Self-tuning Resource Allocation for Distributed Storage Systems
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Solution Overview
Problem
Conventional distributed storage systems face issues with overprovisioning of resources and inability to dynamically adjust resource allocation based on actual usage, leading to suboptimal performance and inefficient resource utilization.
Innovation Solution
A self-tuning resource allocating system that identifies and groups resource objects based on utilization, assigns weights to these groups, and dynamically releases underutilized resources to allocate additional resources to critical groups, improving performance by redistributing resources based on real-time usage and workload analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are overprovisioned to ensure adequate capacity for all workloads, then system reliability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring workload characteristics and automatically adjusting resource provisioning levels. The system transitions from static overprovisioning to dynamic adaptation, allocating resources based on real-time demand patterns while maintaining reliability thresholds, thereby improving resource utilization efficiency without sacrificing system reliability
Solution Approach 2:
The system employs feedback mechanisms by monitoring workload characteristics, performance metrics, and resource utilization patterns. This feedback loop enables the system to detect when resources are underutilized and automatically reduce provisioning, or when reliability thresholds are approached and increase provisioning, resolving the contradiction between reliability and efficiency
2Device complexity
If static resource allocation is used to simplify system management, then device complexity is reduced, but adaptability to changing workload patterns deteriorates
Solution Approach 1:
The patent implements self-service automation where the system autonomously monitors workload patterns, analyzes performance data, and adjusts resource allocation without manual intervention. This self-managing capability provides adaptability to changing workload patterns while keeping operational complexity low, as the automation handles the complexity internally rather than requiring complex manual management procedures
Solution Approach 2:
The system performs preliminary analysis of workload characteristics and predicts future resource needs based on historical patterns. By proactively adjusting resource allocation before workload changes impact performance, the system achieves adaptability while maintaining simple management interfaces, as the complex analysis and adjustment processes occur automatically in advance
3Adaptability or versatility
If resources are allocated to all potential workloads to ensure availability, then adaptability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent applies local quality by allocating resources selectively based on specific workload characteristics and requirements. Rather than uniformly provisioning resources for all potential workloads, the system analyzes individual workload patterns and allocates resources locally to where they are most needed, improving resource utilization efficiency while maintaining adaptability to diverse workload types through targeted allocation
Data Source
AI summary
An apparatus comprises at least one processing device that includes a processor coupled to a memory. The processing device is configured to identify a plurality of resource objects associated with a processing device, to group correlated resource objects according to processing device utilization of the resource objects, to assign a first weight to a first resource object grouping, wherein the first weight is associated with a performance impact of the first resource object grouping on the processing device, and to release at least some of the first resource object grouping to provide additional resources to a second resource object grouping, the additional resources resulting from the releasing, wherein the first object grouping is selected for the releasing based on a comparison between the first weight and a second weight associated with the second resource object grouping, wherein the releasing is performed to improve performance of the processing device.