Virtual Network Interface Queue Allocation for Cloud VM I/O
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Solution Overview
Problem
Current cloud computing environments face inefficiencies in network interface queue utilization, where virtual machines may have either too many or too few queues, leading to compromised user experience and resource waste.
Innovation Solution
A user-configurable API is introduced to dynamically allocate network interface queues based on the specific needs of each virtual machine, allowing users to customize the number of transmit and receive queues for each virtual networking interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual machines are allocated a fixed number of network interface queues based on their size and CPU capacity, then larger virtual machines with more CPUs receive more queues, but this results in queue resource waste for virtual machines with lower data throughput needs and insufficient queues for virtual machines focused on data transmission
Solution Approach 1:
The patent implements dynamic queue allocation where the number of network interface queues for a virtual machine is not fixed but can be adjusted based on actual workload requirements. The system allows runtime modification of queue counts, enabling virtual machines to adapt their network interface configuration dynamically rather than being constrained by static allocations based on CPU capacity or instance type.
Solution Approach 2:
The patent changes the parameter of queue allocation from a fixed value determined by virtual machine size and CPU count to a flexible parameter that can be modified based on actual network I/O demands. This allows the system to optimize queue utilization by adjusting the number of queues according to specific workload characteristics rather than relying on predetermined configurations.
2Speed
If virtual machines are allocated a larger number of queues to support high data throughput operations, then data transmission performance is improved, but virtual machines with mainly management functions waste queue resources
Solution Approach 1:
The patent applies local quality by allowing different virtual machines to have different queue allocations tailored to their specific functional requirements. Management-focused virtual machines can be allocated fewer queues while data transmission-optimized virtual machines can receive more queues, ensuring that each virtual machine receives appropriate resources for its specific workload rather than applying a uniform allocation strategy.
Solution Approach 2:
The system enables dynamic adjustment of queue allocation based on actual network I/O performance needs. Virtual machines can have their queue counts modified at runtime to match changing workload demands, allowing the system to optimize for high throughput when needed while conserving resources during periods of lower network activity or for workloads with different characteristics.
3Loss of energy
If virtual machines are allocated fewer queues to reduce resource consumption, then resource efficiency is improved, but virtual machines focused on data transmission experience insufficient queue utilization
Solution Approach 1:
The patent implements dynamic queue allocation that allows virtual machines to scale their queue resources up or down based on actual network I/O demands. Virtual machines focused on data transmission can dynamically allocate more queues when high throughput is required, while maintaining efficient resource utilization during periods of lower demand, thus balancing both productivity and resource efficiency.
4Adaptability or versatility
If virtual machines are allocated more queues to support potential high throughput needs, then future scalability is improved, but current resource utilization is compromised due to over-provisioning
Solution Approach 1:
The patent enables virtual machines to dynamically adjust their queue allocation based on actual workload requirements rather than being locked into predetermined configurations. This allows the system to provision queues on-demand, improving current resource utilization while maintaining the ability to scale up when needed, thus achieving both efficient current utilization and future scalability.
Solution Approach 2:
The system creates a universal queue allocation mechanism that can serve multiple different workload types and performance requirements. The same dynamic allocation system can optimize for high throughput, low latency, or resource efficiency depending on the specific virtual machine's needs, making the queue allocation system universally adaptable to various scenarios rather than requiring separate configurations for different use cases.
Data Source
AI summary
An Application Programming Interface (API) allows a launching of a virtual machine where a queue count can be configured by a user. More specifically, each virtual machine can be assigned a pool of queues. Additionally, each virtual machine can have multiple virtual networking interfaces and a user can assign a number of queues from the pool to each virtual networking interface. Thus, a new metadata field is described that can be used with requests to launch a virtual machine. The metadata field includes one or more parameters that associate a number of queues with each virtual networking interface. A queue count can be dynamically configured by a user to ensure that the queues are efficiently used given that the user understands the intended application of the virtual machine being launched.