# OPPLOAD: Offloading Computational Workflows in Opportunistic Networks

## TL;DR

OPPLOAD is a new framework enabling efficient computation offloading of workflows in opportunistic networks, addressing challenges of intermittent connectivity and resource limitations in mobile environments.

## Contribution

It introduces a novel, adaptable offloading framework for workflows in opportunistic networks with automatic worker matching and load balancing.

## Key findings

- Feasibility demonstrated through experimental evaluation.
- Supports dynamic worker selection based on capabilities.
- Open source Python implementation available.

## Abstract

Computation offloading is often used in mobile cloud, edge, and/or fog computing to cope with resource limitations of mobile devices in terms of computational power, storage, and energy. Computation offloading is particularly challenging in situations where network connectivity is intermittent or error-prone. In this paper, we present OPPLOAD, a novel framework for offloading computational workflows in opportunistic networks. The individual tasks forming a workflow can be assigned to particular remote execution platforms (workers) either preselected ahead of time or decided just in time where a matching worker will automatically be assigned for the next task. Tasks are only assigned to capable workers that announce their capabilities. Furthermore, tasks of a workflow can be executed on multiple workers that are automatically selected to balance the load. Our Python implementation of OPPLOAD is publicly available as open source software. The results of our experimental evaluation demonstrate the feasibility of our approach.