Endpoint Controller Resource Buffer Management in Distributed Computing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As computing environments become increasingly complex, managing resource allocation for applications across multiple devices and domains becomes challenging, especially during load spikes, which can lead to inefficiencies and performance issues.
Innovation Solution
A method for managing a distributed multi-tiered computing (DMC) environment involves an endpoint controller obtaining an initial resource buffer, maintaining it during task provisioning, and updating it based on device metrics to ensure efficient resource allocation and handle load spikes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If devices are allocated to execute applications, then application performance is improved, but the ability to handle load spikes deteriorates
Solution Approach 1:
The system performs preliminary actions by allocating a resource buffer to devices before load spikes occur. The endpoint controller obtains an initial resource buffer from the local controller and maintains it during task provisioning, ensuring resources are ready in advance to handle future load increases without compromising application performance during peak periods.
Solution Approach 2:
The system implements dynamic resource buffer adjustment by monitoring device metrics and updating the resource buffer based on change events. The endpoint controller dynamically modifies the resource buffer allocation in response to observed device performance metrics, enabling the system to adapt resource allocation to varying load conditions while maintaining optimal application performance.
2Productivity
If resource buffer is maintained during task provision, then resource allocation efficiency is improved, but device complexity increases
Solution Approach 1:
The system segments resource buffer management into distinct functional components: the local controller allocates the initial resource buffer, the endpoint controller maintains and monitors it during task provisioning, and the endpoint controller updates it based on device metrics. This segmentation distributes complexity across multiple controllers rather than concentrating it in a single device.
Solution Approach 2:
The system implements feedback mechanisms where the endpoint controller obtains device metrics based on task performance and uses this feedback to determine when resource buffer changes are needed. This feedback-driven approach automates complex decision-making, reducing the burden on the endpoint controller while maintaining efficient resource allocation through data-driven adjustments.
Data Source
AI summary
Techniques described herein relate to a method for managing a distributed multi-tiered computing (DMC) environment. The method includes obtaining, by an endpoint controller associated with a device, an initial resource buffer from a local controller; in response to obtaining the initial resource buffer: maintaining the initial resource buffer during task provision for the device; obtaining device metrics based on performance of tasks on the device; making a determination that a resource buffer change event is identified; and in response to the determination: updating the initial resource buffer based on the resource buffer change event.