Predictive Cloud Resource Provisioning for Virtual Machines
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Solution Overview
Problem
Current cloud computing environments face challenges in optimizing resource allocation for virtual machines due to static provisioning methods that do not account for varying runtime utilization, leading to inefficient use of resources and poor load balancing across geographically dispersed nodes with multiple hypervisors and vendors.
Innovation Solution
A method for predictively provisioning cloud computing resources by using historical utilization data from similar virtual machines, combined with data mining techniques like regression or constraint analysis, to determine optimal resource allocation, and selecting resources compatible across different hypervisors, ensuring efficient use and compatibility in multi-vendor environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static provisioning methods are used for virtual machines, then provisioning simplicity is maintained, but resource allocation efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting historical utilization data from similar virtual machines before provisioning new VMs. Data mining techniques are applied in advance to analyze patterns and predict future resource needs, enabling proactive resource allocation rather than reactive adjustments
Solution Approach 2:
The provisioning system transitions from static to dynamic by continuously monitoring runtime resource utilization and automatically adjusting resource allocation. The system adapts to changing workloads by re-provisioning resources based on actual usage patterns, making the allocation flexible and responsive
2Speed
If initial static allocations are used for virtual machines, then provisioning speed is maintained, but load balancing quality deteriorates
Solution Approach 1:
The system implements continuous feedback loops by monitoring runtime resource utilization of virtual machines and using this information to adjust future provisioning decisions. Historical utilization data is fed back into the data mining models to improve prediction accuracy and optimize load distribution across the cloud environment
Solution Approach 2:
The provisioning system performs self-service by automatically analyzing historical data, predicting resource needs, and making provisioning decisions without manual intervention. The system self-optimizes resource allocation across multiple hypervisors and geographic locations based on learned patterns from virtual machine utilization data
3Adaptability or versatility
If ad hoc provisioning is performed without centralized management, then deployment flexibility is maintained, but resource allocation optimization deteriorates
Solution Approach 1:
The system achieves universality by creating a centralized data mining framework that works across multiple hypervisors, vendors, and geographic locations. The unified approach to collecting and analyzing historical utilization data enables consistent resource allocation optimization throughout the entire cloud computing environment regardless of underlying infrastructure differences
Data Source
AI summary
Methods, computer program products, and systems are presented. The methods include, for instance: predictively provisioning, by one or more processor, cloud computing resources of a cloud computing environment for at least one virtual machine; and initializing, by the one or more processor, the at least one virtual machine with the provisioned cloud computing resources of the cloud computing environment. In one embodiment, the predictively provisioning may include: receiving historical utilization information of multiple virtual machines of the cloud computing environment, the multiple virtual machines having similar characteristics to the at least one virtual machine; and determining the cloud computing resources for the at least one virtual machine using the historical utilization information of the multiple virtual machines. In another embodiment, the predictively may include updating a provisioning database with the historical utilization information of the multiple virtual machines of the cloud computing environment.