Task Scheduling in Geo-Distributed Computing: A Survey

2025-01-26

AI Summary

This paper addresses the task scheduling challenge in geo-distributed computing, arising from network heterogeneity, heterogeneous resource pricing, and imbalanced computational capacity. It systematically surveys scheduling techniques across four paradigms: cloud, edge, cloud–edge collaboration, and high-performance computing (HPC). The study introduces the first unified taxonomy covering all four environments, grounded in three core objectives—performance, fairness, and fault tolerance—and identifies cross-cutting challenges including cross-domain latency-sensitive scheduling, multi-regional cost optimization, and elastic fault tolerance. Through bibliometric analysis and qualitative comparative evaluation, it classifies and assesses state-of-the-art approaches—including multi-objective optimization, game-theoretic models, reinforcement learning, and heuristic algorithms. The work traces the technical evolution of scheduling research and proposes six future directions: AI-native schedulers, carbon-aware scheduling, among others—thereby providing theoretical foundations and practical guidance for building adaptive, sustainable distributed scheduling systems.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• Task Scheduling
• Geographically Distributed Computing
• Resource Optimization

Innovation

• Distributed Computing
• Task Scheduling Optimization
• Heterogeneity Handling