A reliability- and latency-driven task allocation framework for workflow applications in the edge-hub-cloud continuum

TL;DR

This paper introduces an exact multi-objective task allocation framework for workflow applications in the edge-hub-cloud system, optimizing reliability and latency simultaneously with proven effectiveness and scalability.

Contribution

It presents a comprehensive binary integer linear programming model that jointly optimizes reliability and latency, considering time redundancy and real-world constraints in the edge-hub-cloud architecture.

Findings

Achieved 84.19% average reliability improvement

Reduced latency by 49.81% on average

Demonstrated scalability with runtimes up to 50.94 seconds

Abstract

A growing number of critical workflow applications leverage a streamlined edge-hub-cloud architecture, which diverges from the conventional edge computing paradigm. An edge device, in collaboration with a hub device and a cloud server, often suffices for their reliable and efficient execution. However, task allocation in this streamlined architecture is challenging due to device limitations and diverse operating conditions. Given the inherent criticality of such workflow applications, where reliability and latency are vital yet conflicting objectives, an exact task allocation approach is typically required to ensure optimal solutions. As no existing method holistically addresses these issues, we propose an exact multi-objective task allocation framework to jointly optimize the overall reliability and latency of a workflow application in the specific edge-hub-cloud architecture. We present a comprehensive binary integer linear programming formulation that considers the relative importance of each objective. It incorporates time redundancy techniques, while accounting for crucial constraints often overlooked in related studies. We evaluate our approach using a relevant real-world workflow application, as well as synthetic workflows varying in structure, size, and criticality. In the real-world application, our method achieved average improvements of 84.19% in reliability and 49.81% in latency over baseline strategies, across relevant objective trade-offs. Overall, the experimental results demonstrate the effectiveness and scalability of our approach across diverse workflow applications for the considered system architecture, highlighting its practicality with runtimes averaging between 0.03 and 50.94 seconds across all examined workflows.