Task Scheduling in Geo-Distributed Computing: A Survey

TL;DR

This survey reviews task scheduling techniques in geo-distributed computing, highlighting challenges, objectives, and future research directions across various distributed environments.

Contribution

It provides a comprehensive systematic review and analysis of existing task scheduling methods tailored for geo-distributed computing systems.

Findings

Identifies key challenges like heterogeneity and network variability.

Classifies scheduling approaches based on core objectives.

Outlines promising future research directions.

Abstract

Geo-distributed computing, a paradigm that assigns computational tasks to globally distributed nodes, has emerged as a promising approach in cloud computing, edge computing, cloud-edge computing and supercomputer computing (HPC). It enables low-latency services, ensures data locality, and handles large-scale applications. As global computing capacity and task demands increase rapidly, scheduling tasks for efficient execution in geo-distributed computing systems has become an increasingly critical research challenge. It arises from the inherent characteristics of geographic distribution, including heterogeneous network conditions, region-specific resource pricing, and varying computational capabilities across locations. Researchers have developed diverse task scheduling methods tailored to geo-distributed scenarios, aiming to achieve objectives such as performance enhancement, fairness assurance, and fault-tolerance improvement. This survey provides a comprehensive and systematic review of task scheduling techniques across four major distributed computing environments, with an in-depth analysis of these approaches based on their core scheduling objectives. Through our analysis, we identify key research challenges and outline promising directions for advancing task scheduling in geo-distributed computing.