Workload Placement Optimization via Multi-Dimensional Performance Measurement
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Solution Overview
Problem
Current methods for workload placement in cloud computing environments are insufficient as they rely on static measurements of processor utilization, failing to account for varying performance across different hardware and software configurations, leading to suboptimal server decisions and inefficient resource allocation.
Innovation Solution
A method and system for optimizing workload placement by monitoring performance metrics across hardware and network configurations, identifying optimal platforms based on throughput, response time, and service level agreements (SLAs), and migrating workloads to these platforms for improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional processor utilization tools are used to measure system performance, then basic utilization data can be obtained, but the measurements are insufficient for complex networked computing environments and cannot determine optimal server decisions
Solution Approach 1:
The patent segments performance measurement into multiple dimensions: individual processor metrics, platform-level metrics, and workload-specific metrics. By dividing the measurement system into these segments, it achieves both precise individual measurements and comprehensive system-wide analysis suitable for complex cloud environments.
Solution Approach 2:
The patent creates a universal measurement framework that works across diverse hardware platforms, operating systems, and workload types. The system collects and analyzes performance data from multiple sources (processors, memory, storage, network) to provide a comprehensive evaluation applicable to any cloud computing scenario.
2Productivity
If workloads are placed based on static processor utilization metrics, then simple decision-making is maintained, but suboptimal server decisions are made and resource allocation is inefficient
Solution Approach 1:
The patent performs preliminary performance measurements and platform evaluations before workload placement decisions are made. By pre-characterizing platforms with performance metrics and creating baseline data, the system enables efficient real-time decision-making without complex runtime analysis.
Solution Approach 2:
The patent implements a feedback mechanism where performance measurements from deployed workloads are continuously collected and used to refine platform evaluations. This feedback loop improves resource allocation efficiency over time by learning from actual performance data rather than relying solely on static metrics.
3Reliability
If performance measurements are collected across multiple platforms and configurations, then optimal platform identification is enabled, but measurement and detection complexity increases
Solution Approach 1:
The patent introduces intermediary components including performance measurement agents deployed on platforms and a central analysis system. These intermediaries handle the complexity of multi-platform measurements by standardizing data collection and providing a unified interface for analyzing performance across diverse hardware and software configurations.
Data Source
AI summary
Embodiments of the present invention provide a workload optimization approach that measures workload performance across combinations of hardware (platform, network configuration, storage configuration, etc.) and operating systems, and which provides a workload placement on the platforms where jobs perform most efficiently. This type of placement may be based on performance measurements (e.g., throughput, response, and other such service levels), but it can also be based on other factors such as power consumption or reliability. In a typical embodiment, ideal platforms are identified for handling workloads based on performance measurements and any applicable service level agreement (SLA) terms.