Computation Offloading in Collaborative Computing Systems: A Comprehensive Survey on Methods, Challenges, and Future Directions

• Complex offloading dynamics are rarely considered in offloading problems • The potential of collaborative systems is not fully leveraged by offloading methods • Suitable methods for solving offloading problems depend on system complexity • Offloading methods are mainly evaluated using study-specific simulation environments • Studies lack cross-validation across methods, networks, and configurations The rapid increase in the number of connected devices has created a vast computational infrastructure. While intelligent applications (e.g., VR/AR, autonomous driving, and AI assistants) demand substantial processing power, they are typically executed only in short bursts. As a result, devices remain idle for significant periods, presenting an opportunity to utilize their unused computational resources. Computation offloading has emerged as a viable solution, where resource-constrained devices leverage edge or cloud infrastructure for intensive tasks. More recently, horizontal offloading has gained attention as a complementary approach, enabling devices within the same layer to collaborate and utilize idle resources more efficiently. In this survey, we examine current research on computation offloading in collaborative computing systems that span from horizontal offloading among end devices to more complex systems involving horizontal as well as vertical offloading to edge/cloud systems. Our examination focuses on architectures, collaborative characteristics, and offloading dynamics, such as task dependency, mobility, and multi-hop offloading. We begin by introducing edge computing paradigms and key concepts in computation offloading and collaborative computing. The offloading approaches are then classified into classical, heuristic, metaheuristic, and machine learning methods, further divided into centralized, decentralized, and distributed categories. These approaches are analyzed and compared based on collaborative system, architecture, offloading dynamics, and evaluation methodology. Finally, we have identified open challenges and emphasized the need to unify research across edge computing paradigms, alongside a focus on more complex collaborative systems that resemble real-world systems, and the development of standardized evaluation frameworks as key directions for future work.