Storage Node Workload Management via QoS Proposals
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Solution Overview
Problem
Existing network storage systems face challenges in efficiently allocating resources for processing workloads, resulting in high latency for client devices in large storage environments.
Innovation Solution
A clustered network environment with node computing devices and data storage apparatuses that implement a method for managing workloads by generating and broadcasting quality of service proposals, determining bandwidth requirements, and adjusting local bandwidth configurations to balance traffic across nodes, using a storage operating system to manage communications and file systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If prior techniques are used to allocate resources for processing workloads in a networked storage environment, then resource allocation is performed, but latency to client devices becomes substantially high
Solution Approach 1:
The patent segments the storage system into multiple storage nodes that can independently process workloads. Each node maintains local state and can handle requests autonomously, reducing the need for cross-node communication and thereby reducing latency while maintaining overall system productivity through parallel processing capabilities.
Solution Approach 2:
The patent introduces a new dimension of workload management by implementing a state machine model at each storage node that tracks workload states locally. This local state tracking eliminates the need for centralized coordination for many operations, reducing latency while preserving system-wide productivity through distributed autonomous decision-making.
2Quantity of substance
If resources are allocated to process workloads across numerous storage nodes, then workload capacity increases, but latency to client devices increases
Solution Approach 1:
The patent implements local quality by enabling each storage node to maintain local workload state and make autonomous decisions about local resource allocation. This local intelligence reduces the need for remote coordination, allowing the system to scale resources across multiple nodes without proportionally increasing latency, as each node operates independently within its local context.
Data Source
AI summary
The present technology relates to managing workload within a storage system. A quality of service parameter proposal associated with managing incoming network traffic is generated and provided to a plurality of nodes. The generated quality of service parameter proposal to manage the incoming network traffic is modified based on a response received from the nodes. The incoming network traffic is serviced using the data from the modified quality of service parameter proposal.