Virtualized Service Function Placement With Joint Definition
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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 and repetitive manual iterations in service function placement and definition.
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 determine optimal hosting locations and network connections based on infrastructure availability and characteristics, reducing manual intervention and ensuring dynamic, optimal deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual ad-hoc deployment process is used for virtualized service functions, then flexibility in handling diverse infrastructure characteristics is maintained, but deployment complexity and time consumption increase significantly
Solution Approach 1:
The system enables self-service through automated service function placement and definition. The service orchestrator automatically discovers infrastructure characteristics, selects appropriate service function specifications, and places them on suitable computing systems without requiring manual intervention. This automation resolves the contradiction by making the deployment process self-sufficient while adapting to diverse infrastructure characteristics.
Solution Approach 2:
The system dynamically changes parameters such as service function placement decisions, specification selections, and deployment configurations based on discovered infrastructure characteristics. By automatically adjusting these parameters according to the specific cloud infrastructure environment, the system maintains flexibility while reducing deployment complexity through systematic parameter optimization.
2Reliability
If manual iteration is used for service function placement, then constraints can be considered, but deployment time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by automatically discovering infrastructure characteristics and pre-evaluating suitable service function placements before actual deployment. The service orchestrator proactively identifies computing systems that meet constraints and prepares deployment configurations in advance, eliminating the need for repetitive manual iterations while ensuring constraint satisfaction.
Solution Approach 2:
The system implements feedback mechanisms where the service orchestrator continuously monitors deployment status, constraint satisfaction, and infrastructure characteristics. Based on this feedback, the system automatically adjusts service function placement decisions and re-evaluates configurations, ensuring constraints are met while minimizing deployment time through iterative optimization rather than manual retrying.
3Productivity
If automated service function placement is implemented, then deployment efficiency improves, but adaptability to varying infrastructure capabilities across geographical locations may be reduced
Solution Approach 1:
The system achieves both automation and adaptability through dynamic service function placement. The service orchestrator automatically discovers infrastructure characteristics at each geographical location and dynamically adjusts placement decisions based on the specific capabilities of computing systems, network resources, and storage systems. This dynamic adaptation allows automated deployment while respecting local infrastructure variations.
Solution Approach 2:
The system segments the deployment process into independent evaluation stages for each service function and computing system. By automatically evaluating infrastructure characteristics at each location and making localized placement decisions, the system maintains adaptability to varying capabilities while achieving overall deployment efficiency through systematic automation across distributed locations.
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. In response to determining that there is one or more the service functions that are not yet assigned, the selection of the service function and the determination of the computing system and links are repeated for each remaining functions until all of service function are assigned to computing and networking resources in the cloud infrastructure.