Platform Capacity Tool for Multi-Resource Workload Projection
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Solution Overview
Problem
Traditional platform capacity planning is limited as it primarily considers CPU usage, failing to account for memory, storage, and network bandwidth consumption, leading to inefficient resource allocation and potential overburdening of computer systems when executing multiple applications and processes.
Innovation Solution
A platform capacity tool comprising a retrieval engine, capacity consumption engine, and workload projection engine that determines the projected workload by aggregating memory, CPU usage, storage, and network bandwidth requirements for each process and application, enabling accurate resource allocation and efficient scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional platform capacity planning only considers CPU usage, then the planning process is simple, but resource allocation becomes inefficient and systems may be overburdened
Solution Approach 1:
The patent segments resource capacity planning into distinct components: CPU capacity, memory capacity, storage capacity, and network bandwidth capacity. Each component is evaluated separately through dedicated capacity consumption engines, allowing comprehensive multi-dimensional resource assessment rather than relying on a single CPU metric.
2Measurement precision
If multiple resource types (memory, storage, network) are considered in capacity planning, then resource allocation accuracy improves, but the complexity of the planning system increases
Solution Approach 1:
The system divides complex multi-resource capacity planning into separate functional engines: a CPU capacity consumption engine, a memory capacity consumption engine, a storage capacity consumption engine, and a network bandwidth capacity consumption engine. Each engine independently evaluates its specific resource type, reducing overall system complexity while maintaining comprehensive assessment capability.
Solution Approach 2:
The patent creates a universal platform capacity tool that integrates multiple specialized capacity consumption engines into a single system. This multi-functional tool can evaluate CPU, memory, storage, and network resources simultaneously, providing comprehensive resource allocation guidance without requiring separate planning systems for each resource type.
3Measurement precision
If comprehensive resource aggregation is performed for all processes, then accurate workload projection is achieved, but processing time and computational overhead increase
Solution Approach 1:
The patent segments the workload analysis process into distinct phases: individual process capacity consumption evaluation, aggregation of process consumptions at the application level, and final workload projection. This segmented approach allows systematic processing of multiple processes without overwhelming computational burden.
Solution Approach 2:
The system performs preliminary capacity consumption evaluation for each individual process before aggregating results. By pre-calculating CPU, memory, storage, and network requirements for each process separately, the system prepares data in advance for efficient aggregation and workload projection, reducing overall processing time.
Data Source
AI summary
A platform capacity tool includes a retrieval engine, a capacity consumption engine, and a workload projection engine. The platform capacity tool determines whether there is sufficient memory, processor, and/or network resources to execute an application. The platform capacity tool makes these determinations based on process capacity consumptions and/or application capacity consumptions.