Multi-Cloud Workload Placement Optimizing Transit Costs
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Solution Overview
Problem
In multi-cloud environments, there is a need to optimize workload placement across different clouds to minimize transit costs and ensure optimal performance, as transitioning workloads between cloud components incurs costs associated with resource utilization and monetary value.
Innovation Solution
A computer-implemented method and system that determine the appropriate network node for implementing a network function by calculating the amount of data transition required between functions and evaluating the processing capacity of each network node, selecting the node that minimizes costs and meets processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If workloads are transitioned between cloud components in different cloud environments, then service flexibility and distribution are improved, but transit costs and resource utilization efficiency deteriorate
Solution Approach 1:
The system continuously monitors workload characteristics, cloud environment states, and transit costs, using this feedback to dynamically adjust workload placement decisions and minimize ongoing transit costs while maintaining service flexibility
Solution Approach 2:
The system changes key parameters including workload placement location, data transition volume, and cloud environment selection based on real-time conditions, optimizing the balance between service flexibility and transit cost
2Reliability
If workloads are distributed across multiple cloud environments, then system reliability and performance are improved, but complexity of workload management deteriorates
Solution Approach 1:
The system introduces an intermediary workload placement system that automatically manages workload distribution across multiple cloud environments, handling the complexity of multi-cloud management while maintaining system reliability through coordinated placement decisions
Solution Approach 2:
The system enables self-service automated workload placement by using algorithms to independently determine optimal cloud environment selections and data transition strategies, eliminating the need for manual intervention in complex multi-cloud management
3Productivity
If data transition volume between cloud components is increased, then processing capacity utilization is improved, but transit cost and time consumption deteriorate
Solution Approach 1:
The system applies local quality by placing workloads and their associated data in specific cloud environments based on local conditions such as processing capacity availability, data access patterns, and transit cost characteristics, optimizing the balance between processing utilization and transition overhead
Data Source
AI summary
Systems, methods, computer-readable media are disclosed for determining a point of delivery (POD) device or network component on a cloud for workload and resource placement in a multi-cloud environment. A method includes determining a first amount of data for transitioning from performing a first function on input data to performing a second function on a first outcome of the first function; determining a second amount of data for transitioning from performing the second function on the first outcome to performing a third function on a second outcome of the second function; determining a processing capacity for each of one or more network nodes on which the first function and the third function are implemented; and selecting the network node for implementing the second function based on the first amount of data, the second amount of data, and the processing capacity for each of the network nodes.