Network Function Virtualization Server Placement Optimization
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Solution Overview
Problem
Network function virtualization systems face challenges in determining the optimal server for instantiating new services due to multi-constraint optimization problems, particularly in distributed edge data centers with constraints like energy, computing power, storage, and high availability, which existing solutions fail to address effectively.
Innovation Solution
A system and method using dynamic bin packing and linear programming to optimize server loading, with bit vectors representing constraints, and an algorithm that selects racks and servers based on these constraints to ensure efficient placement of virtual network functions (VNFs)/virtual machines (VMs)/containers, considering factors like hardware acceleration, reliability, and proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional network function virtualization systems are used, then service deployment is possible, but optimal server selection fails due to inability to handle multi-constraint optimization
Solution Approach 1:
The patent transforms the multi-constraint optimization problem into a bit vector representation where each constraint (energy, computing power, storage, high availability) is encoded as a binary parameter. This parameter transformation enables efficient comparison and selection of optimal servers by converting complex constraints into comparable bit patterns that can be processed algorithmically.
Solution Approach 2:
The patent replaces traditional manual or rule-based server selection mechanisms with an automated algorithmic system. The algorithm processes service requests, evaluates bit vector representations of server constraints, and automatically selects optimal servers, substituting mechanical decision-making processes with computational automation.
2Reliability
If servers are distributed across multiple data centers, then service availability improves, but energy consumption and resource utilization efficiency deteriorate
Solution Approach 1:
The patent implements dynamic server selection that adapts to current system state. The bit vector representation and algorithm allow real-time evaluation of server availability versus energy consumption, enabling the system to dynamically adjust service placement decisions based on current constraints and optimize the balance between availability and energy efficiency.
Solution Approach 2:
The system uses feedback from constraint evaluation to improve service placement decisions. By continuously evaluating bit vectors representing server constraints and outcomes, the algorithm learns and adjusts its selection process to better balance availability requirements with energy consumption considerations.
3Productivity
If more servers are allocated to handle service requests, then service capacity increases, but resource utilization efficiency decreases
Solution Approach 1:
The patent applies partial action by allocating server resources precisely when and where needed based on service requests and constraint evaluation. Rather than over-provisioning servers, the bit vector algorithm enables selective activation and utilization of only the necessary server capacity, improving resource utilization efficiency while maintaining adequate service capacity.
Data Source
AI summary
A system for managing networked devices comprising a plurality of racks of computing devices, each rack computing device further comprising a plurality of constraints and configured to operate one or more hosted systems, each rack comprising a bit vector stored in a data memory defining the minimum available set of constraints for each of the plurality of rack computing devices. A plurality of enterprises, each comprising one or more enterprise computing device configured to operate a virtual network system that is configured to interactively operate with the one or more hosted systems of one of the rack computing devices. A network function virtualization system configured to receive a service request and to determine that a new hosted system is required to respond to the service request, and to select a rack for instantiation of the new hosted system as a function of the plurality of bit vectors.