VNF Allocation via Extinction Factor Computation
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Solution Overview
Problem
The challenge in allocating Virtual Network Functions (VNFs) in a datacenter is complex due to varying computing resource requirements and constraints, such as affinity, anti-affinity, and exclusion rules, which can lead to idle resources despite available capacity, as seen in the example where VM3 cannot be allocated despite sufficient total idle space due to restrictions.
Innovation Solution
A computing system comprising a processing unit, computation module, and allocation module that determines an extinction factor for each datacenter unit based on its state and the VNF catalogue, developing an allocation model to efficiently allocate VNFs by selecting the datacenter unit with the highest extinction factor or minimizing resource extinction, thereby optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If VNFs are allocated based on simple available capacity, then allocation speed is improved, but resource utilization deteriorates due to idle resources from affinity and anti-affinity constraints
Solution Approach 1:
The system pre-calculates extinction factors for all datacenter units before actual VNF allocation. These extinction factors represent the impact of allocating a VNF to each datacenter unit, considering all affinity and anti-affinity constraints in advance. When a VNF needs to be allocated, the system simply compares pre-computed extinction factors and selects the datacenter unit with the minimum extinction factor, achieving both fast allocation and optimal resource utilization.
2Measurement precision
If complex allocation rules (affinity, anti-affinity, exclusion) are enforced, then allocation accuracy is improved, but computational complexity increases
Solution Approach 1:
The system transforms the complex multi-constraint allocation problem into a simple parameter comparison by defining extinction factors. Each extinction factor is a single numerical value that encapsulates the combined effect of all affinity, anti-affinity, and exclusion constraints for a particular datacenter unit. This parameter transformation allows the system to maintain high allocation accuracy while reducing computational complexity during the actual allocation process.
3Reliability
If available capacity is not considered in allocation, then constraint satisfaction is improved, but resource waste increases
Solution Approach 1:
The system incorporates feedback from both constraint satisfaction and resource availability into the extinction factor calculation. The extinction factor for each datacenter unit is determined by evaluating how the allocation would affect constraint satisfaction (affinity, anti-affinity, exclusion rules) and how it would impact remaining available capacity. This feedback mechanism ensures that allocations satisfy all constraints while simultaneously optimizing resource utilization by selecting datacenter units with sufficient available capacity.
Data Source
AI summary
Virtual Network Functions (VNF) applies automation and virtualization techniques to move current network functions from dedicated hardware to general purpose hardware of an Information Technology (IT) infrastructure. A VNF may include one or more Virtual Machines (VM) and virtual networks which may implement the function of a network. Systems and methods provide a processing unit, a computation module and an allocation module for VNF allocation. The computation module is configured to determine an extinction factor corresponding to a datacenter unit based on a state of the datacenter and a VNF catalogue including a plurality of VNFs. The computation module is also configured to develop an allocation model based on the determined extinction factor. The allocation module is configured to allocate a first VNF from the plurality of VNFs in the datacenter based on the allocation model.