An optimization framework for task allocation in the edge/hub/cloud paradigm
Andreas Kouloumpris, Georgios L. Stavrinides, Maria K. Michael, Theocharis Theocharides

TL;DR
This paper introduces a binary integer linear programming framework for optimal task allocation in IoT edge/hub/cloud systems, aiming to minimize latency or energy consumption while considering device constraints.
Contribution
It presents a novel, application-driven BILP formulation for task allocation that accounts for practical constraints often neglected in prior research.
Findings
The framework achieves optimal task allocation results.
It scales effectively for real-world and synthetic scenarios.
It enables efficient exploration of design options.
Abstract
With the advent of the Internet of Things (IoT), novel critical applications have emerged that leverage the edge/hub/cloud paradigm, which diverges from the conventional edge computing perspective. A growing number of such applications require a streamlined architecture for their effective execution, often comprising a single edge device with sensing capabilities, a single hub device (e.g., a laptop or smartphone) for managing and assisting the edge device, and a more computationally capable cloud server. Typical examples include the utilization of an unmanned aerial vehicle (UAV) for critical infrastructure inspection or a wearable biomedical device (e.g., a smartwatch) for remote patient monitoring. Task allocation in this streamlined architecture is particularly challenging, due to the computational, communication, and energy limitations of the devices at the network edge.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIoT and Edge/Fog Computing · UAV Applications and Optimization · Age of Information Optimization
