Reinforcement Learning Model for Distributed Compute Network Resource Deployment
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Solution Overview
Problem
Optimizing resource deployment and load balancing in distributed compute networks to meet user service level agreements (SLAs) is challenging due to the complexity of managing data centers, virtual resources, and data traffic routing.
Innovation Solution
A computer-implemented method using reinforcement learning to train a machine learning model for dynamic application provisioning in edge compute platforms. The method involves defining an action space, selecting actions, determining rewards based on predicted load changes, and training the model using simulated load data to optimize resource allocation and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtualization techniques are used to host multiple virtual computing instances on physical servers, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The system employs self-service mechanisms through automated orchestration that dynamically manages virtual computing instances based on demand. The orchestration system automatically provisions, scales, and manages virtual instances without manual intervention, allowing the system to serve itself in optimizing resource allocation while handling the complexity of multi-tenant virtualization environments.
Solution Approach 2:
The patent implements dynamic resource allocation where the number and configuration of virtual computing instances are not fixed but adapt in real-time based on workload demands. The system dynamically creates, removes, and modifies virtual instances in response to changing conditions, enabling efficient resource utilization while managing complexity through flexible, adaptive configurations rather than static rigid structures.
2Reliability
If the number of virtual computing instances is dynamically adjusted in response to demand, then service level agreement compliance is improved, but control complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the orchestration continuously monitors workload conditions, performance metrics, and service level agreement compliance. Based on this feedback, the system automatically adjusts the number and distribution of virtual computing instances across physical servers. This closed-loop control ensures SLA compliance while managing complexity through automated decision-making based on real-time system state information.
Solution Approach 2:
The patent employs preliminary action by pre-configuring orchestration policies and rules that define acceptable performance thresholds and scaling parameters. These pre-established guidelines enable the system to make rapid automated decisions about virtual instance management without complex real-time calculations, ensuring SLA compliance through proactive rather than reactive control mechanisms.
3Speed
If physical servers are geographically distributed to reduce latency, then user service performance is improved, but network complexity increases
Solution Approach 1:
The system segments the distributed compute network into multiple geographic regions or zones, each with its own set of physical servers and virtual instances. The orchestration system manages these segments independently, allocating workloads to appropriate geographic segments based on user location and performance requirements. This segmentation reduces latency by placing compute resources closer to users while managing network complexity through modular, decentralized architecture.
Solution Approach 2:
The orchestration system implements multi-functionality by serving multiple purposes: it manages virtual instance lifecycles, performs load balancing across geographic regions, enforces service level agreements, and optimizes resource allocation. This universal control mechanism handles the complexity of geographically distributed networks through a single coordinated system rather than requiring separate management for each function or region.
Data Source
AI summary
There is provided a computer-implemented method of reinforcement learning for training a machine learning model configured to provision and/or deprovision virtual application instances of an application in a distributed compute network having one or more compute nodes, the method being performed by at least one hardware processor and the method comprising: a) defining an action space for a training environment representative of the distributed compute network, the training environment having a plurality of states and, for each state, one or more available actions in the action space, each action being operable to transition the distributed compute network in the training environment from a current state to a new state; b) selecting one or more available actions in the action space to define a new state; c) determining a reward for the one or more selected actions based upon a predicted change in load characteristics for the distributed compute network in the training environment in moving from the current state to the new state, the predicted change in load characteristics being determined at least in part based on training data relating to the distributed compute network, wherein the training data comprising simulated load data relating to one or more parameters of one or more compute nodes; and d) training the machine learning model using the predicted change in load characteristics and the reward values to generate an updated trained machine learning model. nodes for provisioning of virtual application instances of the application.