Hierarchical Storage QoS Allocation for Noisy Neighbor Control
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Solution Overview
Problem
Existing storage systems in data centers face challenges in managing quality of service (QoS) standards across multiple tenants, leading to disruptive 'noisy neighbor' issues and inefficient resource utilization.
Innovation Solution
Implementing an orchestration system that allocates storage cluster resources hierarchically, schedules operations, and applies algorithms to ensure guaranteed and maximum availability of resources, dynamically moving storage units and rate limiting to optimize resource use across the storage cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple tenants share storage cluster resources, then resource utilization efficiency is improved, but service quality and reliability deteriorate due to noisy neighbor issues
Solution Approach 1:
The patent segments the storage cluster resources by implementing hierarchical QoS policies that divide resource allocation into multiple levels (cluster-level, storage pool-level, and storage unit-level). This segmentation allows different tenants to have guaranteed resource allocations while preventing any single tenant from monopolizing resources and affecting others' service quality.
Solution Approach 2:
The patent changes the parameter of resource allocation from static to dynamic by implementing algorithms that adjust resource distribution based on real-time conditions. The system monitors resource usage and automatically adjusts allocations to maintain service quality standards while optimizing overall resource utilization efficiency.
2Reliability
If QoS standards are enforced across storage cluster resources, then service level agreement compliance is improved, but resource allocation flexibility and efficiency worsen
Solution Approach 1:
The patent implements dynamic QoS enforcement where resource allocation parameters can adjust in real-time based on current system conditions and tenant needs. The hierarchical policy framework allows guaranteed minimum allocations to maintain SLA compliance while permitting excess resource usage when available, thus maintaining allocation efficiency.
Solution Approach 2:
The system employs self-adjusting algorithms that automatically monitor resource usage patterns and dynamically reallocate resources among tenants based on current QoS requirements and availability. This self-service mechanism maintains SLA compliance without requiring manual intervention, preserving resource allocation efficiency.
3Productivity
If storage units are dynamically moved to optimize resource distribution, then resource utilization is improved, but system complexity and operational difficulty increase
Solution Approach 1:
The patent implements preliminary action by pre-establishing hierarchical QoS policies and allocation rules before resource distribution occurs. The system pre-configures guaranteed allocations and maximum limits for each tenant, allowing dynamic movements to occur within predetermined boundaries, thus reducing the complexity of real-time decision-making.
Solution Approach 2:
The patent introduces an intermediary orchestration layer that manages the complexity of dynamic storage unit movements. This intermediary component handles the computational complexity of optimizing resource distribution while presenting simplified interfaces to users and maintaining QoS guarantees, thus improving resource utilization without proportionally increasing operational difficulty.
4Reliability
If hierarchical resource allocation is implemented, then QoS management predictability is improved, but allocation overhead and processing time increase
Solution Approach 1:
The patent segments the hierarchical allocation process into distinct levels (cluster-level aggregation, storage pool-level distribution, and storage unit-level assignment). This segmentation allows each level to operate independently with optimized processing, improving predictability while minimizing the time loss at each hierarchical level through specialized handling.
Data Source
AI summary
This disclosure describes a set of techniques that include establishing and managing quality of service standards across storage cluster resources in a data center. In one example, this disclosure describes a method that includes establishing a quality of service standard for a tenant sharing a storage resource with a plurality of tenants, wherein the storage resource is provided by the plurality of storage nodes in the storage cluster; allocating a volume of storage within the storage cluster, wherein allocating the volume of storage includes identifying a set of storage nodes to provide the storage resource for the volume of storage, and wherein the set of storage nodes are a subset of the plurality of storage nodes; and scheduling operations to be performed by the set of storage nodes for the volume of storage.