Virtual Machine Resource Optimization via User Feedback
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Solution Overview
Problem
Current systems for managing virtual machines (VMs) face challenges in optimizing resource allocation, leading to over-allocation and energy waste, while under-allocation impairs user Quality-of-Service (QoS). Users' feedback is not considered for subsequent resource adjustments, making it difficult to maintain efficient IT resource utilization.
Innovation Solution
A system where client devices include an optimization agent that monitors resource usage and suggests configuration changes, allowing users to adjust resources on-the-fly, enabling real-time optimization of resource allocation in a cloud environment. This involves a management system that dynamically adjusts resource capacity based on user feedback and usage profiles, allowing for reconfiguration of virtual desktops to optimize resource usage and reduce energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If physical hardware is replaced with virtual machines to reduce IT infrastructure costs, then resource sharing efficiency is improved, but determining optimal capacity and configuration allocation becomes difficult
Solution Approach 1:
The patent implements feedback mechanisms where user feedback on desktop configuration and capacity is collected and used to iteratively optimize VM resource allocation. The system continuously monitors resource usage and adjusts capacity allocation based on actual performance data and user preferences, transforming the complex allocation problem into a dynamic optimization process.
Solution Approach 2:
The system transitions from static capacity allocation to dynamic resource management. Virtual machine capacity and configuration are not fixed but are continuously adjusted based on real-time resource usage monitoring, workload predictions, and user feedback, allowing the system to adapt to changing demands and optimize resource sharing efficiency dynamically.
2Reliability
If resource allocation is increased to maintain Quality-of-Service, then user QoS is improved, but resource waste and energy consumption increase
Solution Approach 1:
The patent applies partial action by allocating resources dynamically based on actual needs rather than providing full capacity to all VMs continuously. The system monitors resource usage and allocates capacity partially to each VM based on current workload demands, user feedback, and performance requirements, ensuring QoS is maintained only where necessary while reducing overall energy consumption.
Solution Approach 2:
The system changes resource allocation parameters dynamically based on monitored conditions. Virtual machine capacity, CPU allocation, memory allocation, and storage resources are adjusted as parameters respond to changing workload demands, user feedback, and performance metrics, allowing the system to maintain QoS during high-demand periods while reducing resource allocation and energy consumption during low-demand periods.
3Loss of energy
If resource allocation is decreased to reduce waste, then energy consumption is reduced, but user Quality-of-Service deteriorates
Solution Approach 1:
The patent implements feedback loops where user feedback on desktop performance and resource adequacy is continuously collected and used to adjust resource allocation. This feedback mechanism ensures that resource reduction does not compromise QoS, as the system responds to user-reported performance issues by reallocating capacity to maintain acceptable service levels.
Solution Approach 2:
The system performs preliminary resource allocation based on workload predictions and user profiles before actual resource demands arise. By anticipating resource needs through monitoring patterns and user behavior, the system pre-allocates capacity to prevent QoS deterioration while avoiding excessive allocation that would waste energy, thus resolving the contradiction between energy reduction and service maintenance.
4Productivity
If user feedback is collected and processed to optimize resource allocation, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically processes user feedback and adjusts resource allocation without requiring complex manual intervention. The optimization agent autonomously monitors resource usage, processes user feedback, and makes capacity allocation decisions, reducing the operational complexity of managing the feedback-loop system while improving resource utilization efficiency.
Data Source
AI summary
A shared resource system, method of optimizing resource allocation in real time and computer program products therefor. At least one client device includes an optimization agent monitoring resource usage and selectively suggesting changes to resource configuration for the client device. A management system, e.g., in a cloud environment selectively makes resource capacity available to client devices and adjusts resource capacity available to client devices in response to the optimization agent. Client devices and provider computers connect over a network. The client devices and provider computer pass messages to each other over the network.