Scheduling of Distributed Applications on the Computing Continuum: A Survey

TL;DR

This survey reviews scheduling methods for distributed applications across Cloud, Fog, and Edge devices, focusing on resource allocation challenges, conflicting objectives, and evaluation techniques in the heterogeneous computing continuum.

Contribution

It provides a comprehensive overview of existing scheduling techniques, analyzing their objectives, methods, and evaluation tools in the context of the Computing Continuum.

Findings

Most techniques address conflicting objectives like time, energy, and cost.

Heterogeneous devices pose NP-hard scheduling challenges.

Evaluation methods vary across different research works.

Abstract

The demand for distributed applications has significantly increased over the past decade, with improvements in machine learning techniques fueling this growth. These applications predominantly utilize Cloud data centers for high-performance computing and Fog and Edge devices for low-latency communication for small-size machine learning model training and inference. The challenge of executing applications with different requirements on heterogeneous devices requires effective methods for solving NP-hard resource allocation and application scheduling problems. The state-of-the-art techniques primarily investigate conflicting objectives, such as the completion time, energy consumption, and economic cost of application execution on the Cloud, Fog, and Edge computing infrastructure. Therefore, in this work, we review these research works considering their objectives, methods, and evaluation tools. Based on the review, we provide a discussion on the scheduling methods in the Computing Continuum.