Policy-Based Cloud Resource Scaling via Dependency Data Sets
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Solution Overview
Problem
Business entities face challenges in determining the optimal time to purchase external cloud services or adjust internal cloud resources due to difficulties in assessing when it becomes economically advantageous to scale out or scale in, influenced by factors like market conditions and internal network capabilities.
Innovation Solution
A method and system for policy-based scaling of network resources, which involves creating a dependency data set for application components, setting scaling policies by tier, and applying these policies within the network to manage resource allocation between internal and external cloud networks, enabling efficient scaling out or scaling in based on defined conditions and thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual assessment and decision-making is used to determine when to scale cloud resources, then flexibility and control are maintained, but the complexity of monitoring and decision-making increases significantly
Solution Approach 1:
The system enables self-service automation where the cloud management platform automatically monitors resource utilization metrics, evaluates scaling conditions, and executes scaling operations without requiring manual intervention. The platform services itself by autonomously managing resource allocation based on predefined policies and real-time data analysis.
Solution Approach 2:
The system implements continuous feedback loops by monitoring resource utilization metrics, comparing them against threshold values, and automatically triggering scaling actions when conditions are met. The feedback mechanism tracks scaling outcomes and adjusts future decisions based on performance data, creating a closed-loop control system that simplifies operation while maintaining precision.
2Adaptability or versatility
If cloud resources are scaled out to external cloud services, then resource capacity and flexibility increase, but cost and resource expenditure increase
Solution Approach 1:
The system dynamically adjusts cloud resource allocation based on real-time utilization metrics and predefined policies. Resources are scaled out adaptably when demand increases and scaled in when demand decreases, creating a dynamic balance between adaptability and cost. The scaling thresholds and policies can be adjusted to optimize the trade-off between resource flexibility and expenditure.
Solution Approach 2:
The system changes key parameters such as resource allocation levels, scaling thresholds, and policy configurations to optimize the balance between adaptability and cost. By adjusting these parameters based on organizational needs and market conditions, the system achieves optimal resource utilization while minimizing unnecessary expenditure on cloud services.
3Loss of energy
If cloud resources are scaled in to reduce costs, then resource expenditure decreases, but resource capacity and ability to handle increased network activity diminish
Solution Approach 1:
The system maintains dynamic resource capacity by implementing automated scaling policies that respond to changing network activity levels. When traffic increases, resources are quickly scaled in to maintain capacity; when traffic decreases, resources are scaled out to reduce costs. This dynamic approach ensures the system can handle increased network activity when needed while minimizing costs during lower utilization periods.
Solution Approach 2:
The system performs preliminary scaling actions by pre-configuring scaling policies and thresholds that anticipate future resource needs. When utilization approaches predefined thresholds, the system proactively scales resources before capacity is exhausted, ensuring continuous ability to handle network activity while avoiding premature scaling that would increase costs unnecessarily.
Data Source
AI summary
A method of policy-based scaling of network resources comprises, with a processor, creating a dependency data set for a number of application components on a network, setting a number of scaling policies by tier based on the dependency data set, and applying the scaling policies within the network. A cloud management device for policy-based scaling of network resources comprises a processor, and a data storage device communicatively coupled to the processor, in which the processor creates a dependency data set for a number of application components on a network, and sets a number of scaling policies by tier based on the dependency data set.