SIOV Queue Scheduling to Reduce Contention and Queue Pollution
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Solution Overview
Problem
Conventional virtualization techniques face challenges in efficiently managing shared resources and meeting quality of service (QoS) demands in cloud computing environments, leading to increased contention and scheduling overhead, which hinders scalability and flexibility.
Innovation Solution
Implementing scalable input/output virtualization (SIOV) devices with a scheduler that employs dispatch and queue policies to manage shared work queues and hardware queues, prioritizing high-priority tasks, reducing hardware queue pollution, and optimizing resource utilization through policies like prioritization, exclusivity, distribution, throttling, and batch sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional virtualization techniques are used to manage shared resources, then resource sharing is enabled, but contention and scheduling overhead increase
Solution Approach 1:
The patent segments the queue management system into multiple independent hardware queues, each capable of autonomous operation. This segmentation allows different software processes to be assigned to different hardware queues, reducing contention for shared resources and minimizing scheduling overhead by enabling parallel processing across multiple queue instances.
2Adaptability or versatility
If more software processes share backend hardware resources, then scalability improves, but resource contention increases
Solution Approach 1:
The patent introduces hardware queues as intermediary structures between software processes and backend hardware resources. These hardware queues act as buffers and mediators that decouple the software processes from direct resource access, allowing multiple processes to share resources efficiently while reducing contention through queued-based access patterns and load distribution.
3Productivity
If shared work queues are used to accept tasks from multiple software processes, then resource utilization improves, but hardware queue pollution increases
Solution Approach 1:
The patent applies local quality by assigning specific software processes to specific hardware queues based on their characteristics and requirements. This localized assignment ensures that each hardware queue serves a dedicated set of processes, preventing pollution from unrelated processes while maintaining high resource utilization through targeted queue usage and process-queue matching.
Data Source
AI summary
In accordance with the described techniques, a scalable input/output virtualization (SIOV) device includes multiple hardware queues, backend hardware resources, and a command processor running scheduling firmware. The scheduling firmware selects a shared work queue of multiple shared work queues managed by the scheduling firmware from which to dispatch tasks based on one or dispatch policies. In addition, the scheduling firmware selects a hardware queue of the multiple hardware queues in which to enqueue the tasks based on one or more queue policies. Further, the scheduler dispatches the tasks from the shared work queue to the hardware queue, and the tasks are read from the hardware queue by the backend hardware resources for execution.