Modular Distributed System Manager for Seamless Updates
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Solution Overview
Problem
Managing distributed systems, such as computer clusters, is challenging due to the difficulty in updating these systems while maintaining service availability, as existing methods often disrupt service and lack efficient workflows for coordinating updates across multiple devices.
Innovation Solution
A distributed system manager with a plug-in architecture that uses an orchestrator to identify updates, select devices, and manage workloads, allowing for seamless updates by preparing devices, relocating workloads, and reconfiguring systems to maintain service availability through standardized commands like scan, download, and install, with plugins adapting to specific software and system types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional update methods are used on distributed systems, then updates can be performed, but service availability is disrupted and downtime occurs
Solution Approach 1:
The distributed system is segmented into multiple independent devices or nodes, allowing updates to be applied incrementally to individual devices rather than requiring system-wide updates. This enables continuous operation of other devices while one device is being updated, maintaining overall service availability.
Solution Approach 2:
The system performs preliminary actions by identifying and preparing update candidates before actual updates are applied. Devices are selected for updates based on workload analysis and system state, and updates are staged and prepared in advance, allowing for coordinated rollout that minimizes disruption to service availability.
2Productivity
If manual update management is used across multiple devices, then updates can be applied, but the complexity of coordinating updates and managing workloads increases significantly
Solution Approach 1:
The distributed system implements self-service mechanisms where each device autonomously evaluates its own update readiness, reports its state to the orchestrator, and executes updates based on received instructions. This automated self-management reduces the need for complex manual coordination while improving update efficiency across the distributed system.
Solution Approach 2:
The system incorporates feedback loops where devices continuously report their operational status, workload levels, and update completion states back to the orchestrator. This feedback enables dynamic adjustment of update scheduling and workload distribution, simplifying management complexity while maintaining high update efficiency through automated decision-making.
3Adaptability or versatility
If a rigid update architecture is used, then the system structure is simple, but the system cannot adapt to different software types and system configurations
Solution Approach 1:
The orchestrator is designed with universal, standardized interfaces and protocols that enable it to manage diverse device types, software configurations, and update scenarios through a common framework. This universal architecture allows the system to adapt to different software types and system configurations without requiring device-specific management logic, thereby improving adaptability while controlling complexity through standardization.
Data Source
AI summary
A distributed system manager may identify updates to perform, identify devices to update, and perform the updates while maintaining service availability. The manager may consist of an orchestrator that uses a plug in architecture to interface with different update systems. The plugins may interface with an update system and respond to scan, download, and install commands from the orchestrator. The orchestrator may prepare each device within the distributed system for updating, then cause the updates to be performed via the plugins. In some embodiments, the logic for selecting devices to update and managing the workloads on the devices may be contained in a second type of plugin.