Virtualized Service Deployment for Cloud Interoperability Constraints
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Solution Overview
Problem
The deployment of virtualized services on distributed cloud infrastructures is complex due to varying hardware, software, and networking capabilities across geographical locations, with existing methods lacking a unified, automated process to handle latency, cost, and resource constraints, leading to inefficient manual iterations and suboptimal placements.
Innovation Solution
A method and system for automated deployment of virtualized services that jointly consider service function placement and definition, using a service orchestrator to iteratively select computing systems and network links based on service specifications and infrastructure characteristics, ensuring global interoperability and optimizing deployment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual deployment processes are used for virtualized service functions on distributed cloud infrastructure, then deployment flexibility is maintained, but deployment efficiency and consistency deteriorate due to ad-hoc iterations and lack of unified process
Solution Approach 1:
The system enables self-service deployment by automatically selecting service function specifications, determining computing systems and network links, and generating deployment plans without manual intervention. The service orchestrator autonomously handles the entire deployment process, including selecting computing systems based on availability and characteristics, and generating deployment plans that satisfy interoperability requirements.
Solution Approach 2:
The system changes deployment parameters dynamically by selecting from multiple service function specifications and determining optimal computing systems and network links based on availability and characteristics. The deployment plan adapts parameters such as computing system selection, network link configuration, and service function assignment to optimize deployment while satisfying constraints.
2Extent of automation
If automated deployment processes are implemented to improve deployment efficiency, then deployment speed increases, but the complexity of handling interoperability requirements and constraints increases
Solution Approach 1:
The system segments the deployment process into distinct automated steps: selecting service function specifications, determining computing systems and network links, assigning service functions to computing systems, and generating deployment plans. Each segment handles specific interoperability requirements independently, making the overall complex process manageable and automatable.
Solution Approach 2:
The service orchestrator acts as an intermediary that automatically handles interoperability constraints by mediating between service function requirements and cloud infrastructure capabilities. It translates interoperability requirements into concrete deployment decisions, selecting appropriate computing systems and network links that satisfy the constraints without requiring manual intervention.
3Adaptability or versatility
If service functions are deployed across multiple geographical locations with varying capabilities, then service availability and scalability are improved, but deployment complexity and difficulty of ensuring interoperability increase
Solution Approach 1:
The system implements a universal deployment process that works across multiple geographical locations with varying capabilities. The service orchestrator uses a standardized approach to select service function specifications and determine computing systems and network links regardless of location, ensuring consistent interoperability management across diverse cloud infrastructures.
Solution Approach 2:
The system adapts to local characteristics of each geographical location by determining computing systems and network links based on their specific availability and characteristics. The deployment process considers local capabilities while maintaining overall interoperability, allowing service functions to be deployed optimally at each location according to its specific properties.
4Manufacturing precision
If iterative manual selection of computing systems and network links is performed, then deployment accuracy can be optimized, but deployment time and resource consumption increase
Solution Approach 1:
The system performs preliminary automated actions by pre-selecting service function specifications and pre-determining computing systems and network links based on availability and characteristics before final deployment. This preliminary automated selection reduces the need for iterative manual optimization, achieving placement optimization more quickly by establishing a solid foundation in advance.
Solution Approach 2:
The system uses feedback mechanisms to automatically refine deployment decisions. The service orchestrator evaluates the results of selecting computing systems and network links, and uses this feedback to improve subsequent selections, achieving optimized placement automatically without requiring manual iterative adjustments.
Data Source
AI summary
A method and system of deployment of a virtualized service on a cloud infrastructure are described. A first service function specification of a first service function is selected. A determination of a set of the computing systems and a set of the links is performed based on availability and characteristics of the computing systems and the network resources in the cloud infrastructure. A selection of a first computing system to be assigned to host the first service function and links is performed based on the first service function specification and based on interoperability requirements for the first service function and one or more other ones of the service functions that form the virtualized service. The selection of a service function and the determination of a computing system and links is repeated for the remaining service functions until all of the service functions are assigned to resources in the cloud infrastructure.