Dynamic Storage Port Allocation for Data Center I/O Bottlenecks
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Solution Overview
Problem
Data centers face inefficiencies due to over-provisioning of bandwidth, leading to excess costs, as peak demand is infrequent and conventional static network configurations fail to leverage dynamic bandwidth allocation effectively.
Innovation Solution
A computer-implemented method that dynamically adjusts storage port allocations based on anticipated demand and real-time workload evaluations, using statistical models to predict and manage future demand, prioritize traffic, and rebalance workloads across storage ports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bandwidth is provisioned to meet projected peak data demand, then data center can handle peak demand, but excess costs are incurred due to over-provisioning for significant time
Solution Approach 1:
The patent implements dynamic bandwidth allocation that automatically adjusts storage port allocations based on real-time workload conditions and anticipated demand. This replaces static over-provisioning with adaptive resource allocation, allowing the system to scale bandwidth usage according to actual needs while maintaining peak demand capability.
Solution Approach 2:
The system uses statistical models to predict upcoming demand patterns and proactively adjusts storage port allocations before peak demand occurs. This preliminary action allows the data center to prepare adequate bandwidth capacity in advance of actual usage spikes, avoiding both over-provisioning costs and insufficient capacity.
2Loss of energy
If less bandwidth is provisioned to lower costs, then excess costs from over-provisioning are reduced, but expensive overcharges occur when peak demand exceeds provisioned bandwidth
Solution Approach 1:
The patent implements continuous monitoring of workload conditions and demand patterns with feedback loops that adjust bandwidth allocation in real-time. This feedback mechanism ensures the system automatically scales up provisioned bandwidth when approaching peak demand thresholds, preventing overcharge penalties while minimizing unnecessary over-provisioning costs during low-utilization periods.
Solution Approach 2:
The system dynamically changes the bandwidth allocation parameter based on workload intensity and predicted demand. By adjusting this key parameter responsive to actual conditions rather than maintaining a fixed conservative allocation, the data center optimizes the trade-off between provisioning costs and peak demand handling capability.
3Device complexity
If static network configuration is used, then system simplicity is maintained, but bottlenecking occurs that prevents leveraging full potential of greater bandwidth allocation
Solution Approach 1:
The patent implements self-service automation where the storage system automatically monitors its own workload conditions, predicts demand patterns, and adjusts its own bandwidth allocations without external intervention. This self-managing approach maintains operational simplicity while enabling dynamic optimization of bandwidth utilization to eliminate bottlenecks.
Solution Approach 2:
The system introduces an intermediary intelligent controller that sits between the static network infrastructure and the storage workloads. This intermediary layer translates static network capacity into dynamically optimized resource allocation, reconciling the simplicity of static configuration with the efficiency needs of variable workloads.
Data Source
AI summary
Dynamic quality of service for storage I/O port allocation is provided by obtaining indications of anticipated demand on a plurality of storage ports of a storage device of a data center, the anticipated demand being at least in part for storage input/output supporting transfer of data to or from a network, and dynamically adjusting storage port allocations based on the indications of anticipated demand and on real-time evaluation of workloads being serviced by the plurality of storage ports.