SaaS Resource Allocation via Historical CPU Memory Analysis
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Solution Overview
Problem
Existing cloud computing services, such as SaaS, face challenges in efficiently allocating resources to data processes, leading to potential waste of resources when data processes are assigned to computing servers with unsuitable configurations.
Innovation Solution
A resource analysis system and a resource allocation server work together to determine the resource type of data processes based on historical data, and then assign these data processes to computing servers with matching resource configurations, ensuring optimal resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data processes are assigned to computing servers with various capabilities without proper resource allocation, then the system can handle diverse workloads, but resources are wasted due to mismatched configurations
Solution Approach 1:
The system performs preliminary analysis of history data to determine the resource type of each data process before assignment. The resource analysis system calculates average CPU and memory resources from historical executions, then uses this information to pre-categorize data processes into resource types (CPU-intensive, memory-intensive, or balanced). This preliminary classification enables the resource allocation server to assign data processes to appropriately configured computing servers, avoiding resource wastage while maintaining the ability to handle diverse workloads.
2Productivity
If resource allocation is simplified without analyzing history data, then the allocation process is faster, but resource allocation efficiency decreases leading to wastage
Solution Approach 1:
The resource analysis system performs preliminary analysis of history data to determine resource types of data processes in advance. By calculating average CPU and memory resources from historical executions and categorizing processes before allocation, the system prepares allocation information beforehand. This allows the resource allocation server to make quick decisions based on pre-determined resource types, maintaining high allocation speed while ensuring efficient resource utilization.
3Measurement precision
If the system collects and analyzes history data for all data processes, then resource allocation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the resource allocation functionality into two independent components: a resource analysis system that handles history data collection and analysis, and a resource allocation server that performs assignment based on determined resource types. The resource analysis system is further divided into modules that separately calculate average CPU resources and average memory resources. This segmentation isolates the complexity of data analysis from the allocation decision-making process, allowing each component to be optimized independently while maintaining high measurement precision for resource type determination.
Data Source
AI summary
Embodiments are described for a resource analysis system comprising a memory and at least one processor coupled to the memory. The at least one processor is configured to receive history data of one or more data processes and determine an average central processing unit (CPU) resource and an average memory resource of a data process of the one or more data processes based on the history data. The at least one processor is further configured to determine a resource type of the data process based on the average CPU resource and the average memory resource and transmit a resource type report to a resource record server, wherein the resource type report indicates the resource type of the data process and the data process.