Cloud Service Auto-Recovery Framework With Scheduled Health Checks
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Solution Overview
Problem
Cloud environments experience network connectivity disruptions and hardware failures that affect the responsiveness of software applications and services, leading to potential service disruptions and poor performance.
Innovation Solution
A scheduling framework is implemented in a cloud environment to automatically execute recovery jobs for service components, utilizing health check operations to determine the need for recovery measures, with decoupled logic for health checks and recovery operations, allowing for flexible and efficient execution of recovery operations at the service component level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and recovery operations are used for service components, then operational control and flexibility are maintained, but service availability and responsiveness deteriorate due to delays in detecting and addressing issues
Solution Approach 1:
The system performs preliminary actions by scheduling recovery jobs in advance and continuously monitoring service components through health check operations. The scheduling framework pre-configures recovery routines and executes them proactively before service failures propagate, thereby improving availability without adding response delays.
2Ease of operation
If centralized recovery management is implemented, then coordination and control are improved, but system complexity and deployment difficulty increase
Solution Approach 1:
The system segments recovery management into independent, modular components: a scheduling framework that coordinates recovery jobs, health check operations that monitor service components, and recovery routines that execute corrective actions. This segmentation simplifies deployment and operation while maintaining centralized coordination through the scheduling framework.
3Adaptability or versatility
If tight coupling between health check logic and recovery operation logic is maintained, then system simplicity is preserved, but adaptability and flexibility for different recovery scenarios are reduced
Solution Approach 1:
The system extracts health check logic from recovery operation logic, allowing each to be developed, tested, and executed independently. The scheduling framework invokes health check operations separately from recovery routines, enabling flexible configuration of different health checks and recovery operations for various service components without increasing overall system complexity.
Data Source
AI summary
The present disclosure relates to computer-implemented methods, software, and systems for an automatic recovery job execution through a scheduling framework in a cloud environment. One or more recovery jobs are scheduled to be performed periodically for one or more registered service components included in a service instance running on a cluster node of a cloud platform. Each recovery job is associated with a corresponding service component of the service instance. A health check operation is invoked at a service component based on executing a recovery job at the scheduling framework corresponding to the service component. In response to determining that the service component needs a recovery measure based on a result from the health check operation, a recovery operation is invoked as part of executing a set of scheduled routines of the recovery job. Implemented logic for the recovery operation is stored and executed at the service component.