Dynamic Compute Node Provisioning for Latency Optimization
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Solution Overview
Problem
Optimizing latency and load in distributed compute networks, particularly in edge compute platforms, to improve performance and resource usage while meeting user service level agreements (SLAs).
Innovation Solution
A computer-implemented method and system that dynamically provisions resources in a distributed compute network by receiving operational parameter data from routing and compute nodes, using machine learning models to simulate and optimize the selection and provisioning of compute nodes for virtual application instances, and implementing these optimizations to achieve desired latency and load thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more compute nodes are provisioned to meet user demand, then service reliability and performance are improved, but resource usage costs and system complexity increase
Solution Approach 1:
The system dynamically provisions and deprovisions compute nodes based on real-time demand patterns and predictive analytics. The dynamic provisioning manager continuously adjusts the number of active compute nodes, transitioning from static resource allocation to adaptive, demand-driven resource management that optimizes both reliability and resource utilization.
Solution Approach 2:
The system performs preliminary actions by predicting future demand patterns using machine learning models and proactively provisioning compute nodes before peak demand occurs. This predictive approach ensures service reliability is maintained while avoiding over-provisioning during low-demand periods, thereby optimizing resource usage.
2Productivity
If compute nodes are dynamically provisioned to meet varying demand, then resource usage efficiency is improved, but system complexity and management difficulty increase
Solution Approach 1:
The system implements self-service through automated decision-making frameworks where the dynamic provisioning manager independently analyzes demand patterns, predicts resource requirements, and executes provisioning decisions without manual intervention. This automation reduces operational complexity while maintaining high resource usage efficiency.
Solution Approach 2:
The system employs feedback mechanisms where performance data from compute nodes is continuously collected, analyzed, and used to refine predictive models and provisioning strategies. This closed-loop feedback system enables the system to learn from past performance and improve future decisions, managing complexity through data-driven optimization.
3Loss of time
If latency is reduced by selecting optimal compute nodes, then user experience is improved, but routing complexity and decision-making difficulty increase
Solution Approach 1:
The system changes key parameters such as selecting compute nodes based on multiple criteria including geographic proximity, network conditions, current load, and predictive performance metrics. This multi-parameter optimization approach reduces latency by finding the optimal compute node for each user request while managing routing complexity through systematic decision frameworks.
Solution Approach 2:
The system replaces manual routing decisions with machine learning-based predictive models that automatically determine optimal compute node selections. This substitution of mechanical routing processes with intelligent prediction systems reduces latency while managing complexity through automated, data-driven decision-making rather than complex manual control.
Data Source
AI summary
There is provided a computer-implemented method of provisioning resources in a distributed compute network comprising one or more routing nodes and one or more compute nodes configured to host one or more virtual application instances of an application thereon, the method being performed by at least one hardware processor and comprising: a) receiving, by a system manager, routing operational parameter data from one or more routing nodes and compute operational parameter data from one or more compute nodes for a current state of the distributed compute network; b) generating a first proposed state of the distributed compute network by utilizing the routing operational parameter data in a first model to simulate selection and/or deselection of one or more compute nodes for provisioning of virtual application instances of the application; c) generating a second proposed state of the distributed computing network by utilizing the compute operational parameter data in a second model to provision and/or deprovision virtual application instances of the application on the compute nodes selected in the first proposed state; and c) implementing the second proposed state on the distributed compute network by provisioning and/or deprovisioning one or more virtual application instances of the application on one or more compute nodes on the distributed computing network to define a new state of the distributed computing network.