Virtualization System Upgrade Scheduling via Session Data

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Solution Overview

Problem

Virtual desktop systems face challenges in minimizing downtime and user disruption during upgrades, as existing methods often schedule upgrades at inconvenient times, leading to user dissatisfaction and potential service disruptions.

Innovation Solution

The system monitors session launch data to determine optimal upgrade times when user activity is minimal, allowing for scheduled downtimes that minimize service disruption and user impact, by assigning users to servers based on geographic location to reduce access latency and optimize resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If upgrades are performed on the virtualization system, then system reliability and performance are improved, but user accessibility and service continuity deteriorate due to downtime

Engineering Contradiction:

Improvesystem reliabilityVSAvoiduser accessibility

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system performs preliminary analysis of session launch data patterns to predict optimal upgrade windows before scheduling upgrades. By analyzing historical user behavior data in advance, the system identifies time periods with minimal user activity and schedules upgrades during these predicted low-usage windows, thereby maintaining reliability while minimizing disruption to user accessibility.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If upgrades are scheduled during low-usage periods, then user impact is minimized, but upgrade timing flexibility is reduced

Engineering Contradiction:

Improveuser impactVSAvoidupgrade timing flexibility

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system dynamically adjusts upgrade scheduling based on real-time and historical session launch data. Rather than using fixed scheduling rules, the system continuously analyzes user behavior patterns and adapts upgrade windows to match actual usage trends. This dynamic approach allows the system to minimize user impact while maintaining flexibility to schedule upgrades at optimal times based on current data patterns.

Inventive Principle:

Principle #15Dynamics

3Loss of time

If session launch data is monitored and analyzed, then optimal upgrade timing is determined, but system complexity and data processing requirements increase

Engineering Contradiction:

Improvedowntime windowVSAvoidsystem complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The system implements self-service mechanisms by automatically collecting, analyzing, and acting upon session launch data without requiring external intervention. The system autonomously identifies optimal upgrade windows by processing its own operational data, thereby reducing downtime windows while managing complexity through automated decision-making algorithms that learn from historical patterns.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS10721130B2Upgrade/downtime scheduling using end user session launch data

Publication Date: 2020.07.21 CITRIX SYSTEMS INC

AI summary

Methods, systems, computer-readable media, and apparatuses for updating a multi-tenant virtualization system are described herein. Session launch data for a plurality of end users associated with a plurality of tenants is obtained from a session database, and queried. The session launch data is analyzed for session launch activity. An update time is obtained based on the analysis. A component of the multi-tenant virtualization system is updated at the determined update time. During the updating, new sessions by the plurality of end users associated with the plurality of tenants are prevented from launching.