Virtualized Service Placement Across Variable Cloud Infrastructure
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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 consider latency, cost, and resource constraints, leading to inefficient manual iterations and deployment failures.
Innovation Solution
A method and system for automatic deployment of virtualized services that jointly consider service function placement and definition, using a service orchestrator to determine optimal hosting locations and network connections based on infrastructure availability and characteristics, reducing manual intervention and optimizing deployment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual ad-hoc deployment process is used for virtualized service functions, then flexibility in deployment is maintained, but deployment efficiency and reliability deteriorate due to lack of automated optimization
Solution Approach 1:
The system enables self-service deployment by automatically evaluating multiple candidate placements against service requirements and selecting optimal configurations without manual intervention. The service orchestrator autonomously performs deployment decisions based on infrastructure characteristics and service constraints.
Solution Approach 2:
The system changes deployment parameters dynamically by evaluating different placement configurations and selecting the optimal one based on varying service requirements, infrastructure capabilities, and performance constraints. This allows automated optimization while maintaining adaptability to different deployment scenarios.
2Device complexity
If manual deployment process is used, then simplicity of process is maintained, but deployment reliability and compliance with service requirements worsen due to iterative manual adjustments
Solution Approach 1:
The system performs preliminary evaluation of deployment options before actual deployment by assessing infrastructure characteristics, service requirements, and constraints in advance. This preliminary analysis ensures that the selected deployment configuration meets all requirements before implementation, improving reliability without adding operational complexity.
Solution Approach 2:
The system incorporates feedback mechanisms that evaluate deployment configurations against service requirements and constraints, automatically adjusting selections to ensure compliance. This feedback-driven approach guarantees reliable deployments while maintaining process simplicity through automated decision-making.
3Productivity
If automated service function placement is implemented, then deployment efficiency is improved, but system complexity increases due to need to evaluate multiple constraints and infrastructure variations
Solution Approach 1:
The system segments the deployment evaluation process into distinct components: infrastructure characterization, service requirement analysis, candidate generation, and selection. This segmentation manages complexity by breaking down the automated decision-making into modular, manageable steps while maintaining high deployment efficiency.
Solution Approach 2:
The service orchestrator acts as an intermediary that manages the complexity of automated deployment by mediating between infrastructure capabilities and service requirements. It translates complex constraints into actionable deployment decisions, maintaining efficiency while handling system complexity through centralized coordination.
4Adaptability or versatility
If distributed cloud infrastructure with varying capabilities is used, then service versatility and adaptability are improved, but deployment complexity and difficulty of determining optimal placement worsen
Solution Approach 1:
The system applies local quality by evaluating infrastructure characteristics at each potential deployment location and matching them with specific service requirements. This allows the system to leverage the unique capabilities of different distributed locations while managing deployment complexity through localized assessment and matching.
Solution Approach 2:
The system handles distributed infrastructure variations by dynamically changing evaluation parameters based on local infrastructure characteristics. It adapts its selection criteria to match service requirements with the specific capabilities of each location, maintaining versatility while managing complexity through parameter-based flexibility.
Data Source
AI summary
A method and system of deployment of a virtualized service on a cloud infrastructure while taking into consideration variability/elasticity of the cloud resources are described. A first service function specification is selected. Candidate computing systems and candidate links are determined. The determination of the candidates is performed based on the availability and characteristics of the computing systems and the network resources in the cloud infrastructure which includes varying characteristics. A first computing system and one or more links are determined from the candidates for placing the first service function in the cloud infrastructure.