Data Coherency Engine Adaptive Scheduling for Network Congestion
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Solution Overview
Problem
Existing data update mechanisms in communications networks frequently fail to maintain data coherency due to unreachable devices during maintenance, failures, or network congestion, leading to stale data, and require costly manual intervention to recover.
Innovation Solution
Implementing a data coherency engine that schedules data requests based on age criteria, dynamically reschedules requests to avoid blackout periods, and adaptsively retries requests until data freshness is maintained, with notifications for failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If management entities use fixed schedules to request updated information from devices, then data updates are requested at regular intervals, but data coherency is lost when devices are unreachable or during network congestion
Solution Approach 1:
The system dynamically adjusts the scheduling of data requests based on real-time conditions. When devices are unreachable or network congestion is detected, the system modifies the request timing and frequency automatically, transitioning from a static fixed schedule to a dynamic adaptive schedule that responds to network conditions and device availability.
Solution Approach 2:
The system implements feedback mechanisms where management entities monitor the results of data requests and use this information to adjust future scheduling decisions. Feedback from failed requests, timeout conditions, and network congestion alerts triggers rescheduling actions that improve both reliability and productivity.
2Ease of operation
If management entities request updated information at fixed times, then scheduling is simple, but manual intervention is required to recover from failures and avoid network congestion
Solution Approach 1:
The system performs self-service by automatically detecting and recovering from request failures without requiring manual intervention. When failures are detected, the system autonomously reschedules requests, adjusts timing parameters, and attempts recovery operations, eliminating the need for administrator involvement in routine failure recovery.
Solution Approach 2:
The system takes preliminary actions by proactively adjusting schedules before failures occur. It anticipates potential issues by monitoring network conditions and device availability, and pre-adjusts request timing to avoid known problem periods, thereby preventing failures before they impact data coherency.
3Productivity
If management entities schedule many update requests at common times, then update frequency is high, but network congestion causes requests to be lost or timed out
Solution Approach 1:
The system segments the batch of update requests into smaller, distributed portions and schedules them at different times. Instead of sending all requests at once, it divides the workload and spreads requests across multiple time slots, reducing the impact of any single request on network congestion and improving overall delivery success.
Solution Approach 2:
The system uses periodic action by implementing retry mechanisms with exponential backoff. When requests fail due to congestion, the system waits for a progressively longer period before attempting again, effectively spacing out periodic retry actions to avoid overwhelming the network and improve delivery reliability.
4Device complexity
If the system waits until a scheduled request fails before detecting the issue, then simple error detection is used, but data becomes stale before recovery can occur
Solution Approach 1:
The system performs preliminary monitoring and detection of request failures before they manifest as data staleness. By continuously tracking request status and detecting failures early, the system has sufficient time to reschedule requests and recover data coherency before the data becomes stale, thereby reducing the loss of time.
Data Source
AI summary
In one embodiment, a data coherency system access an age criterion associated with a data set and determines a previous refresh time associated with the data set. The data coherency defines a next refresh time that satisfies the age criterion relative to the previous refresh time and schedules a request for the data set at the next refresh time.