Dynamic I/O Throttling for Multi-Tenant Cloud Storage
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Solution Overview
Problem
Cloud computing environments face performance issues due to unpredictable and fluctuating workloads from multiple tenants, leading to latency and resource monopolization, which can be exacerbated by I/O contention and network bandwidth bottlenecks, making it challenging to manage resources effectively.
Innovation Solution
A throttling module within a cloud platform dynamically manages requests by comparing usage metrics and priorities for tenants, delaying excessive I/O requests to ensure fair access and prevent resource monopolization, employing a throttling kernel with modules for priority management, monitoring, metric calculation, and interleaving to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud computing environments serve multiple tenants with fluctuating workloads, then resource utilization and service coverage are improved, but performance degradation and resource monopolization occur
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring workload characteristics and adjusting resource allocation in real-time based on actual demand patterns, allowing the system to adapt to fluctuating workloads while maintaining performance guarantees
Solution Approach 2:
The patent applies different quality of service levels to different tenants and workload types, providing guaranteed performance for critical workloads while allowing best-effort service for non-critical workloads, thereby resolving the contradiction between serving diverse tenants and maintaining overall performance
2Speed
If I/O requests are processed without throttling, then request handling speed is improved, but pathological latency and resource monopolization occur
Solution Approach 1:
The patent implements periodic throttling mechanisms that regulate I/O request rates by introducing controlled delays when thresholds are exceeded, preventing any single tenant from monopolizing I/O resources while maintaining high throughput for compliant workloads
Solution Approach 2:
The patent employs feedback-based throttling where the system monitors I/O request patterns and dynamically adjusts throttling intensity based on observed latency and resource utilization, creating a self-regulating mechanism that prevents pathological latency without unnecessarily slowing down legitimate requests
3Stability of the object's composition
If resource allocation is statically configured, then system stability is improved, but adaptability to fluctuating workloads deteriorates
Solution Approach 1:
The patent transitions from static to dynamic resource allocation by implementing continuous monitoring of workload characteristics and automatically adjusting resource allocation policies in real-time, enabling the system to maintain stability through adaptive rather than rigid configuration
Solution Approach 2:
The patent enables the system to automatically self-adjust resource allocation based on observed workload patterns without external intervention, with the resource management system monitoring itself and making reallocation decisions to maintain both stability and adaptability
Data Source
AI summary
Systems, methods, and media for method for managing requests for computing resources. Methods may include dynamically throttling requests for computing resources generated by one or more tenants within a multi-tenant system, such as a cloud. In some embodiments, the present technology may dynamically throttle I/O operations for a physical storage media that is accessible by the tenants of the cloud. The present technology may dynamically throttle I/O operations to ensure fair access to the physical storage media for each tenant within the cloud.