Cloud Deployment Orchestration for Multi-Provider Workload Management
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Solution Overview
Problem
Comparing and managing cloud solution providers is challenging due to changing workloads, cost goals, and performance requirements, especially when needing to switch between different cloud server services seamlessly and transparently.
Innovation Solution
A method and system for transmitting deployments to multiple cloud server services, utilizing machine learning algorithms to determine which deployments to change and update based on received requests, with each deployment containing a metrics repository, an analytic module, and an inter-cloud routing module to analyze and route requests across different cloud services based on metrics such as cost, performance, and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud solution providers are used for storage and content delivery, then content providers can leverage cloud infrastructure, but comparing and managing multiple cloud providers becomes difficult due to changing workloads, cost goals, and performance requirements
Solution Approach 1:
The patent introduces a cloud management platform that acts as an intermediary between content providers and multiple cloud solution providers. This platform abstracts the complexity of managing multiple cloud providers by providing unified deployment interfaces, centralized monitoring, and automated decision-making capabilities. The platform receives deployment requests, analyzes them against multiple cloud providers' capabilities and metrics, and automatically selects and deploys to the most suitable provider, thereby simplifying the management complexity while maintaining adaptability.
2Productivity
If deployments are transmitted to multiple cloud server services, then workload distribution and provider comparison improve, but the system complexity and difficulty of managing changing workloads increase
Solution Approach 1:
The patent segments the cloud deployment system into distinct modular components: deployment packages containing application code and configuration, cloud adapter modules for each cloud provider, metrics collection agents, and a central orchestration platform. Each component has a specific function and can be independently developed, deployed, and managed. This segmentation allows the system to handle multiple cloud providers and changing workloads efficiently while keeping the overall system complexity manageable through clear separation of concerns.
Solution Approach 2:
The system implements dynamic workload management by continuously monitoring performance metrics, cost parameters, and workload characteristics across multiple cloud providers. The orchestration platform dynamically adjusts deployment decisions based on real-time conditions, automatically migrating workloads between cloud providers when it becomes beneficial. This dynamic approach improves productivity by optimizing resource utilization while the automated nature of the adjustments prevents system complexity from becoming unmanageable.
3Reliability
If seamless and transparent switching between cloud server services is implemented, then service continuity is maintained, but the complexity of analyzing metrics and making routing decisions increases
Solution Approach 1:
The cloud management platform implements self-service capabilities by automatically collecting performance metrics from cloud providers, analyzing the data against predefined criteria and goals, and making routing decisions without human intervention. The system autonomously monitors service health, detects when switching between cloud providers is necessary, executes the switching process, and maintains service continuity. This automation maintains high reliability while preventing the complexity of metric analysis and routing decisions from becoming unmanageable by delegating these tasks to the self-service platform.
4Measurement precision
If machine learning algorithms are used to determine deployment changes, then decision accuracy and resource optimization improve, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models offline using historical deployment data and performance metrics. The models are trained to recognize patterns and make accurate predictions about optimal cloud provider selection and workload distribution. Once trained, the models can make real-time deployment decisions with high accuracy without requiring extensive computational resources during actual operations. This preliminary training phase improves decision accuracy while minimizing the processing time and computational requirements during live deployment operations.
Data Source
AI summary
Systems and methods are shown for providing metric driven deployments to cloud server services that are adapted to interface with each provider. In some implementations, there is insight and control over network, disk, CPU, and other activity giving the ability to do performance metrics analysis for a given application or service between different cloud server services as each application or service is run in a container within an instance running on the respective cloud server service. This allows for comparison between a plurality of providers for a given container driven by one or more metrics such as cost, flexibility, and performance. The instances which runs the one or more containers can be scaled up and down to a desired workload performance. Replication of images between providers can allow for seamless changing between providers based on changing goals as well as distribution of workload.