Dynamic Inter-Cloud VNF Placement for Network Slicing
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Solution Overview
Problem
Current network slicing technologies are confined to a single data center and fail to efficiently manage dynamic multi-cloud demands, leading to bottlenecks and inefficiencies in resource allocation.
Innovation Solution
A dynamic inter-cloud optimizer is introduced to determine optimal slice paths across multiple clouds, balancing Service Level Agreements (SLAs) with cloud load, and dynamically placing Virtual Network Functions (VNFs) to ensure efficient resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static VNF placement is used in a single data center, then implementation simplicity is maintained, but network resource utilization efficiency deteriorates under dynamic multi-cloud demands
Solution Approach 1:
The patent implements dynamic VNF placement that continuously monitors cloud load conditions and automatically adjusts slice paths in real-time. The system transitions from static to dynamic configuration, allowing VNFs to be relocated across multiple clouds based on current resource availability and demand fluctuations, thereby optimizing network resource utilization while maintaining manageable complexity through automated orchestration
Solution Approach 2:
The patent creates a multi-cloud orchestration framework that enables a single slice path to operate across multiple cloud infrastructure providers. This universal approach allows the same network slice to leverage resources from different clouds dynamically, improving overall resource utilization efficiency without requiring separate static configurations for each cloud environment
2Adaptability or versatility
If multiple slice paths are reserved statically across multiple clouds, then demand fluctuations at different locations can be accommodated, but bottlenecks and inefficiencies are introduced
Solution Approach 1:
The system dynamically adjusts slice paths in real-time based on current cloud load conditions rather than maintaining static reservations. When demand fluctuates, the orchestration framework automatically reroutes traffic through alternative cloud paths, accommodating varying demands without creating the bottlenecks associated with over-provisioning static paths
Solution Approach 2:
The patent changes the operational parameters of slice paths from fixed static configurations to dynamic parameters that adapt to real-time cloud load conditions. The system monitors resource availability metrics and adjusts slice path selection accordingly, allowing flexible accommodation of demand fluctuations while maintaining optimal network performance efficiency
3Stability of the object's composition
If existing slice paths are used during high-demand events, then infrastructure is maintained, but service level agreement requirements are violated
Solution Approach 1:
The system maintains infrastructure stability by keeping existing slice paths operational while dynamically introducing alternative paths when needed. During high-demand events, the orchestration framework activates backup or alternative slice paths without disrupting the core infrastructure, ensuring both stability and SLA compliance through dynamic path selection
Solution Approach 2:
The patent implements preliminary action by pre-configuring multiple potential slice paths and monitoring their availability in advance. When high-demand events are detected or anticipated, the system can switch to pre-prepared alternative paths, ensuring SLA requirements are met without compromising infrastructure stability
Data Source
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AI summary
Examples can include an optimizer that dynamically determines where to place virtual network functions for a slice in a distributed Telco cloud network. The optimizer can determine a slice path that complies with a service level agreement and balances network load. The virtual network functions of the slice can be provisioned at clouds identified by the optimal slice path. In one example, performance metrics are normalized, and tenant-selected weights can be applied. This can allow the optimizer to prioritize particular SLA attributes in choosing an optimal slice path.