Hybrid Cloud ML Broker with Interactive AR Configuration
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Solution Overview
Problem
Cloud service consumers face challenges in efficiently managing computationally intensive machine learning tasks due to high costs and compromised time and security when relying solely on on-premises computation or using public cloud services, necessitating a cost-effective solution for complex data processing in hybrid cloud environments.
Innovation Solution
A system that enables users to configure and distribute machine learning tasks across multiple cloud providers and on-premises resources through an interactive augmented reality view, dynamically identifying resource requirements and recommending an optimized load distribution model based on user-defined contextual parameters, including cost and security constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If on-premises computation is used for machine learning tasks, then cost is reduced, but time and security are compromised
Solution Approach 1:
The system segments machine learning tasks into different components that can be executed on different platforms. Critical security-sensitive operations are performed on secure on-premises resources, while computational-intensive tasks are offloaded to cloud providers, achieving both cost efficiency and security maintenance.
Solution Approach 2:
The patent merges on-premises and cloud computing resources into a hybrid cloud architecture. This combination allows the system to leverage the security advantages of on-premises infrastructure while utilizing the scalability and cost-effectiveness of cloud services, resolving the contradiction between cost and reliability.
2Reliability
If public cloud services are used for machine learning tasks, then time and security are improved, but cost increases
Solution Approach 1:
The system segments workloads by prioritizing security-critical operations for on-premises execution and reserving cloud resources for computational-intensive tasks, thereby reducing overall cloud spending while maintaining security and performance standards.
Solution Approach 2:
The patent applies local quality by executing security-sensitive operations locally on-premises while using cloud resources for tasks where their computational power is most beneficial, optimizing the cost-performance ratio for each specific workload type.
3Loss of energy
If hybrid cloud is used for machine learning tasks, then cost-effectiveness is improved, but device complexity increases
Solution Approach 1:
The patent introduces a cloud broker as an intermediary layer that manages the complexity of hybrid cloud orchestration. The broker handles resource allocation, task distribution, and coordination between on-premises and cloud resources, making the complex hybrid system manageable for end users while maintaining cost-effectiveness.
Solution Approach 2:
The system implements self-service capabilities where the hybrid cloud platform automatically optimizes resource allocation and task distribution without requiring manual intervention. This automation reduces the operational burden on users despite the underlying system complexity, enabling cost-effective hybrid cloud utilization.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for cloud service brokerage. The embodiment may include receiving a data set and user defined contextual parameters relating to a machine learning (ML) problem of a user to be performed on the data set. The embodiment may include identifying a resource requirement of the ML problem and available resources. The embodiment may include enabling user configuration of the contextual parameters in an interactive augmented reality (AR) view. The embodiment may include identifying a set of clusters upon which to execute computing tasks of the ML problem. The set of clusters is identified out of the available resources. The embodiment may include implementing a ML evaluation process to determine an optimized load distribution model for execution of the computing tasks within the set of clusters. The embodiment may include implementing the optimized load distribution model.