Microservice Resource Provisioning with Guided Reinforcement Learning
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Solution Overview
Problem
Traditional infrastructure management systems struggle with manual scaling of resources for microservices, leading to inefficiencies such as SLA violations, poor resource utilization, and wastage, especially in large-scale and dynamic computing environments.
Innovation Solution
A reinforcement learning framework with a guided order of learning technique that orders microservices based on similarity analysis, using metrics like DTW and cosine similarity, to optimize resource configurations through a multi-armed bandit agent, enabling sample-efficient learning and resource provisioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If manual scaling of resources is used for microservices, then resource provisioning can be controlled, but resource utilization efficiency deteriorates and SLA violations occur
Solution Approach 1:
The system implements self-service through reinforcement learning agents that automatically provision and scale resources for microservices without manual intervention. The agents learn optimal resource allocation policies through continuous interaction with the computing environment, enabling the system to autonomously adjust resource provisioning based on actual service demands and performance metrics.
Solution Approach 2:
The system employs feedback mechanisms where reinforcement learning agents continuously monitor service performance metrics, resource utilization rates, and SLA compliance status. This feedback loop enables the agents to learn from past decisions and adjust resource provisioning strategies in real-time, improving resource utilization efficiency while maintaining automated control.
2Reliability
If resources are over-provisioned to prevent SLA violations, then service reliability is improved, but resource wastage increases
Solution Approach 1:
The system applies dynamics by transitioning from static resource provisioning to dynamic, adaptive resource allocation. Reinforcement learning agents continuously adjust resource provisioning decisions based on real-time service demands, workload patterns, and performance metrics. This dynamic approach ensures service reliability is maintained while avoiding resource wastage by provisioning only what is needed.
Solution Approach 2:
The system implements parameter changes by adjusting resource allocation parameters (CPU, memory, storage) based on learned policies from reinforcement learning. The agents optimize these parameters dynamically to maintain service reliability thresholds while minimizing resource consumption, preventing both SLA violations and resource wastage through data-driven parameter tuning.
3Measurement precision
If reinforcement learning learns without guided ordering, then learning completeness is improved, but sample efficiency deteriorates in large-scale environments
Solution Approach 1:
The system applies preliminary action by performing similarity analysis and ordering microservices before the reinforcement learning process begins. This pre-processing step groups similar microservices together, enabling the learning agent to generalize knowledge more effectively across similar services. The guided ordering reduces the exploration space and accelerates convergence, improving sample efficiency without sacrificing learning completeness.
Solution Approach 2:
The system implements segmentation by dividing the large-scale microservice environment into smaller, similarity-based groups or clusters. The reinforcement learning agent learns policies for each segment independently before generalizing to similar segments, reducing the overall learning complexity. This segmentation approach maintains comprehensive learning coverage while significantly improving sample efficiency in large-scale environments.
Data Source
AI summary
Embodiments of the present disclosure provide systems, methods, and computer storage media for resource provisioning of microservices using guided order of learning in a reinforcement learning framework. In embodiments, service resource information relating to microservices operating in a computing environment is received and used to perform a similarity analysis to generate similarity scores for each of the services. The service resource information is ordered based on a closeness between the similarity scores of the services. The ordered service resource information is inputted into a reinforcement learning agent to generate a resource configuration determination of at least one service of the services. The resource configuration determination is then provided to a provisioning component associated with the computing environment for provisioning the microservice.