Dynamic Resource Unit Sizing for Distributed Computing Bottlenecks
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Solution Overview
Problem
In distributed computing environments, users face challenges in accurately specifying resource units for tasks, leading to bottlenecks or resource wastage due to discrepancies between allocated and actual resource needs, which affects overall cluster efficiency.
Innovation Solution
Implementing a method to monitor resource usage dynamically adjust resource unit sizes based on actual usage information, allowing for real-time allocation and reclamation of resources to match task requirements, thereby optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually specify resource unit sizes for tasks, then resource allocation can be controlled, but resource wastage or bottlenecks occur due to discrepancies between allocated and actual resource needs
Solution Approach 1:
The system implements feedback mechanisms by monitoring actual resource usage of tasks and using this information to dynamically adjust resource unit sizes. The resource scheduler continuously collects usage data from running tasks and feeds this information back into the allocation decision-making process, enabling adaptive optimization that eliminates the need for manual specification while preventing resource wastage.
Solution Approach 2:
The patent transforms static resource allocation into a dynamic system where resource unit sizes are automatically adjusted based on actual task requirements. The resource scheduler modifies allocation parameters in real-time according to monitored usage patterns, allowing the system to adapt to changing conditions and eliminate the mismatch between allocated and needed resources.
2Device complexity
If fixed resource unit sizes are allocated to tasks, then allocation simplicity is maintained, but cluster efficiency decreases due to resource bottlenecks or underutilization
Solution Approach 1:
The system enables self-service by allowing tasks to effectively determine their own resource requirements through monitoring and feedback mechanisms. The resource scheduler automatically adjusts allocations based on actual usage without requiring user intervention or complex manual configuration, thus maintaining simplicity while improving efficiency through adaptive optimization.
Solution Approach 2:
The patent implements parameter changes by dynamically modifying resource unit size parameters based on actual task performance and usage data. The system adjusts allocation parameters such as CPU cores, memory size, and storage capacity according to monitored metrics, transforming fixed allocations into flexible, performance-optimized configurations.
3Loss of energy
If resource unit sizes are dynamically adjusted based on usage monitoring, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The resource scheduler acts as an intermediary between tasks and physical resources, absorbing the complexity of monitoring and adjustment operations. This intermediary component handles the sophisticated logic of usage tracking, analysis, and dynamic reallocation, thereby isolating the complexity from the overall system architecture and presenting a simplified interface to users and tasks.
Data Source
AI summary
Embodiments for optimizing resource usage in a distributed computing environment. Resource usage of each task in a set of running tasks associated with a job is monitored to collect resource usage information corresponding to each respective task. A resource unit size of at least one resource allocated to respective tasks in the set of running tasks is adjusted based on the resource usage information to improve overall resource usage in the distributed computing environment.