Automated Master Election for Cloud Resource Managers
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Solution Overview
Problem
Existing cloud computing systems require significant administrative overhead to achieve high availability and scalability, relying on human intervention for hardware management and error-prone processes.
Innovation Solution
A method where servers autonomously manage resources by identifying capacity, registering to manage resources, and updating trackers, allowing for dynamic assignment as master or backup managers, reducing the need for system administrator intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If system administrators manually manage hardware and configurations to achieve high availability and scalability, then system reliability and adaptability are improved, but device complexity and loss of time increase due to human intervention and error-prone processes
Solution Approach 1:
The patent implements self-service through automated master election among resource managers. When a master resource manager fails, backup resource managers automatically detect the failure and elect a new master through a standardized protocol, eliminating the need for system administrators to manually intervene for failover operations. This automated self-healing mechanism maintains high availability while reducing administrative complexity.
Solution Approach 2:
The system employs dynamic resource manager roles where resource managers can transition between master and backup states based on operational conditions. The master resource manager is not static but can be dynamically elected and re-elected based on system state, allowing the system to adapt to changing conditions without administrative intervention and maintain reliability with reduced operational complexity.
2Adaptability or versatility
If system administrators upgrade hardware and update configurations to achieve scalability, then adaptability is improved, but loss of time and device complexity increase due to manual intervention requirements
Solution Approach 1:
The system enables self-service scalability through automated resource manager registration and master election. When new resource managers are added to the system, they automatically register themselves and participate in the master election process through standardized protocols. This eliminates the need for system administrators to manually configure and integrate new resources, reducing the time and complexity associated with scaling the system while maintaining full adaptability.
Solution Approach 2:
The patent implements preliminary action by pre-defining standardized protocols for resource manager initialization, registration, and master election. These protocols are established in advance and automatically executed when new resource managers join the system. This preliminary framework enables seamless integration of new resources without requiring administrative configuration, thereby improving scalability while minimizing the time and complexity of deployment.
3Reliability
If manual hardware management is used to ensure system reliability, then reliability is improved, but ease of operation deteriorates due to the need for physical onsite intervention
Solution Approach 1:
The system implements self-service monitoring and failover detection where backup resource managers continuously monitor the health of the master resource manager and automatically detect failures. When a failure is detected, the backup managers automatically initiate the master election process through standardized protocols. This eliminates the need for system administrators to be physically present to detect or respond to failures, maintaining reliability while dramatically improving ease of operation.
Solution Approach 2:
The system employs feedback mechanisms where resource managers exchange standardized status information and health indicators through defined protocols. This continuous feedback loop enables automatic detection of master resource manager failures and triggers automated failover procedures. The feedback mechanism maintains system reliability through continuous monitoring while improving ease of operation by eliminating the need for manual intervention in failure scenarios.
Data Source
AI summary
The invention provides a technique for assigning a server to manage resources. The server identifies a capacity value that represents a maximum number of resources that the server can simultaneously manage, and parses data included in a tracker directory to identify the resources that have the fewest number of servers assigned to manage them. Next, based on the capacity value, the server registers to manage the identified resources, where each registration to manage an identified resource results in the server actively managing the resource when no other server is registered to manage the resource, or acting as a backup manager to the resource when at least one other server is registered to manage the resource, and the tracker directory is updated accordingly. Finally, the server, for each registration that results in actively managing a resource, configures itself to process client device requests that are directed to utilizing the resource.