Adaptive Resource Allocation for Virtual Network Functions
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Solution Overview
Problem
Current VMM schedulers prioritize fairness in computing resource allocation for virtual network functions (VNFs), leading to resource shortages and waste, which impede network service performance due to diverse resource requirements based on ingress packet rates and types.
Innovation Solution
An adaptive computing resource allocation approach that predicts real-time VNF resource requirements through offline profiling and online monitoring, reallocating resources using either a direct or incremental approach, adjusting task priorities to ensure each VNF receives the necessary computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fair resource allocation strategy is used in VMM scheduler, then resource distribution fairness is improved, but network service performance deteriorates due to resource mismatch with actual VNF requirements
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring VNF performance metrics and adjusting resource allocation in real-time based on actual needs. The system transitions from static fair allocation to dynamic allocation that adapts to changing traffic conditions and VNF requirements, resolving the contradiction between fairness and performance.
Solution Approach 2:
The system changes allocation parameters by using performance metrics (CPU usage, memory usage, packet processing rate) as the basis for resource allocation decisions. Instead of equal allocation, resources are allocated proportionally to actual usage patterns and performance requirements, optimizing overall network service performance while maintaining appropriate fairness.
2Device complexity
If equal computing resources are allocated to all VNFs, then allocation simplicity is improved, but resource utilization efficiency deteriorates due to diverse VNF requirements
Solution Approach 1:
The patent implements a feedback mechanism that monitors VNF performance metrics and uses this information to adjust resource allocation. The system collects data on actual resource usage and performance outcomes, then feeds this information back to the allocation algorithm to optimize future allocations, improving efficiency without excessive complexity.
Solution Approach 2:
The system enables VNFs to effectively request and receive resources based on their own performance needs. Through automated monitoring and allocation algorithms, each VNF can be seen as serving its own resource requirements, with the system automatically matching supply to demand without manual intervention, improving both efficiency and simplicity.
Data Source
AI summary
The present invention provides an adaptive computing resource allocation approach for virtual network functions, including the following two steps: Step 1: predicting VNFs' real-time computing resource requirements; Step 1.1: offline profiling different types of VNFs, to obtain a parameter relation between the required amount of computing resources and the ingress packet rate; Step 1.2: online monitoring the network traffic information of each VNF, and predicting VNFs' required amount of computing resources with combination of the parameters in Step 1.1; Step 2: reallocating computing resources based on VNFs' resource requirements. The computing resource allocation approach includes a direct allocation approach and an incremental approach. The adaptive computing resource allocation approach for virtual network functions of the present invention allocates computing resources based on VNFs' actual requirements, and remedies performance bottlenecks caused by fair allocation.