Cost-Aware VM Template Clustering for Cloud Resource Allocation
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Solution Overview
Problem
In cloud computing, selecting optimal virtual machine (VM) templates is challenging due to variability in user requirements, leading to issues of over and under provisioning, which result in resource waste and impact Quality-of-Service (QoS).
Innovation Solution
A cost-aware clustering method is implemented to generate and select a pool of VM templates that efficiently match client needs by considering the collective cost of resources such as processing power, memory, and disk space, using a resource provisioning unit with configuration, server request, and template storage to determine the optimal set of templates for minimal cost allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of VM templates is increased to meet all user requirements, then user needs are better satisfied, but resource overprovisioning and template sprawling occur
Solution Approach 1:
The system changes the selection criteria from purely technical matching to cost-aware clustering by introducing cost parameters into the template selection process. Templates are clustered and selected based on both their technical suitability and cost efficiency, resolving the contradiction between meeting user needs and avoiding resource waste
Solution Approach 2:
Instead of providing all possible templates (excessive action), the system selectively provides a curated subset of templates that achieves the majority of user requirements at minimal cost. This partial action approach prevents template sprawling while still satisfying most user needs
2Extent of automation
If VM templates are selected based on provider system optimization and workload predictions, then resource allocation is automated, but user experience deteriorates due to over or under allocation
Solution Approach 1:
The system incorporates feedback mechanisms by monitoring actual VM performance and resource usage against predicted values. This feedback loop enables continuous optimization of template selection, allowing the system to learn from actual usage patterns and adjust future allocations to improve user experience while maintaining automation
3Loss of energy
If resource allocation is optimized to minimize over-allocation, then energy and resources are saved, but Quality-of-Service deteriorates due to under-allocation
Solution Approach 1:
The system changes the optimization parameters from purely technical resource matching to a cost-aware model that balances resource allocation with actual user needs. By considering both technical requirements and cost implications, the system achieves optimal allocation that minimizes waste while maintaining QoS
Data Source
AI summary
A template generator organizing templates in a cost-aware clustering, a method of allocating resources using cost-aware clustering and computer program products therefor. A resource provisioning unit generates, selects and maintains a selected number of resource templates. Each template specifies an allocable resource capacity configuration. Each requesting client device has resources allocated determined by one of the selected resource templates. A resource provisioning unit includes a configuration store with costs of allocable resources and associated attributes, a server request store with previously received requests, and an input parameter store with template list options. A template generator determines an optimum list of templates to satisfy previously received requests. A template store stores generated template lists.