Cloud Management Server Predictive Resource Allocation
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Solution Overview
Problem
Conventional cloud computing services face challenges in providing fast and efficient computing resources to both business and private users due to long response times when activating virtual machines, making them unsuitable for private users who require immediate access.
Innovation Solution
A management server system that predicts and reserves virtual machines and devices based on historical data and usage patterns, allowing for pre-emptive deployment and quick service provision, including a virtual device manager, resource pool, and predicting unit to optimize resource allocation and reduce response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual machines are activated on-demand in conventional cloud computing services, then resource allocation flexibility is improved, but response time deteriorates
Solution Approach 1:
The patent applies preliminary action by predicting future resource demands using historical data and usage patterns, then pre-allocating virtual machines and virtual devices before actual requests arrive. The predicting unit analyzes log information to forecast resource needs, and the resource pool pre-deploys virtual resources in advance, eliminating the activation delay that occurs in conventional on-demand systems.
2Loss of time
If virtual machines are pre-deployed to reduce response time, then response time is improved, but resource waste increases
Solution Approach 1:
The patent implements feedback mechanisms where the predicting unit continuously monitors actual resource usage and compares it with predictions. Log information collecting units gather data on resource consumption patterns, and this feedback is used to refine future predictions. The system adjusts pre-allocation strategies based on actual demand patterns, ensuring that pre-deployed virtual resources are accurately matched to future needs and minimizing waste.
Solution Approach 2:
The system dynamically adjusts prediction parameters and resource allocation parameters based on changing usage patterns. The predicting unit modifies its forecasting models according to seasonal variations, user behavior changes, and resource utilization trends, allowing the pre-allocation strategy to adapt to different demand scenarios and reduce unnecessary resource deployment.
3Productivity
If resource pool management is implemented to optimize allocation, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies self-service principles by implementing automated prediction and allocation systems that operate without manual intervention. The predicting unit automatically analyzes log information and forecasts resource demands, the virtual machine manager autonomously allocates resources based on predictions, and the system self-adjusts to changing patterns. This automation reduces the need for complex manual management procedures while maintaining high resource utilization efficiency.
Data Source
AI summary
A management server and method for providing a cloud computing service at high speed and reasonable cost, are provided. The management server provides a virtual machine to a client as a computing resource. The virtual machine is multiplexed by operating multiple virtual devices on a single virtual machine. Accordingly, demand for computing resources may be predicted in advance and may be provided to a user more efficiently.