A Predictive Application Offloading Algorithm Using Small Datasets for Cloud Robotics
Manoj Penmetcha, Shyam Sundar Kannan, and Byung-Cheol Min

TL;DR
This paper introduces a predictive offloading algorithm for cloud robotics that uses small datasets to accurately estimate application execution time, optimizing resource use and reducing latency without prior application knowledge.
Contribution
It presents a novel predictive algorithm that estimates execution time based on limited previous observations, enhancing cloud offloading decisions in robotics.
Findings
Algorithm achieves acceptable accuracy with N>40 observations.
Linear regression effectively models execution time based on input size.
Validated on mobile robot path planning application.
Abstract
Many robotic applications that are critical for robot performance require immediate feedback, hence execution time is a critical concern. Furthermore, it is common that robots come with a fixed quantity of hardware resources; if an application requires more computational resources than the robot can accommodate, its onboard execution might be extended to a degree that degrades the robot performance. Cloud computing, on the other hand, features on-demand computational resources; by enabling robots to leverage those resources, application execution time can be reduced. The key to enabling robot use of cloud computing is designing an efficient offloading algorithm that makes optimum use of the robot onboard capabilities and also forms a quick consensus on when to offload without any prior knowledge or information about the application. In this paper, we propose a predictive algorithm to…
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Taxonomy
TopicsRobotics and Automated Systems · IoT and Edge/Fog Computing · Distributed and Parallel Computing Systems
