Cloud VM Profile Scheduling to Prevent Resource Contention
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Solution Overview
Problem
In IaaS environments, high load stacking leads to resource contention and performance degradation due to multiple tenants with high resource requirements running on the same physical resources, necessitating improved resource scheduling to enhance utilization and reduce costs.
Innovation Solution
A resource scheduling method that involves acquiring deployment attribute information of a virtual machine, matching it with a target virtual machine profile based on historical operation data, and scheduling the machine to hosts with sufficient available resources, thereby avoiding high resource requirements on the same host.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple tenants with high resource requirements are scheduled to the same physical host, then resource consolidation and utilization are improved, but resource contention and performance degradation occur
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing resource consumption information for different virtual machine profiles based on historical operation data. When a virtual machine needs scheduling, the system retrieves the matching profile's resource consumption information in advance, enabling proactive resource allocation decisions that prevent resource contention before it occurs, thus maintaining both high utilization and stable performance
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual resource consumption of scheduled virtual machines and comparing it with the expected consumption from profiles. This feedback loop allows the system to refine resource consumption predictions and adjust scheduling decisions dynamically, ensuring that resource allocation optimizes utilization while preventing performance degradation through real-time adjustments
2Productivity
If resource scheduling is optimized to meet multiple tenants' requirements, then resource utilization improves, but scheduling complexity increases
Solution Approach 1:
The system applies parameter changes by transforming complex multi-dimensional scheduling decisions into simplified profile-matching operations. Instead of evaluating numerous scheduling parameters simultaneously, the system categorizes virtual machines into profiles based on key parameters (resource requirements, workload characteristics) and uses pre-computed resource consumption information for each profile, significantly reducing scheduling complexity while maintaining optimization effectiveness
Solution Approach 2:
The system uses copying by creating virtual machine profiles that represent typical workload patterns and resource consumption characteristics. Once a profile is established based on historical data, it serves as a template that can be quickly matched against new virtual machines, avoiding the need to perform complex analysis for each individual scheduling decision and thereby reducing system complexity
Data Source
AI summary
A resource scheduling method based on a cloud service platform, an electronic device, and a non-transitory computer-readable storage medium are provided. The method includes: acquiring deployment attribute information of a to-be-scheduled virtual machine that is applied for deployment by a tenant on a cloud service platform; acquiring a target virtual machine profile matching the deployment attribute information of the to-be-scheduled virtual machine from a virtual machine profile set of the cloud service platform, the virtual machine profile is used to describe deployment attribute information and resource consumption information corresponding to a virtual machine, and the resource consumption information is used to describe an expected consumption of at least one resource of a virtual machine matching the deployment attribute information of the virtual machine; and scheduling the to-be-scheduled virtual machine according to the resource consumption information in the target virtual machine profile.