VNF Deployment Optimization for NFV Resource Costs
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Solution Overview
Problem
Network function virtualization (NFV) deployments face resource costs associated with provisioning and deprovisioning virtualized network functions (VNFs), which can be inefficient due to the need for managing and scaling these resources across a virtualized computing environment.
Innovation Solution
A method for determining a VNF deployment plan that optimizes the number of VNF instances to be deployed or released over a planning period, based on constraints such as demand, capacity, and resource consumption, using an optimization function to minimize the number of deployed VNFs and resource costs, with the plan executed by a processor and memory system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If VNFs are deployed repeatedly for multiple services, then service provisioning flexibility is improved, but resource costs and deployment complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-deploying a pool of VNF instances in an undeployed state before services are actually needed. The optimization function proactively determines future deployment needs and pre-positions VNFs, so when services are provisioned, VNFs are already available for immediate deployment, eliminating the need for repeated resource allocation and reducing overall resource consumption.
Solution Approach 2:
Multiple service requirements are merged into a unified optimization problem. The optimization function consolidates deployment decisions for different services and time periods into a single coordinated plan, sharing common VNF instances across multiple services where possible, thereby reducing the total number of VNF instances needed while maintaining service provisioning flexibility.
2Productivity
If VNF instances are frequently deployed and released, then service demand responsiveness is improved, but deployment time and resource overhead increase
Solution Approach 1:
VNF instances are pre-deployed and held in an undeployed state in advance, so when service demand arises, the VNFs are already prepared and can be deployed immediately without waiting for resource allocation or initialization time, significantly reducing deployment lead time while maintaining responsiveness to service demands.
Solution Approach 2:
The system dynamically adjusts the state of VNF instances between undeployed and deployed based on real-time service demands. The optimization function continuously monitors service requirements and dynamically transitions VNFs between states, allowing the system to respond flexibly to changing demands while minimizing the time spent in transition states.
3Reliability
If the number of VNF instances is increased to meet peak demand, then service capacity is improved, but resource consumption and cost increase
Solution Approach 1:
The optimization function performs preliminary analysis of future service demand patterns and pre-deploys the exact number of VNF instances needed to meet peak capacity requirements. By anticipating future demand rather than reacting to it, the system maintains sufficient service capacity during peak periods while avoiding the resource consumption associated with continuously maintaining oversized VNF pools.
Solution Approach 2:
The system changes the operational parameters of VNF instances by transitioning them between undeployed and deployed states based on actual service demands. Rather than maintaining a fixed large pool of active VNFs, the system dynamically adjusts the active VNF population parameter, deploying additional instances only when needed to meet capacity requirements and releasing them when demand decreases, thereby optimizing the balance between service capacity and resource consumption.
Data Source
AI summary
A method for deploying virtualized network functions (VNFs) as virtualized implementations of logical network devices in a virtualized computing environment for providing network services, the method including determining a VNF deployment plan including a specification, for each point in time over a planning period, of zero or more VNF instances to be released at the point in time and zero or more VNF instances to be deployed at the point in time, wherein the deployment plan is determined by an optimization function based on: a constraint on a number of VNFs in all states of undeployed, being deployed, deployed and being release; constraints based on a demand for VNFs defined by characteristics of the VNFs and the virtualized computing environment; and constraints based on a capacity of the virtualized computing environment to accommodate a resource consumption of the VNFs, and wherein the optimization function is configured to minimize one or more of: a number of VNFs in a deployed state; and a resource cost of deploying and releasing VNFs; and executing the VNF deployment plan to deploy VNFs for the virtualized computing environment.