Virtual Machine Computing Capacity Estimation via Calibrated Process Iteration
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Solution Overview
Problem
Existing methods struggle to accurately determine the computing capacity of virtual machines for multimedia processing tasks, as they rely on a priori evaluations that are impractical due to the complex interactions between CPU, memory, and other resources, and are not well-suited for virtual machines with flexible resource allocations.
Innovation Solution
A method involving the execution of calibrated computer processes with varying loads to iteratively determine the computing capacity of machines, allowing for the estimation of resources needed for real-time multimedia processing by analyzing output quality metrics and adjusting the number of processes until a desired precision is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a priori evaluation of resources is used to determine computing capacity, then the method is simple to implement, but it is inaccurate for multimedia processing tasks due to complex interactions between CPU, memory, and other resources
Solution Approach 1:
The system performs self-testing by automatically executing calibrated computer processes on the target machine and measuring actual performance metrics. The machine evaluates its own computing capacity through real execution rather than relying on external a priori evaluations, thereby achieving accurate measurements while maintaining implementation simplicity.
Solution Approach 2:
The system uses feedback from actual execution of calibrated processes to determine computing capacity. By measuring real-time performance metrics such as processing time, resource utilization, and output quality, the system adjusts its understanding of the machine's true capacity, resolving the contradiction between simple implementation and accurate measurement.
2Ease of operation
If resources are reserved for virtual machines based on a priori evaluation, then resource allocation is simple, but it leads to suboptimal utilization due to inability to accurately predict actual needs
Solution Approach 1:
The system performs preliminary calibration by executing standardized computer processes with known computational characteristics before actual task allocation. This preliminary action establishes accurate baseline measurements of computing capacity, enabling both simple automated allocation and optimal resource utilization without requiring complex real-time predictions.
Solution Approach 2:
The system changes the parameters of calibrated computer processes systematically to probe different aspects of computing capacity. By varying process complexity, resource requirements, and execution conditions, the system comprehensively characterizes machine performance, enabling accurate resource allocation decisions that balance simplicity with productivity.
3Measurement precision
If calibrated computer processes are executed to determine computing capacity, then accurate measurement is achieved, but the process requires multiple iterations and increases time consumption
Solution Approach 1:
The system segments the computing capacity evaluation into multiple calibrated processes with different computational characteristics. By dividing the overall measurement task into independent, standardized sub-processes, the system achieves comprehensive and accurate measurement while enabling parallel execution and reducing total evaluation time through efficient resource utilization.
Data Source
AI summary
The estimation of a computing capacity of a machine. The computing capacity is estimated by iteratively adding and removing calibrated computer processes on the machine, and performing a sum of computing loads of processes that execute on the machine. In order to characterize the ability of a machine to run in parallel a number of processes having a defined computing load, the processes are associated to a condition of success.