Predictive Failover Planning for Cloud Data Centers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In cloud computing environments, managing system and application outages is challenging due to the large scale and complexity, making it difficult to ensure high availability and seamless failover of resources and applications across data centers.
Innovation Solution
A method is implemented to monitor resource usage across data centers, project a 'shadow load' of active applications, and develop a dynamic failover resource allocation scheme, allowing for seamless re-allocation of resources and applications in case of failures, with minimal disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual management of each computing resource and application is implemented, then failover control precision is improved, but system management complexity increases significantly
Solution Approach 1:
The patent segments the failover management into two distinct phases: atomic management of individual applications during failover events, and non-atomic bulk management of resource groups during normal operation. This allows precise control at the application level when needed while enabling simplified group-level management during routine operations, resolving the contradiction between precision and complexity.
Solution Approach 2:
The system dynamically adjusts its management granularity based on operational context. During normal operation, it manages resources at the group level for simplicity; during failover events, it switches to atomic application-level management for precision. This dynamic adaptation allows the system to achieve both low complexity in normal operation and high precision during critical events.
2Reliability
If dynamic adaptation to cloud resource changes is implemented, then system availability is improved, but planning and coordination complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-planning failover strategies and pre-positioning resource allocation plans before actual failures occur. The system continuously monitors cloud resource changes and updates failover plans in advance, so when failures happen, the coordination complexity is already resolved and execution can proceed smoothly, improving availability without burdening operational complexity.
Solution Approach 2:
The system employs feedback mechanisms that continuously monitor cloud resource allocations, hardware changes, and application states, then adjust failover plans accordingly. This closed-loop approach ensures the system adapts to dynamic changes while maintaining manageable complexity through automated feedback-driven updates rather than manual re-planning.
3Measurement precision
If atomic management of all applications is enforced, then failover precision is improved, but operational efficiency decreases
Solution Approach 1:
The patent segments failover management by application criticality and dependency. Not all applications require atomic management - the system identifies which applications need precise atomic failover based on their criticality and inter-dependencies, applying atomic management only where necessary while using bulk management for less critical applications, thus maintaining precision where needed without sacrificing overall operational efficiency.
Solution Approach 2:
The system applies partial atomic management rather than universal atomic management. It selectively enforces atomic failover only for critical applications and resource groups, while allowing non-atomic bulk operations for less critical components. This partial application of atomic management maintains failover precision for essential services while improving operational efficiency by avoiding unnecessary atomic constraints across the entire system.
Data Source
AI summary
Variations discussed herein pertain to identifying a resource usage of applications in a first data center; and for the applications in the first data center, writing those usages to a database. Variations also pertain to identifying a resource usage of applications in a second data center; reading the first data center loads from the database; determining, from the read loads, which applications in the first data center will fail over to the second data center should the first data center fail. For those applications, computing a shadow load that represents predicted computing resource requirements of those applications in the second data center based read loads; and developing a failover resource allocation scheme from the shadow load and a current local resource load of the second data center such that the second data center can take on the resource usage load of those applications if the first data center goes offline.