Network Upgrade Scheduling Using Redundancy and Service Dependencies
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Solution Overview
Problem
Existing network device management systems lack an efficient method for scheduling software upgrades that considers device and service redundancy, traffic loss minimization, and user-defined constraints, leading to potential network disruptions and suboptimal upgrade processes.
Innovation Solution
A controller device generates a multi-objective upgrade graph to represent device relationships and computes an optimization algorithm that schedules upgrades to minimize traffic loss and adhere to user-defined constraints, using enhanced device and service models to ensure concurrent upgrades of non-redundant devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network devices are upgraded simultaneously to minimize upgrade time, then productivity is improved, but reliability deteriorates due to potential network disruptions and traffic loss
Solution Approach 1:
The patent segments network devices into multiple groups based on their redundancy relationships and service dependencies. The controller divides devices such that upgrading one group does not disrupt services provided by other groups, enabling parallel upgrades while maintaining network availability through strategic grouping.
Solution Approach 2:
The patent performs preliminary analysis by determining redundancy relationships and service dependencies among network devices before scheduling upgrades. The controller pre-calculates upgrade schedules that account for potential service disruptions, allowing proactive avoidance of reliability issues rather than reacting to them during upgrades.
2Reliability
If manual scheduling of device upgrades is performed, then reliability can be maintained, but device complexity and operational burden increase
Solution Approach 1:
The patent implements self-service by enabling the controller to automatically determine redundancy relationships, compute service dependencies, and generate optimized upgrade schedules without manual intervention. The system autonomously analyzes network topology and service mappings to produce reliability-preserving upgrade sequences.
Solution Approach 2:
The patent incorporates feedback mechanisms where the controller continuously monitors service status and upgrade progress, adjusting schedules in real-time to maintain service availability. The system uses feedback about actual service behavior to refine future upgrade scheduling decisions.
3Reliability
If upgrade schedules are optimized considering multiple objectives, then reliability is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex optimization problem into manageable components by first determining redundancy relationships and service dependencies, then using these pre-computed relationships to guide the scheduling optimization. This segmentation reduces the computational burden of considering all possible upgrade sequences simultaneously.
Solution Approach 2:
The patent performs preliminary computation of redundancy relationships and service dependency graphs before the actual optimization scheduling. By pre-establishing these structural relationships, the system reduces the complexity of subsequent optimization algorithms that only need to respect pre-computed constraints rather than analyzing all possibilities from scratch.
Data Source
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AI summary
A controller device manages a plurality of network devices. The controller device includes one or more processing units configured to receive an upgrade request; determine an upgrade graph having nodes each representing one of the network devices or a network service provided by the network, and also having one or more edges each connecting two of the nodes and representing a network redundancy or service dependency; compute and assign, an edge weight to each edge by combining results of at least one objective function, each of the at least one objective functions having a minimum objective or maximum objective for the network; partition the upgrade graph into a plurality of sub-groups based on the edge weights; determine an upgrade schedule; and upgrade software of each of the plurality of network devices according to the upgrade schedule.