Cloud Compute Termination Policies for Instance Selection
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Solution Overview
Problem
Users in cloud computing environments lack control over the scaling of compute resources, such as virtual machines, as they cannot specify which instances to terminate first during scaling down, leading to inefficient resource management and increased costs.
Innovation Solution
Implementing user-specified termination policies that allow users to select which virtual machine instances to terminate first based on criteria like age, launch configuration, or billing intervals, enabling the automatic scaling service to manage resources more efficiently and effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional cloud computing resource scaling is used, then automatic scaling based on demand is achieved, but users lack control over which instances to terminate first
Solution Approach 1:
The system allows users to pre-define termination policies that specify which instances should be terminated first based on criteria such as age, launch configuration, or billing intervals. These policies are established in advance and automatically applied during scaling operations, giving users control without adding operational complexity.
Solution Approach 2:
The automatic scaling service applies the user-defined termination policies autonomously when scaling operations are needed. The system self-manages the complex decision-making process of selecting which instances to terminate, based on the pre-established policies, thereby maintaining ease of operation while providing user control.
2Productivity
If instances are terminated without specific policies, then scaling operations are simplified, but resource management efficiency decreases and costs increase
Solution Approach 1:
The termination policies allow different instances to be treated differently based on their specific characteristics such as age, launch configuration, or billing intervals. This localized differentiation enables the system to make intelligent decisions about which instances to terminate first, optimizing resource management efficiency and reducing waste.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor instance characteristics and scaling outcomes. Based on this feedback, the termination policies can be adjusted and refined to better align with user needs and optimization goals, continuously improving resource management efficiency.
3Quantity of substance
If all instances are treated equally during scaling, then system simplicity is maintained, but cost optimization is lost
Solution Approach 1:
The termination policies introduce dynamic differentiation among instances based on their characteristics such as age, launch configuration, or billing intervals. This dynamic approach allows the system to optimize cost by treating instances differently according to their specific properties, while the automation maintains overall system simplicity.
Data Source
AI summary
Approaches are described for enabling a user to specify one or more termination policies that can be used to select which instances in a group of virtual machines (or other compute resources) allocated to the user should be terminated first when scaling down the group of virtual machine instances. The termination policies can be utilized by an automatic scaling service when managing the resources in a multitenant shared resource computing environment, such as a cloud computing environment.