A Survey of Computation Offloading with Task Types
Siqi Zhang, Na Yi, Yi Ma
TL;DR
This survey classifies computation offloading research by task type, distinguishing static and dynamic tasks, and further by full versus partial offloading to reveal how data generation shapes optimization objectives and resource allocation. It provides a comprehensive view of the offloading process, from task generation to decision making, and highlights key directions such as task splitting, redundancy removal, caching, energy harvesting, and AI-driven decision making within MEC/fog/cloud ecosystems. The analysis covers both static-task and dynamic-task literature, emphasizing that static tasks favor simpler allocations while dynamic tasks demand queuing, reliability, and data-aware strategies, and it discusses gaps in data-centric modeling and the potential of THz sensing–communication co-design for future offloading systems.
Abstract
Computation task offloading plays a crucial role in facilitating computation-intensive applications and edge intelligence, particularly in response to the explosive growth of massive data generation. Various enabling techniques, wireless technologies and mechanisms have already been proposed for task offloading, primarily aimed at improving the quality of services (QoS) for users. While there exists an extensive body of literature on this topic, exploring computation offloading from the standpoint of task types has been relatively underrepresented. This motivates our survey, which seeks to classify the state-of-the-art (SoTA) from the task type point-of-view. To achieve this, a thorough literature review is conducted to reveal the SoTA from various aspects, including architecture, objective, offloading strategy, and task types, with the consideration of task generation. It has been observed that task types are associated with data and have an impact on the offloading process, including elements like resource allocation and task assignment. Building upon this insight, computation offloading is categorized into two groups based on task types: static task-based offloading and dynamic task-based offloading. Finally, a prospective view of the challenges and opportunities in the field of future computation offloading is presented.