Scalability Orchestrators for Distributed Service Resource Coordination
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Solution Overview
Problem
In distributed applications with service-oriented architecture, managing resource provisioning and scaling across multiple, independently managed auxiliary services is complex, especially when handling increased workloads, as existing systems lack efficient coordination mechanisms for resource acquisition and release across constituent services.
Innovation Solution
Implementing scalability orchestrators that coordinate scale-out and scale-in operations across constituent services by analyzing workload metrics and adjusting throttling limits, allowing for asynchronous and customizable resource management to ensure optimal performance and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If resource provisioning is managed independently at each constituent service, then each service can be administered autonomously, but coordination across services becomes complex and inefficient
Solution Approach 1:
The patent introduces a central provisioning system that acts as an intermediary between multiple constituent services and resource pools. This mediator coordinates resource allocation across services, managing the complexity centrally while allowing individual services to maintain their autonomy. The provisioning system translates service-level resource requests into coordinated resource allocation actions.
2Productivity
If scaling operations are coordinated across all constituent services, then resource utilization optimizes, but the provisioning process becomes slower and less responsive
Solution Approach 1:
The patent segments the provisioning process into independent service-level operations that can execute asynchronously. Each constituent service's resource provisioning is handled as a separate, parallel task rather than a sequential coordinated process. This allows resource allocation to proceed at multiple service levels simultaneously, improving overall provisioning speed while maintaining coordination through the central system's oversight.
Solution Approach 2:
The system performs preliminary resource allocation decisions at the provisioning system level before services execute their specific resource acquisition. This preliminary action pre-coordinates the scaling operations, allowing services to then proceed with their resource acquisition independently and asynchronously, thereby optimizing resource utilization without the full coordination overhead during execution.
3Reliability
If throttling limits are adjusted dynamically based on workload, then performance levels improve, but system control becomes more complex
Solution Approach 1:
The patent implements feedback mechanisms where the provisioning system continuously monitors workload metrics from constituent services and automatically adjusts throttling limits based on observed performance. This closed-loop control enables dynamic adaptation to changing conditions, improving service performance levels while the automated feedback-driven approach manages control complexity by replacing manual tuning with systematic monitoring and adjustment.
Data Source
AI summary
Based on analysis of a workload associated with a throttling key of a client request directed to a first service, a scale-out requirement of the throttling key is obtained at respective resource managers of a plurality of other services which are utilized by the first service to respond to client requests. The resource managers initiate, asynchronously with respect to one another, resource provisioning tasks at each of the other services to fulfill the scale-out requirement. A throttling limit associated with the throttling key is updated to a second throttling key after the resource provisioning tasks are completed by the resource managers, and the updated limit is used to determine whether to accept another client request associated with the throttling key.