Don't Let Me Down! Offloading Robot VFs Up to the Cloud

TL;DR

This paper introduces DLMD, an algorithm that optimally offloads robot functions to the Cloud to reduce Edge resource use while meeting strict latency requirements, demonstrated through formulation, comparison, and stress testing.

Contribution

DLMD is a novel algorithm that efficiently offloads robot functionalities to the Cloud, balancing latency constraints and resource consumption, outperforming existing methods.

Findings

DLMD finds solutions in less than 30ms.

DLMD is optimal in a local warehousing scenario.

DLMD uses only 5% of Edge resources under stress.

Abstract

Recent trends in robotic services propose offloading robot functionalities to the Edge to meet the strict latency requirements of networked robotics. However, the Edge is typically an expensive resource and sometimes the Cloud is also an option, thus, decreasing the cost. Following this idea, we propose Don't Let Me Down! (DLMD), an algorithm that promotes offloading robot functions to the Cloud when possible to minimize the consumption of Edge resources. Additionally, DLMD takes the appropriate migration, traffic steering, and radio handover decisions to meet robotic service requirements as strict latency constraints. In the paper, we formulate the optimization problem that DLMD aims to solve, compare DLMD performance against state of art, and perform stress tests to assess DLMD performance in small & large networks. Results show that DLMD (i) always finds solutions in less than 30ms; (ii) is optimal in a local warehousing use case, and (iii) consumes only 5% of the Edge resources upon network stress.