Multi-Cloud Orchestration via Pattern Decision Trees
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Solution Overview
Problem
Current systems for managing computing resources across different cloud technologies, such as Infrastructure as a Service (IaaS) and Container as a Service (CaaS), face inefficiencies in workload distribution, leading to suboptimal utilization of resources and increased costs due to the lack of effective orchestration patterns and easy workload shifting between these environments.
Innovation Solution
A method that employs machine learning to monitor and analyze utilization data from IaaS and CaaS clouds, generating and optimizing orchestration patterns through a pattern decision tree, which automatically deploys computing resources across environments based on user requirements, ensuring balanced resource utilization and efficient placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud providers monitor and manage VMs and containers separately in discrete cloud areas, then each cloud technology can be managed independently, but resource utilization becomes inefficient and costs increase
Solution Approach 1:
The patent combines IaaS and CaaS cloud environments into a unified multi-cloud orchestration system. The system manages both virtual machines and containers across different cloud providers simultaneously, enabling workloads to be shifted between them based on utilization metrics, thereby improving overall resource efficiency while maintaining adaptability.
Solution Approach 2:
The orchestration system provides universal management capabilities across multiple cloud technologies and providers. It can monitor, analyze, and deploy workloads on both IaaS and CaaS environments, as well as across public, private, and hybrid clouds, making the system versatile and adaptable to different cloud infrastructures.
2Ease of operation
If cloud providers manage resources from vendor perspective rather than user holistic business view, then vendor-specific optimizations are achieved, but overall business efficiency and resource placement optimization are reduced
Solution Approach 1:
The patent introduces an intermediary orchestration system that sits between users and multiple cloud providers. This intermediary abstracts the complexity of managing different cloud technologies by providing a unified interface for resource deployment, while internally handling the complex tasks of monitoring, analysis, and automated placement across IaaS and CaaS environments.
Solution Approach 2:
The orchestration system performs self-service by automatically monitoring resource utilization, analyzing deployment patterns, and making intelligent decisions about workload placement without requiring manual user intervention. The system autonomously shifts workloads between clouds based on real-time metrics, reducing operational complexity for users.
3Adaptability or versatility
If workload shifting between IaaS and CaaS clouds is implemented without effective orchestration patterns, then flexibility is improved, but system complexity and difficulty of implementation increase
Solution Approach 1:
The patent implements preliminary action by pre-establishing orchestration patterns and deployment strategies for workload shifting between IaaS and CaaS clouds. The system analyzes historical data and utilization patterns to pre-determine optimal placement strategies, reducing the complexity of real-time decision-making and simplifying the implementation of flexible workload management.
Data Source
AI summary
An approach is provided for orchestrating computing resources between different computing environments. Data from first and second computing environments is monitored. The data specifies utilization of infrastructure, middleware, software testing tools, integrated development environments (IDEs), relationships among nodes, utilization of the nodes, and user behavior in the first and second computing environments. Based on the utilization of the infrastructure, middleware, tools, IDEs, node relationships and utilization, and user behavior, a pattern decision tree is updated, and unbalanced workloads are identified. Based on a comparison of the unbalanced workloads to patterns in the updated pattern decision tree, an orchestration topology is generated that specifies a new placement of the computing resources in the first and second computing environments. Based on the orchestration topology, computing resource(s) are automatically deployed in the first computing environment and other computing resource(s) are automatically deployed in the second computing environment.