Data Center Load Balancing via Server Load Variance Minimization
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Solution Overview
Problem
Existing scheduling methods in cloud computing data centers fail to achieve load balance among physical servers due to inconsistencies between user specification requirements and server configurations, leading to load imbalance.
Innovation Solution
A method and device that monitor current utilization ratios of physical machines in a scheduling domain, calculate load mean values and variance, and select a physical machine with a minimum load variance to deploy tasks, ensuring optimal resource allocation and balancing load across servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple allocation scheduling methods (round-robin, weighted round-robin, minimum load, weighted least-connection, or hash method) are adopted, then the scheduling process is simple and fast, but load balance of physical servers cannot be achieved
Solution Approach 1:
The patent implements feedback by continuously monitoring the actual running state of virtual machines on physical servers and using this information to adjust scheduling decisions. The system collects runtime data on resource consumption and server load, then feeds this back into the scheduling algorithm to dynamically balance loads, resolving the contradiction between simple scheduling and effective load balancing.
Solution Approach 2:
The patent transitions from static scheduling methods to dynamic scheduling that adapts to changing system conditions. The scheduling algorithm continuously updates based on real-time server status, virtual machine performance metrics, and resource utilization patterns, enabling the system to maintain load balance while preserving scheduling simplicity through automated adaptation.
2Adaptability or versatility
If user specification requirements are strictly followed for resource allocation, then user needs are met, but load imbalance occurs due to inconsistency with server configurations
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting allocation parameters based on the matching between user specifications and server configurations. The system modifies scheduling parameters such as weight factors, selection criteria, and distribution ratios in response to observed discrepancies between requested and actual server capabilities, thereby maintaining specification compliance while achieving load balance.
Solution Approach 2:
The patent implements local quality by tailoring allocation decisions to the specific characteristics of each physical server and its configured virtual machines. The scheduling system considers local server attributes, hardware configurations, and runtime performance metrics to make differentiated allocation decisions, ensuring that each server is utilized optimally according to its specific capabilities rather than applying uniform allocation rules.
Data Source
AI summary
The present disclosure relates to a method and a device for implementing load balance of data center resources, and the method includes: obtaining current utilization ratios of properties of each physical machine in a scheduling domain, and determining, according to the current utilization ratios, physical machines that have residual capability satisfying a current allocation task, where the properties include a CPU load, a memory load and a network load; predicting a load degree of each of the physical machines on the assumption that the virtual machine is allocated by each of the physical machines; selecting, according to the predicted load degree of each of the physical machines, a physical machine from the physical machines to allocate the virtual machine. By adopting the solutions of the present disclosure, the problem of the load imbalance caused by the inconsistency of specification requirements of users and specifications of physical servers is solved.