Cognitive Cloud Offloading for Multi-RAT Wireless Devices
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Solution Overview
Problem
Current mobile devices face challenges in efficiently managing resources such as memory and battery power when handling computationally intensive applications like augmented reality and 3D gaming, due to limited local processing capabilities and increased energy consumption during data transfer for cloud offloading.
Innovation Solution
A cognitive cloud offloading system that dynamically distributes processing and data between a mobile device and a remote server using multiple radio access technologies (RATs), employing a time-adaptive heuristic to optimize CPU, memory, and connectivity usage, while selecting the best radio interfaces for data transfer and scheduling components for parallel execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If computationally intensive applications are executed locally on mobile devices, then processing capability is improved, but energy consumption and memory usage increase
Solution Approach 1:
The application is divided into multiple executable components with different computational requirements. Some components are executed locally on the mobile device while others are offloaded to remote servers, allowing the system to segment processing tasks between local and remote resources to optimize energy consumption while maintaining processing capability.
Solution Approach 2:
A cognitive offloading system acts as an intermediary between the mobile device and remote servers. This intermediary intelligently determines which application components should be executed locally and which should be offloaded to remote servers, managing the transition and coordination to balance processing capability and energy consumption.
2Use of energy by moving object
If cloud offloading is used to reduce local processing burden, then energy consumption is reduced, but data transfer time and network dependency increase
Solution Approach 1:
The system dynamically adjusts the distribution of executable components between local and remote execution based on real-time conditions such as network availability, server load, and local resource status. This dynamic adaptation allows the system to optimize the balance between energy consumption and data transfer time according to current operational context.
Solution Approach 2:
The cognitive offloading system monitors and responds to changes in system parameters including network connectivity quality, server response times, and local device state. By detecting parameter changes and adapting component distribution accordingly, the system minimizes data transfer time while maintaining energy efficiency.
3Productivity
If multiple radio access technologies are used for offloading, then data transfer capacity is improved, but system complexity increases
Solution Approach 1:
The cognitive offloading system is designed to work with multiple radio access technologies (LTE, WiFi, 5G) through a unified interface and common decision-making framework. This universal approach allows the system to leverage multiple communication interfaces for enhanced data transfer capacity while avoiding the complexity that would arise from implementing separate specialized systems for each radio technology.
Data Source
AI summary
A system, method and apparatus having a mobile device with a plurality of radio access technologies, a server computer in the cloud running a cognitive offloader and cloud scheduler improves the execution time and reduces energy use of an application program residing on or accessible to the mobile device and having a plurality of components by apportioning executable tasks and routing data between the mobile device and the server computer based upon a cognitive offloader algorithm aware of dynamic parameters such as CPU and memory use, energy costs for transmissions and measurements of connectivity. The scheduling of tasks apportioned between the computing devices in the system may be enlightened by a component dependency graph of the application that is used by the offloader algorithm.