Cloud Resource Rightsizing via Utilization Metrics
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Solution Overview
Problem
Cloud-based scalable distributed search data analytics services face inefficiencies in resource configuration, leading to suboptimal performance and increased costs due to inadequate rightsizing of computing, memory, and storage capacities.
Innovation Solution
A system utilizing a resource configuration optimization (RCO) stack that analyzes historical utilization metrics to generate recommendations for computing capacity, memory capacity, and storage volume rightsizing, ensuring efficient resource allocation and cost-effectiveness by determining the optimal instance type and storage volume type based on actual usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resource configuration is increased to improve service performance, then processing speed and data retrieval rates improve, but system costs increase
Solution Approach 1:
The system dynamically adjusts resource configuration parameters (CPU, memory, storage, I/O capacity) based on monitored utilization metrics and performance requirements, transitioning from static to adaptive parameter settings to optimize the balance between productivity and resource consumption
Solution Approach 2:
The system performs self-optimization by automatically monitoring its own resource utilization metrics and generating rightsizing recommendations without external intervention, enabling the service to adapt its configuration based on actual usage patterns
2Quantity of substance
If resource configuration is decreased to reduce costs, then system costs decrease, but service performance and processing speed deteriorate
Solution Approach 1:
The system continuously monitors resource utilization metrics and performance indicators, using this feedback to generate rightsizing recommendations that maintain service performance while optimizing resource allocation and reducing unnecessary capacity
Solution Approach 2:
The system applies rightsizing recommendations partially, adjusting resource configuration to match actual utilization needs rather than maintaining excessive capacity, thereby reducing resource consumption while preserving adequate performance
3Speed
If computing capacity is increased to improve processing speed, then latency decreases, but resource utilization efficiency worsens
Solution Approach 1:
The system transitions from static computing capacity allocation to dynamic adjustment based on monitored utilization metrics, enabling the service to scale computing resources according to actual demand and improve overall utilization efficiency while maintaining processing speed
Data Source
AI summary
Embodiments of this disclosure disclose a method and system for optimizing resources for cloud-based scalable distributed search data analytics service. The method may include generating a computing capacity rightsizing recommendation on the computing capacity based on the computing utilization metrics and generating a memory capacity rightsizing recommendation on the memory capacity based on the memory utilization metrics. The method may further include determining a recommended instance type for the service resource unit based on the computing capacity rightsizing recommendation and the memory capacity rightsizing recommendation. The method may further include performing a storage volume check on the service resource unit to obtain a storage volume check result. The method may further include determining whether to accept the recommended instance type as a final optimization recommendation based on the storage volume check result, the computing capacity rightsizing recommendation, and the memory capacity rightsizing recommendation.