Multi-Cloud Network Function Deployment via Formal Model Checking
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Solution Overview
Problem
The complexity and cost of maintaining hybrid cloud networks are exacerbated by disparities in cloud environments and dynamic alterations, requiring enhanced deployment models that optimize cloud components and remap network topologies compatible with multi-cloud computing environments, especially when complex filters like application layer classification are needed.
Innovation Solution
A method is introduced to generate and validate deployment models using formal model checking, ensuring functional equivalence between logical and physical network topologies, with deployment generator instructions that automatically create optimized network topologies for multi-cloud environments, utilizing partial order reduction model checking to verify and remap network functions across public and private clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If deployment models use templates for network functions, then topology consistency is improved, but device complexity and cost increase when complex filters like application layer classification are needed
Solution Approach 1:
The patent segments the network function deployment into template-based standard functions and custom-filter functions. Templates handle common network functions with consistent topology, while custom filters are implemented separately using virtual machine elements with flexible filtering capabilities, avoiding the need to complexify all deployment models.
Solution Approach 2:
The patent creates a universal deployment model framework that can accommodate both template-based network functions and custom-filter virtual machine elements. This multi-functional approach allows the same deployment infrastructure to support simple standardized functions and complex customized functions without requiring separate specialized systems.
2Stability of the object's composition
If deployment models use templates for network functions, then topology consistency is improved, but cost increases
Solution Approach 1:
The patent segments deployment costs into standardized template costs and custom virtual machine costs. By segmenting, organizations can use cost-effective templates for standard functions and only incur higher custom VM costs when complex filters are truly needed, rather than paying for template complexity across all deployments.
Solution Approach 2:
The patent applies local quality by using simple, cost-effective template deployments for standard network functions where high consistency is needed, and selectively deploying more expensive custom virtual machine elements only at specific locations where complex application layer classification is required, optimizing the cost-quality balance.
3Adaptability or versatility
If hybrid cloud networks are maintained with disparities in cloud environments, then flexibility in service selection is improved, but management complexity and maintenance cost increase
Solution Approach 1:
The patent creates a universal deployment model that functions across multiple cloud environments (public and private). This multi-functional framework provides a consistent management interface and deployment methodology that works across disparate cloud platforms, reducing management complexity while preserving the ability to select services from different cloud providers.
Solution Approach 2:
The patent introduces deployment models as an intermediary layer between the enterprise and multiple cloud environments. This mediator abstracts the disparities between different cloud platforms, providing a unified management approach while allowing flexible service selection from various cloud providers underneath.
4Adaptability or versatility
If complex filters like application layer classification are implemented using special-purpose virtual network elements, then filtering capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses virtual machine elements as software-based copies of filtering functionality rather than requiring physical special-purpose hardware for each filter type. This virtualization approach provides complex application layer classification capabilities through software, reducing hardware device complexity while maintaining filtering versatility.
Data Source
AI summary
In an embodiment, a computer-implemented method comprises receiving logical model input that specifies a logical topology model of networking elements and/or computing elements for deployment at least partially in a private cloud computing infrastructure and at least partially in a public cloud computing infrastructure; receiving resource input specifying an inventory of computing elements that are available at least partially in the private cloud computing infrastructure and at least partially in the public cloud computing infrastructure; automatically generating an intermediate topology comprising a set of deployment instructions that are capable of execution at least partially in the private cloud computing infrastructure and at least partially in the public cloud computing infrastructure to cause physical realization of a network deployment corresponding to the logical topology model; determining whether the intermediate topology is functionally equivalent to the logical topology model; in response to determining that the intermediate topology is functionally equivalent to the logical topology model, transmitting the deployment instructions at least partially to the private cloud computing infrastructure and at least partially to the public cloud computing infrastructure.