Cloud Manager Agent for Dynamic Deployment Configuration
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Solution Overview
Problem
Cloud deployment configurations for computing applications often become suboptimal due to fluctuations in workload and component failures, requiring continuous adjustments to ensure scalability and reliability, which existing technologies fail to address effectively in dynamic environments.
Innovation Solution
A system and method that employs a cloud manager agent to collect and analyze metrics from virtual machines, using a rules engine to automatically adjust resource allocation and deployment configurations in real-time, enabling dynamic scaling and failure recovery by separating application and infrastructure logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud deployment configuration is manually managed, then implementation simplicity is maintained, but adaptability to dynamic workload changes deteriorates
Solution Approach 1:
The system implements self-service through automated rule-based configuration management. The cloud manager agent continuously monitors workload metrics and automatically adjusts deployment configurations based on predefined rules, eliminating the need for manual intervention while maintaining adaptability to dynamic conditions.
Solution Approach 2:
The system applies dynamics by enabling real-time adaptation of cloud deployment configurations. The rule engine processes current workload metrics and dynamically modifies resource allocation, scaling policies, and deployment parameters to match changing conditions, transforming static configuration management into a dynamic responsive system.
2Productivity
If automated rule-based management is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments configuration management into modular components: metric collection by the cloud manager agent, rule evaluation by the rule engine, and configuration application by the deployment manager. This segmentation improves productivity through specialized automated functions while managing complexity through clear separation of concerns and independent可维护 components.
Solution Approach 2:
The rule engine serves multiple functions by evaluating various workload metrics, applying different scaling rules, and managing diverse configuration parameters across multiple cloud services. This multi-functionality improves productivity by consolidating automation logic into a single universal system rather than requiring separate automation mechanisms for each configuration aspect.
3Reliability
If real-time metric collection is performed, then reliability is improved, but use of energy increases
Solution Approach 1:
The system maintains continuous metric collection and rule evaluation to ensure reliable deployment configuration management. The cloud manager agent continuously monitors workload metrics and the rule engine continuously evaluates conditions, providing uninterrupted adaptive control that maintains system reliability through persistent monitoring and adjustment.
Solution Approach 2:
The system implements selective metric collection focusing on the most critical workload parameters relevant to deployment configuration decisions. Rather than collecting all possible metrics, the system gathers only the essential data needed for rule evaluation, reducing energy consumption while maintaining sufficient reliability for effective configuration management.
Data Source
AI summary
A system is provided to manage cloud deployment configuration of a computing application. The system comprises a request detector, a retrieving module, a manager loader, a configuration change request detector, and a configuration module. The request detector may be configured to detect a request to install a manager agent on an instance of a virtual machine executing a computing application within a virtualization service. The retrieving module may be configured to obtain a manager agent object for loading the manager agent, and install the manager agent on the instance. The manager loader may be configured to invoke the manager agent to collect metrics for the computing application. The configuration change request detector may be configured to receive an instruction to alter cloud deployment configuration of the computing application. The configuration module may be configured to automatically alter the cloud deployment configuration of the computing application in response to the instruction.