Switch Load Value Determination for Network Downtime Scheduling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Computing networks face disruptions due to downtime events like firmware upgrades, which can be unpredictable and affect network traffic significantly, especially when considering varying traffic patterns across different times and days.
Innovation Solution
A system that determines switch load values at multiple time parameters by analyzing traffic volume, redundancy configurations, and loss potential, allowing for weighted scoring and scheduling of downtime events to minimize disruption, using a network appliance connected to switches via a control and data plane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If downtime events are performed for firmware upgrades, then network device maintenance and reliability are improved, but network traffic disruption increases
Solution Approach 1:
The system performs preliminary analysis of network traffic patterns, switch load values, and redundancy configurations before scheduling downtime events. By evaluating historical traffic data and identifying optimal time parameters in advance, the system schedules maintenance during periods of lower network utilization, thereby reducing traffic disruption while ensuring device reliability through planned firmware upgrades
2Duration of action of moving object
If downtime events are scheduled during high-traffic periods, then maintenance can be completed quickly, but network disruption and loss of time increase
Solution Approach 1:
The system dynamically evaluates switch load values across multiple time parameters and adjusts maintenance scheduling based on real-time network conditions. By continuously monitoring traffic patterns and adapting the scheduling decision to current network state, the system can identify windows where maintenance can be performed with minimal impact, balancing maintenance duration against network disruption
3Device complexity
If switch load values are not analyzed, then scheduling is simpler, but network disruption cannot be minimized
Solution Approach 1:
The system introduces an intermediary analysis layer that evaluates switch load values, traffic patterns, and redundancy configurations to inform scheduling decisions. This intermediary component processes network data and provides optimized scheduling recommendations, balancing the added analytical complexity against the significant reduction in network disruption achieved through data-driven scheduling
Data Source
AI summary
Various examples disclosed herein relate to determining switch load values for a switch according to a weighted score for categorized network traffic. In some examples, traffic volume information is determined for a switch in a network. The traffic volume information can include volume of network traffic for the switch for multiple time parameters. The network can include multiple switches. The switches can be associated with loss potential information based on a topology of the switches. The traffic volume information can be categorized into multiple categories. Multiple switch load values can be determined for the switch. Each switch load value can correspond to one of the multiple time parameters. Further, each switch load value can be determined according to a weighted score for each categorized network traffic and according to the loss potential information.