Server Cluster Cost Models for Predictive Workload Allocation
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Solution Overview
Problem
Traditional workload management in Big Data environments is reactive, lacks predictive capabilities, struggles with monitoring multiple metrics and tool diversity, and is inconsistent due to varying administrator skills, leading to inefficient resource utilization and waste.
Innovation Solution
Implementing machine learning algorithms to analyze historical behaviors of server clusters, construct cost models, and automate workload distribution to predict processing efficiencies and adjust to changing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If administrators manually monitor and manage workload in Big Data environments, then they can identify abnormalities and take corrective actions, but the system becomes reactive, wastes computing power, and cannot provide consistent guidance due to varying administrative skills
Solution Approach 1:
The system enables self-service through automated workload management where the computing system independently monitors, analyzes, and adjusts workload distribution without human intervention. Machine learning models automatically detect abnormalities and optimize resource allocation, eliminating reliance on administrator expertise and providing consistent, scalable management across the Big Data environment.
Solution Approach 2:
The patent replaces manual administrative actions with automated machine learning-based systems. Instead of administrators manually monitoring metrics and making decisions, the system uses automated algorithms to analyze system state, predict abnormalities, and execute workload management actions, substituting human mechanical processes with intelligent automated systems.
2Measurement precision
If administrators monitor multiple machine and job metrics to achieve accurate workload management, then they can understand workload complexities and resource utilizations, but the monitoring complexity becomes unmanageable with hundreds of metrics
Solution Approach 1:
The system merges hundreds of individual metrics into unified, composite performance indicators through machine learning analysis. Instead of monitoring each metric separately, the system integrates multiple data points into consolidated views that capture essential workload characteristics, reducing monitoring complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw metrics and administrative decisions. These models process and synthesize multiple underlying metrics, transforming complex data into actionable insights that are easier to interpret and act upon, thereby reducing the perceived complexity for administrators.
3Adaptability or versatility
If different tools are used to access data in the Big Data environment, then various data access methods are available, but it becomes very difficult to diagnose coding qualities and provide useful recommendations
Solution Approach 1:
The system implements a universal interface that works across multiple data access tools and formats. The machine learning models are designed to handle diverse data sources and tool behaviors through standardized processing pipelines, enabling consistent coding quality assessment and recommendation generation regardless of which access tools are used.
4Adaptability or versatility
If new server clusters are added or old servers are replaced in the Big Data environment, then hardware capacities change and system evolves, but administrators cannot readily adjust their understanding and provide accurate advice
Solution Approach 1:
The system continuously monitors hardware configurations and automatically updates its understanding through feedback loops. Machine learning models learn from observed system behavior and configuration changes, automatically adapting to new server clusters and hardware replacements without requiring administrator reconfiguration, thereby preventing knowledge loss during system evolution.
Data Source
AI summary
Systems and methods are disclosed for managing workload among server clusters is disclosed. According to certain embodiments, the system may include a memory storing instructions and a processor. The processor may be configured to execute the instructions to determine historical behaviors of the server clusters in processing a workload. The processor may also be configured to execute the instructions to construct cost models for the server clusters based at least in part on the historical behaviors. The cost model is configured to predict a processor utilization demand of a workload. The processor may further be configured to execute the instructions to receive a workload and determine efficiencies of processing the workload by the server clusters based at least in part on at least one of the cost models or an execution plan of the workload.