Deep-Inspection Weighted Round Robin for Virtualized Resource Scheduling
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Solution Overview
Problem
Current scheduling algorithms, such as fair queuing, fail to ensure fair access to shared hardware resources among clients in virtualized environments, leading to inefficiencies and processing delays due to lack of visibility into resource utilization and differing operation complexities.
Innovation Solution
Implementing a deep-inspection weighted round robin technique that provides clients with visibility into the weights of operation queues, allowing intelligent selection of hardware resources to minimize execution time and maximize system performance by distributing work across available resources based on queue weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional fair queuing algorithms are used to schedule operations across hardware resources, then implementation simplicity is maintained, but fairness and efficiency of resource access deteriorates due to lack of visibility into resource utilization and operation complexities
Solution Approach 1:
The system implements feedback by providing clients with visibility into queue weights of hardware resources. Clients can inspect the current state of operation queues and use this information to make intelligent scheduling decisions, thereby achieving fair and efficient resource access without requiring complex centralized scheduling algorithms
Solution Approach 2:
The system enables self-service by allowing clients to autonomously select hardware resources based on queue weight information. Instead of relying on a centralized scheduler to make decisions, clients independently choose resources that will minimize their execution time, improving both fairness and efficiency
2Productivity
If operations are distributed across multiple hardware resources without deep inspection, then system throughput is maintained, but execution time increases due to inability to minimize queuing delays
Solution Approach 1:
The system performs preliminary action by providing clients with queue weight information before operations are submitted. Clients can inspect the current state of hardware resources and proactively choose the best resource for each operation, minimizing queuing delays and execution time while maintaining high throughput
3Ease of operation
If hardware resources are shared among multiple clients without weight visibility, then resource utilization is simplified, but efficiency deteriorates due to overloading and underutilization of resources
Solution Approach 1:
The system maintains ease of operation by allowing continued sharing of hardware resources among multiple clients while introducing feedback through queue weight visibility. Clients can see the current load on each resource and adjust their operation submission accordingly, preventing overloading and underutilization without complicating the resource sharing model
Data Source
AI summary
Techniques for intelligent use of multiple virtualized resources via deep-inspection weighted round robin are described. Hardware resources shared between multiple clients can be intelligently selected between by the clients to perform operations. Weights of operation queues for the hardware resources that are assigned to instances can be provided to the clients to give the clients insight into how busy particular hardware resources are and what types of operations the hardware resources will be performing. In some embodiments, the clients can make use of a weighted round robin scheme to select between candidate hardware resources, allowing clients to effectively distribute operations between different hardware resources.