Hybrid Cloud Traffic Manager for Automatic Scaling and Data Security
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Solution Overview
Problem
Existing cloud solutions struggle to automatically scale cloud resources between private and public cloud platforms while maintaining access restrictions for private data, leading to inefficiencies and increased costs due to the need for manual scaling and resource procurement.
Innovation Solution
A hybrid cloud system that includes a public-network-accessible server set with a traffic manager, which receives traffic telemetry from a non-public server set to dynamically route requests and scale web application instances between the private and public cloud platforms, ensuring private data is only stored on-premises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a private cloud platform is used to store private data, then data security is improved, but the ability to scale resources automatically is worsened
Solution Approach 1:
The system segments cloud resources into two distinct parts: a private cloud platform for storing sensitive data and a public cloud platform for handling variable workloads. This segmentation allows each platform to fulfill its specific role - the private cloud ensures data security while the public cloud provides automatic scaling capabilities through its elastic resource allocation.
Solution Approach 2:
A hybrid cloud architecture acts as an intermediary between the private and public cloud platforms. This intermediary enables automatic scaling by routing non-sensitive workloads to the public cloud while keeping sensitive data in the private cloud, thus resolving the contradiction between security and scalability.
2Reliability
If manual scaling is performed in a private cloud, then data security is maintained, but operational efficiency and cost-effectiveness are worsened
Solution Approach 1:
The public cloud platform provides self-service automatic scaling capabilities that eliminate the need for manual intervention. The system automatically provisions or deprovisions resources based on real-time workload demands, significantly improving operational efficiency while the private cloud continues to maintain data security for sensitive information.
3Adaptability or versatility
If public cloud resources are used for scaling, then automatic scaling capability is improved, but data security and access restrictions are worsened
Solution Approach 1:
The system applies local quality by assigning different security characteristics to different cloud platforms. The private cloud is configured with strict security controls for storing sensitive data, while the public cloud is configured for elastic resource provisioning. This localized quality assignment allows each platform to optimize for its specific function without compromising overall system security.
Solution Approach 2:
The system extracts sensitive data and stores it exclusively in the private cloud platform, separating it from the public cloud resources used for scaling. This extraction ensures that even though public cloud resources are used for automatic scaling, the sensitive data remains isolated and protected in the private cloud environment.
4Reliability
If hardware is procured for maximum demand in a private cloud, then data security is maintained, but cost-effectiveness is worsened due to idle resources
Solution Approach 1:
The system introduces dynamics by combining static private cloud resources with dynamic public cloud resources. The private cloud maintains a baseline level of resources for security-critical operations, while the public cloud dynamically adjusts resources based on actual workload demands, eliminating the waste of permanently provisioning hardware for peak demand scenarios.
Solution Approach 2:
The system discards the need to permanently maintain unused hardware capacity in the private cloud by offloading variable workloads to the public cloud. When demand decreases, the public cloud resources are deprovisioned and the capacity is effectively recovered, preventing the energy waste associated with running idle hardware in the private cloud.
Data Source
AI summary
Methods, systems, and computer program products are provided that automatically scale an application between private and public cloud computing platforms, while simultaneous enforcing policies ensuring private data is persistently stored on the private cloud computing platform, but not the public cloud computing platform. A traffic manager on the public platform routes traffic to instances of a web app executing on a private platform. A traffic monitor on the private platform monitors performance criteria of the private platform, and reports traffic telemetry to the traffic manager. Based on the traffic telemetry, the traffic manager may instantiate one or more instances of the web app on the public platform to handle traffic. Private data gathered by such instantiated instance(s) is persisted to storage in the private platform, but not in the public platform.