Firefly Virtual Machine Placement for Cloud-Wide Host Performance
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Solution Overview
Problem
Existing virtual machine deployment methods in cloud platforms fail to maximize the operation performance of all hosts by not considering the overall public service capability, leading to instability and inefficiency.
Innovation Solution
A virtual machine deployment method using a firefly algorithm to determine optimal host locations based on an average performance score, incorporating power consumption, CPU utilization, and resource balance, with iterative optimization to ensure maximum performance across all hosts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtual machines are scheduled based on individual host load balancing, then host overload or underload is avoided, but the overall operation performance of all hosts in the cloud platform cannot be guaranteed
Solution Approach 1:
The patent segments the virtual machine deployment decision into two levels: individual host load assessment and global cloud platform performance optimization. The firefly algorithm evaluates multiple hosts simultaneously based on their individual load states while optimizing for overall platform performance, resolving the contradiction between local and global optimization goals.
Solution Approach 2:
The patent implements a feedback mechanism where the firefly algorithm continuously evaluates host performance metrics (CPU utilization, memory usage, disk I/O, network bandwidth) and adjusts deployment decisions based on real-time system state. This feedback loop enables the system to adapt to changing conditions while maintaining optimal overall performance.
2Productivity
If virtual machines are deployed to balance individual host loads, then single host performance is stabilized, but the cloud platform's overall service capability is not optimized
Solution Approach 1:
The patent transforms the scheduling problem from a simple load-balancing decision into a multi-parameter optimization problem. The firefly algorithm evaluates hosts based on multiple parameters including CPU utilization, memory usage, disk I/O capacity, and network bandwidth, simultaneously optimizing for overall platform productivity while managing complexity through algorithmic automation.
3Reliability
If traditional load balancing is used for virtual machine scheduling, then individual host stability is maintained, but overall system efficiency decreases
Solution Approach 1:
The patent performs preliminary evaluation of all candidate hosts before making deployment decisions. The firefly algorithm pre-assesses multiple hosts based on their current resource utilization states and predicts their suitability for receiving new virtual machines, enabling proactive optimization that maintains stability while improving overall system efficiency.
Data Source
AI summary
A virtual machine deployment method and apparatus, a device, and a readable storage medium. The method uses a firefly algorithm to determine deployment locations of virtual machines, and takes an average performance score of all hosts to be selected as a target function. Locations of respective fireflies are locations of the respective hosts. An iterative optimization process of the firefly algorithm involves finding a host capable of maximizing operation performance of all hosts in a cloud platform after deployment of virtual machines is completed. Since the target function is the average performance score of all hosts after deployment of the virtual machines, selection of a host corresponding to the maximum target function value enables average performance of all hosts to be maximized after the virtual machines have been deployed to a destination host, thereby maximizing operation performance of all hosts in the cloud platform while scheduling the virtual machines.