Reinforcement Learning Microservice Placement for Latency-Cost Tradeoffs
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Solution Overview
Problem
Existing technologies face challenges in optimizing microservices performance in cloud and edge computing environments, particularly in automatically placing microservices to balance latency and cost effectively.
Innovation Solution
The use of reinforcement learning to learn actions for an agent based on states and associated rewards related to the cost and latency of microservices, generating an optimal action to place microservices in the most suitable location within a cloud and edge computing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If microservices are placed at edge locations to reduce latency, then response time is improved, but operational cost increases
Solution Approach 1:
The patent implements dynamic microservice placement that adapts to changing conditions. The reinforcement learning agent continuously learns and adjusts placement decisions based on real-time state observations, transforming the static placement problem into a dynamic optimization process that responds to workload variations, resource availability, and performance requirements
Solution Approach 2:
The system changes placement parameters (location, resource allocation) based on learned patterns. The reinforcement learning model adjusts placement decisions by modifying key parameters such as edge vs. cloud location selection, resource provisioning levels, and service replication factors to optimize the trade-off between latency and cost
2Loss of energy
If microservices are placed in cloud environment to reduce cost, then operational cost is reduced, but latency increases
Solution Approach 1:
The patent applies local quality by placing different microservices in different locations (edge vs. cloud) based on their specific requirements. Critical latency-sensitive services are placed at edge locations while less time-critical services remain in the cloud, optimizing the overall system by making localized placement decisions for each microservice
3Device complexity
If manual placement methods are used, then system complexity is reduced, but optimization capability deteriorates
Solution Approach 1:
The system implements self-service through autonomous reinforcement learning agents that automatically make placement decisions without human intervention. The agents learn optimal placement strategies through interaction with the environment and autonomously adjust microservice placements, enabling the system to optimize itself while reducing manual operational complexity
Data Source
AI summary
Systems and methods for automatic placement of microservices with reinforcement learning. Actions for an agent based on states and an associated reward for the actions based on the cost and latency of microservices of a distributed computing application can be learned by a reinforcement learning model. An optimal action based on the actions having the top ranked associated reward that maximizes a reward value based on the cost and the latency of microservices can be generated with the reinforcement learning model. The microservices can be placed to an optimal location that satisfies the latency and the cost of the microservices within a cloud and edge computing environment based on the optimal action.