Probabilistic Device Upgrade Service for Tenant Networks
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Solution Overview
Problem
The manual and arbitrary selection of devices for software updates in a tenant with a large number of devices poses risks, as it may lead to failures in a larger number of devices during upgrades.
Innovation Solution
An upgrade service that automatically performs device upgrades in a probabilistic manner, using a Poisson binomial distribution to determine the probability of maintaining a minimum number of healthy devices, and iteratively selecting devices for upgrade based on success probabilities and risk parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual arbitrary selection of devices for upgrade is used, then IT administrator control is maintained, but the risk of widespread device failures increases
Solution Approach 1:
The system performs automatic device selection for upgrades without requiring manual IT administrator intervention. The upgrade service autonomously identifies and selects devices based on probabilistic models and risk parameters, eliminating manual arbitrary selection while maintaining system control through automated decision-making algorithms
Solution Approach 2:
The manual mechanical process of device selection is replaced with an automated computational system using Poisson binomial distribution and probabilistic modeling. The system substitutes human judgment with algorithmic decision-making that calculates success probabilities and risk parameters to objectively select devices for upgrade
2Productivity
If all devices are upgraded at once, then upgrade completion speed is maximized, but the risk of system-wide failures increases
Solution Approach 1:
The device upgrade process is divided into segmented batches rather than upgrading all devices simultaneously. The system segments the device population into groups based on risk parameters and success probabilities, upgrading devices in controlled increments to limit potential failure propagation while maintaining overall upgrade progress
Solution Approach 2:
The system performs partial upgrades by selecting only a subset of devices for each upgrade cycle rather than upgrading all devices at once. This partial action approach allows the system to make progress toward complete upgrade while controlling risk exposure by limiting the number of devices exposed to potential failures in each iteration
3Reliability
If probabilistic device selection is used, then the risk of widespread failures is reduced, but the upgrade process complexity increases
Solution Approach 1:
The system manages complexity by dynamically adjusting risk parameters and success probability thresholds based on device characteristics and upgrade history. By changing parameters such as confidence levels and risk tolerance values, the system can control the degree of probabilistic selection without fundamentally altering the underlying algorithmic structure
Solution Approach 2:
The upgrade service acts as an intermediary layer between the upgrade management system and individual devices. This intermediary handles the complex probabilistic calculations and device selection logic, shielding the overall system from complexity while enabling sophisticated risk-based decision-making through standardized interfaces and abstraction layers
4Loss of time
If manual ring-based upgrade is used, then device selection is simple, but the process time and administrative effort increase
Solution Approach 1:
The system performs automatic device selection for upgrades without requiring manual IT administrator intervention. The upgrade service autonomously identifies and selects devices based on probabilistic models and risk parameters, eliminating manual arbitrary selection while maintaining system control through automated decision-making algorithms
Solution Approach 2:
The manual mechanical process of device selection is replaced with an automated computational system using Poisson binomial distribution and probabilistic modeling. The system substitutes human judgment with algorithmic decision-making that calculates success probabilities and risk parameters to objectively select devices for upgrade
Data Source
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AI summary
In one example of the technology, device information associated with a device upgrade and a plurality of devices includes risk parameters including values associated with a minimum health value that is associated with a minimum acceptable number of healthy devices among the plurality of devices and a confidence value associated with a minimum acceptable probability that the number of healthy devices among the plurality of devices is at least as great as the minimum health value; and, for each device a success probability value that is associated with a probability that the device will be healthy after the device upgrade is performed on the device. A Poisson binomial distribution is iteratively used to determine a set of devices among the plurality of device for which the largest possible number of devices are included in the set of devices while meeting the risk parameters. The set of devices is then upgraded.