VM Resource Allocation via Workload Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data centers face challenges in efficiently managing resources due to the difficulty in selecting the optimal number and size of virtual machines (VMs) to serve applications, which complicates resource provisioning and affects average resource utilization.
Innovation Solution
A dynamic resource provisioning strategy is implemented, decomposing the problem into determining the optimal number and size of VMs and scheduling incoming requests, using a resource management server that predicts workloads and allocates resources based on service level requirements, optimizing VM selection and distribution across VM pools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static resource provisioning strategy is used, then device complexity is reduced, but average resource utilization deteriorates
Solution Approach 1:
The patent implements dynamic resource provisioning by continuously monitoring workload metrics (CPU utilization, memory usage, I/O operations) and adjusting the number and configuration of virtual machines in real-time based on actual demand, transforming the static provisioning model into a dynamic adaptive system that optimizes resource utilization while managing complexity through automated control
2Productivity
If dynamic resource provisioning is implemented, then average resource utilization is improved, but device complexity increases
Solution Approach 1:
The system establishes a closed-loop feedback mechanism where workload metrics are continuously monitored and fed back to the resource provisioning module, which automatically adjusts virtual machine allocation based on the feedback signals, enabling dynamic optimization without manual intervention and managing complexity through automated control algorithms
Solution Approach 2:
The resource provisioning system operates autonomously by automatically detecting workload changes, calculating optimal resource allocation, and adjusting virtual machine configurations without human intervention, allowing the system to self-manage the complexity of dynamic provisioning while maintaining high resource utilization
3Productivity
If optimal number and size of VMs is selected, then resource utilization is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements a unified workload measurement framework that simultaneously monitors multiple resource metrics (CPU utilization, memory usage, I/O operations, network traffic) using a single integrated system, enabling comprehensive workload characterization without requiring separate measurement mechanisms for each metric and reducing overall measurement complexity
Data Source
AI summary
An embodiment of the disclosure provides a method, performed by a resource management server, for resource allocation in a virtualization environment. The resource management server includes a non-transient computer readable medium and a processor to execute computer executable instructions stored on the non-transient computer readable medium, so that when the instructions are executed, the resource management server performs the method of: (a) determining an average service rate for each virtual machine (VM) pool running an application; (b) predicting a workload of the application; (c) determining a set of collaborative VMs to serve the application in order to satisfy a service level requirement, the set of collaborative VMs comprising a number of selected VMs from the VM pools running the application; and (d) distributing incoming application requests of the application among the VMs in the set of collaborative VMs.