18070118. HYBRID VIRTUAL MACHINE ALLOCATION OPTIMIZATION SYSTEM AND METHOD simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 HYBRID VIRTUAL MACHINE ALLOCATION OPTIMIZATION SYSTEM AND METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 HYBRID VIRTUAL MACHINE ALLOCATION OPTIMIZATION SYSTEM AND METHOD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
HYBRID VIRTUAL MACHINE ALLOCATION OPTIMIZATION SYSTEM AND METHOD
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Benjamin Eric Ahlvin of Seattle WA (US)
Sida Peng of Chestnut Hill MA (US)
Abigail Sandra Atchison of Seattle WA (US)
HYBRID VIRTUAL MACHINE ALLOCATION OPTIMIZATION SYSTEM AND METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18070118 titled 'HYBRID VIRTUAL MACHINE ALLOCATION OPTIMIZATION SYSTEM AND METHOD
Simplified Explanation
The patent application describes systems and methods for determining an allocation plan for allocating virtual machines (VMs) to a hosted service based on demand forecasting, user experience correlation, and eviction rate estimation using mixed-integer optimization.
- Demand forecasting predicts future demand for VMs
- Response curve correlates user experience to VM utilization
- Estimated spot eviction rate is used to determine the allocation plan
Potential Applications
This technology could be applied in cloud computing services, data centers, and virtualization platforms to optimize resource allocation and improve user experience.
Problems Solved
1. Efficient allocation of virtual machines based on demand forecasting 2. Optimization of user experience by correlating it with VM utilization
Benefits
1. Improved resource utilization 2. Enhanced user experience 3. Cost savings through optimized allocation plans
Potential Commercial Applications
Optimizing resource allocation in cloud computing services Improving performance in data centers Enhancing efficiency in virtualization platforms
Possible Prior Art
One possible prior art could be existing systems for resource allocation in cloud computing services or data centers that may not utilize demand forecasting, user experience correlation, and eviction rate estimation for determining allocation plans.
Unanswered Questions
How does the response curve correlate user experience to VM utilization?
The abstract mentions a response curve that correlates user experience to VM utilization, but it does not provide details on the methodology or specific metrics used in this correlation.
What specific factors are considered in the demand forecast for predicting future demand for VMs?
While the abstract mentions a demand forecast for predicting future demand for VMs, it does not specify the specific factors or variables considered in this forecast.
Original Abstract Submitted
Systems and methods for determining an allocation plan for allocating virtual machines (VMs) to a hosted service utilizes a demand forecast that predicts future demand for VMs, a response curve that correlates user experience to VM utilization, and an estimated spot eviction rate to determine the allocation plan. The demand forecast, the response curve and the eviction rate are processed using mixed-integer optimization to determine the numbers of VMs of each allocation type that should be online at any given time to meet demand.
