Local Instance Scaling Manager for Dynamic Resource Redistribution
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Solution Overview
Problem
Existing virtualized computing services face challenges in dynamically adjusting resource allocations among compute instances to match varying workload demands without manual intervention, leading to inefficiencies in resource utilization and potential security vulnerabilities.
Innovation Solution
Implementing a local instance scaling manager within a compute instance to automatically redistribute resources among parent and child instances based on customizable scaling policies, allowing for dynamic resource adjustments and enhanced security through isolated child instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual resource allocation is used among compute instances, then security control is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The compute instance autonomously performs resource allocation and scaling decisions based on pre-configured policies, eliminating the need for manual intervention while maintaining security controls. The instance monitors its own resource usage and automatically adjusts allocations to match workload demands.
Solution Approach 2:
The system dynamically changes resource allocation parameters (CPU, memory, storage) based on workload conditions and policy rules. Scaling policies define parameter thresholds and adjustment rules that automatically modify resource allocations without manual security review for each change.
2Productivity
If dynamic resource adjustment is implemented, then resource utilization efficiency is improved, but system complexity deteriorates
Solution Approach 1:
Each compute instance implements its own local scaling manager that operates independently based on instance-specific policies. This distributes the complexity management to local level rather than requiring centralized control, reducing overall system complexity while enabling dynamic resource adjustment.
Solution Approach 2:
The system implements dynamic resource allocation where compute instances can automatically scale resources up or down based on real-time workload conditions. The flexibility and adaptability of dynamic allocation simplify resource management compared to static configurations that require complex manual adjustment mechanisms.
3Productivity
If automated scaling is implemented, then operational efficiency is improved, but security vulnerabilities deteriorate
Solution Approach 1:
Security policies and scaling rules are pre-configured before automated scaling begins. These pre-established policies define authorized resource allocations and access controls, ensuring that automated scaling operations remain within security boundaries without requiring real-time security interventions.
Solution Approach 2:
The scaling manager continuously monitors resource usage and system state, comparing actual conditions against pre-configured policies. This feedback mechanism ensures that automated scaling decisions comply with security requirements by validating each scaling action against established policy rules before execution.
Data Source
AI summary
At a first compute instance run on a virtualization host, a local instance scaling manager is launched. The scaling manager determines, based on metrics collected at the host, that a triggering condition for redistributing one or more types of resources of the first compute instance has been met. The scaling manager causes virtualization management components to allocate a subset of the first compute instance's resources to a second compute instance at the host.