Multi-Cloud Service Mesh Orchestration via Reinforcement Learning
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Solution Overview
Problem
Current multi-cloud environments face challenges in deploying and managing microservice containers across multiple Cloud Service Providers (CSPs) due to lack of uniformity, leading to suboptimal application performance, increased costs, and limited flexibility in resource utilization and migration between clouds.
Innovation Solution
A multi-cloud service mesh orchestration platform that uses reinforcement learning to dynamically manage and deploy microservice containers across multiple CSPs, optimizing resource allocation based on Total Cost of Ownership (TCO) constraints and Service Level Agreements (SLA) requirements, allowing for flexible scaling and load balancing across different cloud instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If microservice containers are deployed across multiple CSPs, then flexibility and resource utilization improve, but deployment complexity and management difficulty increase
Solution Approach 1:
The patent segments the microservice application into independent containerized components that can be deployed across multiple CSPs. Each microservice container is self-contained with its own dependencies, allowing independent deployment and management on different cloud platforms without affecting other services.
Solution Approach 2:
The patent implements a universal service mesh architecture that provides unified management, networking, and orchestration capabilities across heterogeneous CSPs. The service mesh enables a single deployment framework to manage containers on multiple different cloud platforms, eliminating the need for cloud-specific deployment procedures.
2Productivity
If microservice containers are deployed across multiple CSPs, then resource optimization improves, but cost management complexity increases
Solution Approach 1:
The patent implements centralized monitoring and management capabilities that provide real-time feedback on resource utilization, performance metrics, and cost parameters across all CSPs. This feedback mechanism enables automated decision-making for resource allocation and load balancing, optimizing resource usage while simplifying cost management through unified visibility and control.
3Reliability
If microservice containers are deployed across multiple CSPs, then application performance can be improved, but network latency and connectivity challenges increase
Solution Approach 1:
The patent introduces a service mesh as an intermediary layer between microservice containers deployed on different CSPs. The service mesh handles networking, service discovery, load balancing, and communication protocols, abstracting away the complexity of cross-cloud connectivity and minimizing network latency through optimized routing and caching mechanisms.
Data Source
AI summary
A multi-cloud service mesh orchestration platform can receive a request to deploy an application as a service mesh application. The platform can tag the application with governance information (e.g., TCO, SLA, provisioning, deployment, and operational criteria). The platform can partition the application into its constituent components, and tag each component with individual governance information. For first time steps, the platform can select and perform a first set of actions for deploying each component to obtain individual rewards, state transitions, and expected returns. The platform can determine a reinforcement learning policy for each component that maximizes a total reward for the application based on the individual rewards, state transitions, and expected returns of each first set of actions selected and performed for each component. For second time steps, the platform can select and perform a second set of actions for each component based on the reinforcement learning policy for the component.