Dynamic Instance Selection for Cloud Resource Allocation
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Solution Overview
Problem
Existing approaches to allocating resources for computing tasks in shared environments often result in excessive capacity allocation due to the inability to accurately predict the required capacity for tasks without historical data, leading to increased costs, complexity, and resource inefficiency.
Innovation Solution
A method is introduced that involves a testing phase where jobs are processed with different resource instance types, determining optimal instance sizes based on parameter comparisons with previous jobs, and selecting the smallest instance type that can successfully complete a task, while ensuring sufficient capacity without excessive allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If excess resource capacity is allocated to ensure sufficient capacity for tasks without historical data, then task completion reliability is improved, but resource cost increases
Solution Approach 1:
The system performs preliminary testing phases with different instance types before actual task execution. By pre-testing and establishing performance baselines for various task types, the system can accurately predict resource requirements without allocating excessive capacity, thus maintaining reliability while reducing waste.
Solution Approach 2:
The system implements feedback mechanisms by monitoring actual task performance against predicted resource usage. This feedback loop allows continuous refinement of resource allocation predictions, ensuring that sufficient capacity is allocated for reliability while eliminating excessive allocation that increases costs.
2Reliability
If resource capacity is increased to handle tasks without historical performance data, then task success rate is improved, but resource complexity increases
Solution Approach 1:
The system segments tasks into categories based on their characteristics and historical data availability. By grouping similar tasks together and establishing baseline resource requirements for each category, the system simplifies the complexity of allocating resources for novel tasks while maintaining high success rates.
Solution Approach 2:
The system changes parameters by using dynamic resource allocation based on task-specific parameters rather than static over-provisioning. By adjusting resource capacity based on measured task parameters and historical performance data, the system reduces allocation complexity while maintaining task success rates.
3Reliability
If larger instance types are allocated to unknown tasks, then capacity sufficiency is improved, but resource wastage increases
Solution Approach 1:
The system implements dynamic resource allocation where instance type selection is adjusted based on real-time task characteristics and historical performance data. This dynamic approach ensures capacity sufficiency for each specific task while avoiding the static over-provisioning that leads to resource wastage across multiple tasks.
Solution Approach 2:
The system replaces the mechanical approach of over-provisioning resources with a data-driven prediction system. By using historical performance data and machine learning models to predict optimal instance types, the system eliminates the need for conservative over-allocation, thereby reducing resource wastage while maintaining capacity sufficiency.
4Reliability
If excessive resource capacity is allocated for parallel tasks, then task completion reliability is improved, but cost increases
Solution Approach 1:
The system performs preliminary analysis and testing for each task type before parallel execution. By establishing accurate resource requirements in advance through testing phases and historical data analysis, the system can allocate precise amounts of resources for parallel tasks, ensuring completion reliability without excessive capacity allocation that increases costs.
Data Source
AI summary
Approaches presented herein can allocate resources in such a way that sufficient capacity will be provided to perform a job or task, while minimizing any excess capacity included with those allocated resources. A number of jobs can be performed with differently sized resource instances in some embodiments, to determine an instance size, from a set of available sizes, that is appropriate for each of those jobs. Various parameters for those jobs can be determined, and those values associated with the determined instance sizes. When a new job is received that is to be performed, the parameter values for that job can be compared against the corresponding values for these testing jobs, and an instance size can be selected where a testing job that was successfully performed with that instance size had the same or larger values for these parameters.