Workload Hosting Platform Recommendation System
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Solution Overview
Problem
Users deploying workloads to hosting platforms often lack information about the features provided by different platforms, leading to suboptimal workload execution due to random selection or cost-driven choices, which may not utilize features like I/O optimization or high memory utilization.
Innovation Solution
A platform analysis module replicates user workloads as test workloads and executes them on various hosting platforms to collect execution statistics, comparing these with the features offered by each platform to recommend the most suitable platform for optimal feature utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users randomly select or choose hosting platforms based on single characteristics (e.g., cost), then hosting platform selection is simple, but workload performance optimization deteriorates
Solution Approach 1:
The system performs self-evaluation by automatically analyzing workload characteristics and comparing them against platform features without requiring user expertise. The platform autonomously determines the optimal hosting environment by executing test workloads and analyzing performance metrics, eliminating the need for users to manually evaluate complex platform differences.
Solution Approach 2:
The system introduces an intermediary evaluation layer that mediates between the user's simple selection and the complex platform characteristics. This intermediary automatically assesses workload requirements and matches them with appropriate platform features, bridging the gap between ease of selection and performance optimization.
2Loss of time
If users deploy workloads to hosting platforms without evaluating features, then deployment process is quick, but feature utilization deteriorates
Solution Approach 1:
The system performs preliminary evaluation by executing test workloads on candidate platforms before actual deployment. This advance assessment of platform features and workload compatibility ensures that when deployment occurs, the optimal platform is already identified, minimizing deployment time while maximizing feature utilization.
Solution Approach 2:
The system creates test workload copies to evaluate platform features without affecting the original workload deployment timeline. These copies are executed on various platforms to gather performance data, allowing the system to make informed deployment decisions without delaying the main deployment process.
3Manufacturing precision
If comprehensive platform evaluation is performed, then workload-performance matching is optimized, but evaluation complexity increases
Solution Approach 1:
The system uses simplified test workload copies that replicate essential workload characteristics without requiring full-scale production workloads. These copies enable comprehensive platform evaluation with reduced complexity, allowing the system to assess multiple platform features efficiently while maintaining matching precision.
Solution Approach 2:
The evaluation process is segmented into distinct phases: test workload execution, performance metric collection, platform feature comparison, and optimal platform selection. This segmentation breaks down the complex evaluation task into manageable steps, reducing overall system complexity while maintaining comprehensive assessment capabilities.
Data Source
AI summary
Systems, methods, and other embodiments associated with recommending hosting platforms for user workload execution are described. In one embodiment, a method includes replicating a user workload to generate a test workload. The example method may also include collecting execution statistics of the user workload executing on a first hosting platform and the test workload executing on a second hosting platform. The example method may also include comparing the execution statistics with first features of the first hosting platform and second features of the second hosting platform. In response to the execution statistics matching the second features to a greater degree than the first features, a recommendation is generated to execute the user workload on the second hosting platform. The example method may also include transmitting the recommendation over a computer network to a remote computer for display to a user on a display of the remote computer.