Dynamic Resizer for Cloud Server Instance Optimization
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Solution Overview
Problem
Cloud computing customers face challenges in automatically optimizing computing capacity usage, leading to inefficient resource allocation and increased operational costs due to manual resizing of server instances, which is time-consuming and burdensome.
Innovation Solution
An interactive dynamic resizer application that automatically selects server instances based on real-time usage data and policy rules, including usage time thresholds and computing power capacity percentages, to dynamically resize instances and optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual resizing of server instances is performed, then customers can adjust computing capacity to match demand, but the process is time-consuming and burdensome requiring up to 90 seconds to stop and over 3 minutes to configure and boot new instances
Solution Approach 1:
The system pre-configures multiple server instances with different computing capacities before resizing is needed. When a resizing event is triggered, the system immediately activates a pre-configured instance of the appropriate size, eliminating the need to stop the current instance and configure a new one from scratch. This reduces resizing time from several minutes to seconds.
Solution Approach 2:
The system creates and maintains copies of server instances with different configurations in advance. Instead of creating a new instance during resizing, the system switches to a pre-existing copy that matches the required computing capacity, significantly reducing the time required for capacity adjustment.
2Loss of energy
If customers manually monitor and resize server instances, then they can optimize operational costs, but the process is difficult and burdensome requiring continuous manual intervention
Solution Approach 1:
The system continuously monitors computing capacity usage metrics and automatically compares them against cost-optimization criteria. When resizing is indicated, the system automatically initiates the resizing process and notifies the customer, eliminating the need for manual monitoring and decision-making while optimizing operational costs.
Solution Approach 2:
The system autonomously performs the entire resizing process based on monitored usage patterns and pre-configured policies. The customer defines initial parameters and receives notifications, but the actual monitoring, decision-making, and execution of resizing operations are performed automatically by the system without continuous manual intervention.
3Productivity
If server instances are resized frequently to match varying demand, then resource utilization is optimized, but instance stability and reliability may be compromised due to repeated stop-start cycles
Solution Approach 1:
The system segments the server infrastructure into multiple independent instances with different computing capacities. Instead of repeatedly resizing a single instance, the system switches between pre-configured instances of appropriate sizes, isolating the stability of each instance while enabling flexible resource allocation. This allows frequent resizing operations without compromising the stability of running instances.
Data Source
AI summary
Systems and methods are provided for optimizing computer processing power in cloud computing systems. The method may include obtaining, by an interactive dynamic resizer application stored on non-volatile computer readable memory operatively connected to an administrator device, status information of a first server instance; accessing policy rule information for a first set of server instances associated with a first server; identifying a second server instance based on the status information and the policy rules information; automatically selecting the second server instance; generating resizing instructions based on the selected second server instance; and sending the resizing instructions to a cloud network.