Cloud Mobile Task Offloading for Sensor-Deficient Devices
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Solution Overview
Problem
Cloud computing and mobile devices often face overload and resource shortages, leading to performance issues when handling computing tasks, particularly when they lack necessary resources like sensors.
Innovation Solution
An adaptive system program that monitors resource utilization and device capabilities to determine whether to offload computing tasks from a mobile device to a cloud environment or vice versa, ensuring optimal resource sharing and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If computing tasks are handled locally on mobile devices, then response time is reduced, but device resource exhaustion occurs when sensors or processing power are insufficient
Solution Approach 1:
The patent combines mobile devices and cloud computing environment into an integrated computing system where resources are shared and tasks can be executed on either platform. This merging allows the system to leverage both local processing speed and cloud resource availability, resolving the contradiction between fast local response and reliable task completion.
Solution Approach 2:
The system dynamically determines whether to execute tasks locally or in the cloud based on real-time assessment of device resources, task requirements, and current load conditions. This dynamic adaptability allows the system to optimize between response time and task completion capability by selecting the appropriate execution platform for each task.
2Reliability
If computing tasks are offloaded to cloud environment, then resource availability is improved, but network dependency and latency increase
Solution Approach 1:
The system offloads only the portions of tasks that require cloud-specific resources (such as sensors not available on mobile devices) to the cloud environment, while keeping other portions that can be executed locally. This partial offloading approach maintains resource availability benefits while minimizing network latency by keeping local processing wherever possible.
3Reliability
If mobile devices have all necessary sensors and processing power, then task completion capability is improved, but device complexity and cost increase
Solution Approach 1:
The integrated computing system provides universal access to a diverse set of sensors and processing resources through the cloud platform, allowing any mobile device to access specialized resources on-demand. This eliminates the need for every device to have all possible sensors built-in, reducing device complexity while maintaining task completion capability through resource sharing.
4Power
If cloud computing environment handles all tasks, then processing power is improved, but mobile device autonomy and local responsiveness are reduced
Solution Approach 1:
The system segments tasks into components that can be executed locally and components that require cloud resources. Mobile devices maintain autonomy by executing local components independently, while cloud components are handled by the processing power of the cloud environment. This segmentation preserves device autonomy while leveraging cloud processing power where needed.
Data Source
AI summary
In an approach for converging a cloud computing environment and a mobile device into an integrated environment, a processor receives a request to complete a computing task on a first device, wherein the computing task requires a physical sensor. A processor determines that the computing task should be offloaded from the first device to a second device based, at least in part, on the first device lacking the physical sensor and the second device having the physical sensor. A processor offloads the computing task from the first device to the second device. A processor receives a result of the computing task from the second device.