Soft Preemption Coordination in MPLS Traffic Engineering
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Solution Overview
Problem
In MPLS TE networks, the existing soft preemption methods often result in excessive traffic disruption due to the high number of LSP preemptions, as the decision to compute candidate LSPs for soft preemption is local to each node, leading to unnecessary preemptions and network disruptions.
Innovation Solution
A method is introduced that involves marking LSPs for soft preemption, allowing temporary reservation of bandwidth for new higher-priority paths while continuing to deliver traffic on preempted paths, and coordinating preemption decisions across nodes to minimize the number of preemptions through error and change messages within the RSVP protocol framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If soft preemption is implemented with local decision-making at each node, then bandwidth reservation for high-priority LSPs can be achieved, but excessive number of LSP preemptions occurs leading to network disruption
Solution Approach 1:
The patent merges the preemption decision-making process from distributed local decisions at individual nodes to a centralized coordination mechanism. The head-end node computes the set of candidate LSPs for soft preemption by considering the entire path, and coordinates with downstream nodes to minimize the total number of preemptions across the network, rather than each node independently making preemption decisions.
Solution Approach 2:
The patent implements feedback mechanisms where downstream nodes send preemption status information back to the head-end node. This allows the head-end node to adjust its preemption strategy based on actual network conditions and preemption outcomes, reducing unnecessary preemptions while still ensuring bandwidth allocation for high-priority LSPs.
2Reliability
If multiple LSPs are preempted to accommodate high-priority traffic, then bandwidth guarantee for new LSPs is achieved, but traffic disruption on preempted LSPs increases
Solution Approach 1:
The patent performs preliminary computation of the set of candidate LSPs for soft preemption at the head-end node before actually executing preemptions. By预先 calculating which LSPs should be preempted based on the entire path requirements and coordinating with downstream nodes, the system minimizes the number of preemptions needed while still guaranteeing bandwidth for the new high-priority LSP.
Solution Approach 2:
The patent changes the parameter of preemption decision from local binary decisions at each node to a global optimized selection based on path-level analysis. The head-end node evaluates multiple candidate LSPs and selects the optimal set for preemption that minimizes traffic disruption while satisfying bandwidth requirements, rather than allowing each node to independently preempt LSPs.
3Adaptability or versatility
If local nodes independently compute candidate LSPs for soft preemption, then routing flexibility is maintained, but coordination overhead and preemption conflicts increase
Solution Approach 1:
The patent extracts the complex preemption decision-making function from individual downstream nodes and concentrates it at the head-end node. The head-end node is responsible for computing the set of candidate LSPs for soft preemption and coordinating with downstream nodes, while downstream nodes simply execute the preemption decisions and provide status feedback. This reduces coordination overhead and prevents conflicts while maintaining routing flexibility.
Data Source
AI summary
In an embodiment, a method is disclosed for minimizing soft preemptions of LSPs. Upon receiving a reservation message for an LSP whose requested bandwidth that exceeds the available bandwidth of downstream links, a network node may select a set of LSPs for soft preemption and share the selection with other nodes along their paths, both upstream and downstream. By coordinating the selection of LSPs to soft-preempt among nodes on the path, fewer LSPs may require soft preemption, which may result in minimizing excessive network disruptions, and thus, allowing the network to function more efficiently.