Multi-Cloud Service Orchestration for Mobile IoT Connectivity
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Solution Overview
Problem
Cloud computing infrastructures face challenges in orchestrating the deployment of complex micro-services applications to IoT devices due to their mobility and unreliable connectivity, especially in decentralized and multi-owned networks, requiring efficient orchestration mechanisms that adapt to dynamic changes in device connectivity and resource availability.
Innovation Solution
A method and apparatus for orchestrating service deployment using a performance estimation function to determine the replication and location of software components based on the connectivity and performance metrics of end devices, allowing dynamic adaptation to changes in device connectivity and resource availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service deployment is orchestrated across multiple cloud infrastructures to serve mobile IoT devices, then service availability and reliability are improved, but orchestration complexity and deployment difficulty increase
Solution Approach 1:
The service is divided into multiple software components that can be independently deployed and replicated across different cloud infrastructures. Each component can be selectively placed in infrastructures based on device connectivity patterns, enabling reliable service availability while managing complexity through modular deployment units.
Solution Approach 2:
The orchestration mechanism dynamically adapts to changing connectivity conditions of IoT devices by evaluating performance estimation functions that consider current network states. The system automatically adjusts software component placement and replication in response to real-time connectivity changes, maintaining service reliability without requiring complex manual reconfiguration.
2Reliability
If replicable software components are deployed in multiple infrastructures to improve service performance, then service reliability is improved, but resource consumption and deployment cost increase
Solution Approach 1:
Software components are selectively replicated in specific infrastructures based on local device connectivity patterns and performance requirements. Rather than uniform replication, the system evaluates which infrastructures actually need additional components based on current device states, optimizing resource consumption while maintaining reliability where needed.
Solution Approach 2:
The system uses performance estimation functions that evaluate multiple parameters including connectivity quality, infrastructure capabilities, and device locations to determine optimal replication strategies. By dynamically adjusting replication decisions based on changing network conditions, the system maintains service reliability while minimizing unnecessary resource consumption.
3Productivity
If deployment decisions are made based on performance estimation functions considering multiple infrastructures and access points, then service performance is optimized, but computational complexity and processing time increase
Solution Approach 1:
The system pre-evaluates and stores performance estimation data for various infrastructure configurations before actual deployment decisions are needed. By having performance metrics and connectivity information pre-available, the system can make faster deployment decisions without performing complex calculations in real-time, thus optimizing service performance while reducing processing time.
Data Source
AI summary
A service description is provided to an orchestration function for a service to be deployed over at least one infrastructure of a plurality of infrastructures having respective access points. At least one of the plurality of software components to be deployed is tagged in the service description as being a replicable component adapted to be replicated in two or more of the plurality of infrastructures. The deployment is orchestrated by determining whether to replicate a replicable component and in which infrastructures based on a performance estimation function evaluated for a set of end devices of interest and candidate instances of the plurality of software components. The performance estimation function varies in dependence of (i) candidate infrastructures for deploying the candidate instances and (ii) one or more infrastructures to which the end devices of interest are respectively connected via an access point.