Cloud Management System for Dynamic Infrastructure Scaling
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Solution Overview
Problem
Existing web application infrastructure faces challenges in dynamically scaling infrastructure capacity to match load demand, leading to performance issues, scalability limitations, and availability concerns due to fixed infrastructure provisioning, which results in capacity waste and increased costs.
Innovation Solution
A cloud management system that dynamically directs traffic to geographically distributed server nodes, monitors performance metrics, and adjusts infrastructure capacity by launching or shutting down server instances in response to load changes, ensuring optimal performance, scalability, and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed infrastructure capacity is provisioned for web applications, then the application can handle baseline load demand, but the application performs poorly or becomes unavailable when traffic spikes occur
Solution Approach 1:
The patent implements dynamic infrastructure provisioning where virtual server instances are automatically launched or terminated based on real-time traffic demand. The system monitors load conditions and dynamically adjusts the number of active server instances, transforming the fixed infrastructure into a dynamic one that can adapt to traffic spikes and valleys, thereby maintaining both reliability and scalability
Solution Approach 2:
The system changes the parameter of infrastructure capacity by launching additional virtual server instances when traffic demand increases and terminating them when demand decreases. This parameter change allows the infrastructure to scale elastically, matching capacity to actual need and resolving the contradiction between maintaining availability during spikes and avoiding waste during low traffic periods
2Reliability
If conservative infrastructure provisioning is used to ensure availability during traffic spikes, then the application maintains service continuity, but substantial capacity waste occurs with server utilization below 20%
Solution Approach 1:
The system dynamically changes the capacity parameter by launching additional virtual server instances only when traffic demand exceeds current capacity and terminating instances when demand decreases. This on-demand capacity adjustment ensures availability during spikes while eliminating capacity waste during low traffic periods, directly addressing the contradiction between reliability and energy loss
Solution Approach 2:
The infrastructure automatically monitors its own load conditions and self-adjusts capacity by launching or terminating virtual server instances without manual intervention. This self-service mechanism ensures the system maintains availability during traffic spikes while automatically reducing capacity during low demand periods, preventing both service interruptions and capacity waste
3Productivity
If server clustering is implemented to improve scalability, then the application can handle increased load demand, but the setup and configuration become time consuming and expensive
Solution Approach 1:
The system uses virtual server instances that are copied from a standardized template, allowing rapid deployment of additional capacity when needed. These virtual instances replicate the necessary application stack without requiring complex manual configuration, enabling scalable infrastructure expansion while reducing setup time and complexity compared to traditional server clustering
Solution Approach 2:
The virtual server instances are designed as universal, multi-functional units that can handle various application workloads through standardized configurations. This universality allows the system to scale by simply deploying additional identical instances rather than configuring complex heterogeneous server clusters, reducing both time and cost while maintaining scalability
4Reliability
If site mirroring is used to provide availability during complete site failure, then the application remains operational, but the system becomes more complex requiring data synchronization between sites
Solution Approach 1:
The system creates virtual copies of server instances across multiple geographic locations through virtualization technology. These copied instances maintain application state and can serve as failover targets, providing site-level redundancy without requiring complex physical site mirroring infrastructure or manual data synchronization mechanisms
Solution Approach 2:
The virtualization platform acts as an intermediary that manages the complexity of distributed infrastructure. It handles instance deployment, state management, and failover coordination across multiple locations, abstracting away the complexity of data synchronization and site mirroring while maintaining high availability through geographically distributed virtual instances
Data Source
AI summary
A method for auto-scaling the infrastructure capacity of an application in response to client demands includes providing an application configured to run on an application infrastructure comprising a plurality of server nodes and to be accessed by clients via a first network. Next, providing traffic management means directing traffic from the clients to the server nodes of the application infrastructure. Providing monitoring means gathering performance metrics of the application and metrics of the application infrastructure. Providing controlling means configured to change the application infrastructure. Next, monitoring the performance metrics of the application and the metrics of the application infrastructure via the monitoring means thereby obtaining metrics information and then changing the application infrastructure based on the metrics information via the controlling means. Finally directing network traffic targeted to access the application to server nodes of the changed application infrastructure via the traffic management means.