Cloud Workload Optimization via Static Analysis
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Solution Overview
Problem
Cloud computing environments face sub-optimal resource utilization due to varying user demands and conflicting objectives of resource efficiency and user satisfaction, often resulting in inefficient workload execution and resource allocation.
Innovation Solution
A method for cloud optimization using workload analysis, which involves static and dynamic analysis to identify suitable cloud resources based on workload characteristics, ensuring optimal resource allocation and utilization through a processor and memory-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud computing resources are allocated based on general demand without analysis, then resource availability is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent performs static analysis on workload characteristics before resource allocation to identify key performance indicators and architectural features. This preliminary analysis enables the system to pre-determine suitable resource types and configurations, avoiding trial-and-error allocation and improving overall resource utilization efficiency without requiring complex runtime adjustments
Solution Approach 2:
The patent replaces manual or heuristic-based resource allocation with an automated analysis system that uses static analysis techniques to evaluate workload characteristics. This substitution of mechanical/manual processes with automated computational analysis reduces human intervention while improving allocation accuracy and efficiency
2Measurement precision
If static analysis is performed on workload sections, then workload characteristics are accurately determined, but analysis time increases
Solution Approach 1:
The patent extracts and analyzes only critical sections of the workload that contain key performance indicators and architectural characteristics. By identifying and focusing analysis on these essential portions rather than the entire workload, the system achieves accurate workload characterization while minimizing analysis time through selective examination of representative code segments
Solution Approach 2:
The patent performs static analysis on selected portions of the workload rather than the complete workload. This partial analysis approach targets specific sections that provide sufficient information for resource matching, achieving adequate precision without the time cost of exhaustive full-workload analysis
3Productivity
If cloud resources are allocated without matching workload characteristics, then allocation speed is maintained, but workload execution performance deteriorates
Solution Approach 1:
The patent enables workloads to effectively select their own suitable resources by analyzing workload characteristics and automatically matching them with appropriate cloud computing resources. The system autonomously performs the matching process without requiring user intervention or complex manual configuration, allowing workloads to self-optimize their resource allocation based on their inherent characteristics
Solution Approach 2:
The patent uses static analysis results as feedback to guide resource allocation decisions. By analyzing workload characteristics and using this information to select matching resources, the system creates a feedback loop where workload properties directly inform allocation choices, improving execution performance while maintaining automated operation
Data Source
AI summary
A method for cloud optimization using workload analysis is provided in the illustrative embodiments. An architecture of a workload received for execution in a cloud computing environment is identified. The cloud computing environment includes a set of cloud computing resources. A section of the workload is identified and marked for static analysis. Static analysis is performed on the section to determine a characteristic of the workload. A subset of the set of cloud computing resources is selected such that a cloud computing resource in the subset is available for allocating to the workload and has a characteristic that matches the characteristic of the workload as determined from the static analysis. The subset of cloud computing resources is suggested to a job scheduler for scheduling the workload for execution.