Cluster Deployment Orchestration With Dependency-Based Resource Allocation
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Solution Overview
Problem
Traditional manual deployment and resource allocation methods in complex computing environments, such as cloud platforms and distributed computer clusters, are time-consuming, prone to errors, and inefficient, leading to system performance issues and downtime.
Innovation Solution
A system and method for managing computerized resources that associate server instances with keys defining stages, middleware types, and operational capabilities, determine dependencies, and execute tasks within a computer cluster, using a centralized web-based interface for real-time monitoring and remote command execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual deployment and resource allocation methods are used, then operational control is maintained, but deployment time and operational complexity increase significantly
Solution Approach 1:
The system enables self-service deployment through automated dependency resolution and resource allocation. The deployment manager automatically identifies dependencies, selects appropriate server instances, and executes deployment tasks without requiring manual intervention for each step, thereby reducing deployment time while maintaining operational control through centralized management.
Solution Approach 2:
The system performs preliminary actions by pre-establishing dependency relationships between applications and resources, and pre-configuring server instances with required capabilities. This allows the deployment manager to make informed decisions during deployment without time-consuming real-time analysis, reducing overall deployment time while maintaining controlled operation.
2Adaptability or versatility
If manual resource allocation is used, then flexibility is maintained, but system efficiency and productivity decrease
Solution Approach 1:
The system changes parameters by dynamically adjusting resource allocation based on detected dependencies and server capabilities. The deployment manager modifies allocation decisions automatically according to application requirements and available resources, improving system efficiency while maintaining flexibility through configurable deployment policies and criteria.
Solution Approach 2:
The system implements feedback mechanisms where the deployment manager continuously monitors dependency relationships and server status, adjusting resource allocation in real-time. This feedback loop enables the system to maintain flexibility while improving productivity by optimizing resource distribution based on actual system state and deployment progress.
3Productivity
If automated deployment is implemented, then deployment speed increases, but system complexity and management overhead increase
Solution Approach 1:
The system segments complexity by dividing the deployment management function into distinct modular components: dependency detection module, resource selection module, deployment execution module, and monitoring module. Each component handles specific aspects of deployment, reducing overall system complexity while maintaining high deployment speed through automated coordination of these segmented functions.
Solution Approach 2:
The deployment manager acts as an intermediary layer between the deployment system and underlying infrastructure components. This intermediary abstraction simplifies the system architecture by providing a unified interface for dependency management and resource allocation, reducing complexity while enabling fast automated deployment through standardized communication protocols and interfaces.
4Loss of information
If comprehensive monitoring is implemented, then system visibility improves, but operational overhead and complexity increase
Solution Approach 1:
The monitoring system achieves multi-functionality by using a unified monitoring framework that tracks multiple aspects of system state (dependency status, resource allocation, deployment progress) through a single integrated mechanism. This universal approach improves system visibility across all components while reducing operational overhead by consolidating monitoring functions rather than requiring separate monitoring systems for each aspect.
Data Source
AI summary
A system and method for managing computerized resources, including: associating a first server instance with a plurality of keys defining: a stage within a software development lifecycle, application information, a middleware type, and an operational capability (where each of the plurality of keys refers to a corresponding database); determining application dependencies and/or database dependencies based on one or more of the plurality of keys; and executing computer tasks within a computer cluster based on the determined dependencies, where the computer cluster includes the first server instance associated with the keys. Some embodiments may provide a user interface (UI) to display real time information describing tasks running on the computer cluster. Computer tasks executed according to some embodiments may be included in a deployment process of a software application, where the deployment process includes a plurality of task orchestration tools.