited resources and random workload demands, leading to inefficiencies and delays in resource allocation and scheduling due to lack of real-time resource usage data and smaller resource pools.
Innovation Solution
Implementing a resource uncertainty estimation process using M-PCM-OFFD to accurately predict resource demand load, integrating Multivariate Probabilistic Collocation Method and Orthogonal Fractional Factorial Design for edge orchestrators to optimize resource allocation and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional resource allocation methods are used in edge systems, then device complexity is reduced, but resource allocation accuracy deteriorates leading to inefficient scheduling
Solution Approach 1:
The patent introduces an intermediary estimation process that acts as a mediator between resource requests and allocation decisions. This intermediary layer uses probabilistic methods to translate uncertain resource demands into actionable scheduling decisions, improving accuracy without directly increasing the complexity of the core scheduling system.
Solution Approach 2:
The patent replaces traditional mechanical/resource-based allocation methods with statistical and probabilistic models. By substituting deterministic scheduling with probabilistic resource demand estimation using M-PCM-OFFD, the system achieves higher precision in resource allocation while managing complexity through mathematical abstraction rather than physical system complexity.
2Productivity
If resource allocation is optimized for speed, then productivity improves, but measurement precision of resource demand deteriorates
Solution Approach 1:
The patent performs preliminary resource demand estimation using probabilistic models before actual resource allocation occurs. By pre-calculating resource requirements through M-PCM-OFFD methodology, the system prepares accurate demand profiles in advance, enabling both high-speed allocation and precise measurement without the usual trade-off.
Solution Approach 2:
The patent implements continuous resource demand estimation and monitoring that operates throughout the resource allocation process. This continuous estimation ensures that productivity gains from fast allocation do not compromise measurement precision, as the probabilistic models continuously refine resource demand understanding throughout the scheduling lifecycle.
3Reliability
If detailed resource monitoring is implemented, then reliability improves, but loss of time due to processing overhead increases
Solution Approach 1:
The patent transforms the monitoring approach by changing parameters from detailed continuous monitoring to probabilistic sampling and estimation. By using M-PCM-OFFD to estimate resource demands from limited data points rather than monitoring all parameters continuously, the system maintains high reliability for service level compliance while dramatically reducing processing time and overhead.
Solution Approach 2:
The patent employs lightweight, disposable estimation models that are computationally inexpensive and can be rapidly executed. These probabilistic estimation routines serve as temporary, low-cost analytical tools that provide sufficient reliability for decision-making without the time penalty of sophisticated continuous monitoring systems.
Data Source
AI summary
Managing the resource demand load for edge systems is significantly more complex than for other systems, such as cloud environments. A time period in which an application or task is operating based on initial demand resource load values that are provided by a customer may be inaccurate, which may expose sub-standard execution. Embodiments herein seek to significantly mitigate the potential of sub-standard execution. Embodiments collect a repository of resource demand load usage data over a time period that can be used to accurately determine the statistical moments of uncertain resource demand load. In one or more embodiments, a repository of hypervector and/or hyperspace representations may be generated and used to help with resource demand load estimation.