Redistribution Agent for Distributed Computing Failure Recovery
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Solution Overview
Problem
In distributed computing systems, failures in processing environments lead to disrupted resource management operations, causing outages and user dissatisfaction, as existing systems rely heavily on administrative intervention for remediation.
Innovation Solution
Implementing fine-grained tracking of distributed resource management tasks and applying failure processing rules to automatically redistribute tasks and invoke remediation actions, reducing the need for administrative intervention by using redistribution agents and failure detection mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system relies on administrative intervention for remediation, then the system can handle complex failure scenarios, but the system experiences outages and user dissatisfaction due to manual intervention requirements
Solution Approach 1:
The system implements self-service through automated failure detection and remediation. The redistribution agent continuously monitors processing environments, detects failures, and automatically redistributes tasks without requiring administrative intervention. This enables the system to self-heal and maintain availability autonomously.
Solution Approach 2:
The system employs feedback mechanisms where the redistribution agent monitors the status of processing environments and tasks, detects failures, and triggers remediation actions based on the detected conditions. This closed-loop feedback enables automated response to failures, improving system reliability without manual intervention.
2Productivity
If the system implements fine-grained tracking of resource management tasks, then the system achieves efficient task redistribution and self-remediation, but the system complexity increases due to tracking and monitoring mechanisms
Solution Approach 1:
The redistribution agent serves multiple functions: it tracks resource management tasks, monitors processing environment status, detects failures, and executes remediation actions. By consolidating these functions into a single component, the system achieves fine-grained tracking capability without proportionally increasing overall system complexity.
Solution Approach 2:
The system segments resource management operations into discrete trackable tasks with associated attributes. This segmentation enables fine-grained tracking of individual tasks while maintaining overall system manageability. Each task can be independently monitored and redistributed if needed.
3Loss of time
If the system uses automated failure detection and redistribution, then the system minimizes downtime and enables self-remediation, but the system may redistribute tasks to failed processing environments if tracking precision is insufficient
Solution Approach 1:
The system performs preliminary tracking and monitoring of processing environment status before failures occur. By continuously collecting and analyzing status information in advance, the system can detect failures promptly and accurately, enabling timely redistribution to healthy environments and minimizing downtime.
Solution Approach 2:
The system replaces manual administrative monitoring with automated electronic detection mechanisms. The redistribution agent uses programmatic monitoring and analysis of processing environment status, substituting human-based detection with automated systems that provide continuous, precise failure detection and rapid response.
Data Source
AI summary
Computing cluster system management. Embodiments implement fine-grained rule-based approaches to error recovery. A service dispatches tasks to components of the computing cluster. At the time of task dispatching, entries are made into a write-ahead log. The write-ahead log entries serve for recording task and component attributes. A monitor detects a failure event raised by one or more of the components of the computing cluster. Responses to the failure event include determining a set of conditions that are present in the computing cluster at the time of the detection, and then using the failure event and the determined conditions in combination with a set of fine-grained failure processing rules to determine one or more recovery actions to take. Recovery actions include redistributing the failed task to a different node or to different service. Certain conditions and rules initiate actions that rollback the state of a component to a previous success point.