Automated Hybrid Cloud Upgrade Scheduling via Landscape Model Analysis
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Solution Overview
Problem
Managing and scheduling software upgrades in a hybrid cloud scenario can be complex and costly due to the need to maintain interface dependencies between customer-side and service provider-side systems, requiring coordinated upgrades across multiple systems.
Innovation Solution
A computer-implemented method that receives a landscape model describing changes to the customer landscape, automatically identifies systems to upgrade, and schedules upgrades for both customer-side and server-side systems, ensuring overlapping time slots for seamless communication and reducing operational costs through automated synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual scheduling of upgrades is performed to maintain interface dependencies, then upgrade coordination is achieved, but operation costs increase and scheduling complexity increases
Solution Approach 1:
The system automatically identifies systems requiring upgrades and schedules them by analyzing the landscape model and interface dependencies itself, without requiring external manual coordination. The processor autonomously determines upgrade sequences and time slots based on system relationships.
Solution Approach 2:
The system uses the landscape model as feedback to automatically adjust upgrade scheduling decisions. By continuously analyzing system dependencies and interface relationships, the processor optimizes upgrade timing to maintain compatibility while reducing manual intervention.
2Reliability
If multiple systems are upgraded simultaneously to maintain interface dependencies, then system compatibility is maintained, but downtime increases and operation costs increase
Solution Approach 1:
The upgrade process is segmented into individual system upgrades rather than simultaneous upgrades. The processor divides the upgrade task into discrete time slots for different systems, allowing incremental implementation while maintaining interface dependencies through automated coordination.
Solution Approach 2:
The system performs preliminary analysis of the landscape model to identify upgrade dependencies and schedule time slots before actual upgrades occur. This advance planning ensures compatibility is maintained while minimizing downtime by optimizing the sequence and timing of upgrades.
3Productivity
If automated upgrade identification is implemented, then operation costs are reduced, but scheduling accuracy must be maintained
Solution Approach 1:
Manual scheduling mechanisms are replaced with automated processor-based analysis of the landscape model. The system uses algorithmic processing to identify upgrade requirements and determine optimal time slots, maintaining precision through computational analysis rather than manual coordination.
Solution Approach 2:
The landscape model serves as an intermediary data structure that captures system relationships and dependencies. The processor analyzes this model to automatically determine upgrade scheduling, ensuring accuracy is maintained through structured data representation of system interfaces.
Data Source
AI summary
Techniques are described for automatically scheduling and performing upgrades in a hybrid cloud scenario. Advantages to these techniques include revision safe system upgrades and that the process is automated, thus reducing expensive operation costs. Techniques include receiving a landscape model describing changes to a customer landscape, identifying a customer-side system within a customer landscape to upgrade based on the landscape model, and identifying a server-side system within the server landscape that is associated with the customer-side system, and scheduling the customer-side system and the server-side system for upgrade.