Synergistic task-offloading in 6G edge networks based on propagation dynamics

In future 6G edge networks, Device-to-Device (D2D)-assisted Mobile Edge Computing (MEC) can fully utilize the idle resources of user terminals (UT) and alleviate the burden on backhaul links. However, the limited idle resources of UT and the over-reliance on D2D-assisted computation offloading may result in a large number of terminals experiencing task overload, which could lead to the risk of edge network paralysis. To address these issues, this paper establishes a Service-Auxiliary-Request-Healing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mi>S</mml:mi><mml:mi>A</mml:mi><mml:mi>R</mml:mi><mml:mi>H</mml:mi></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math> task-offloading propagation model based on propagation dynamics theory. This model describes the dynamic transmission process of offloaded tasks in 6G edge networks and constructs two linear threshold functions to characterize the differences in task processing capabilities between UT and edge servers (ES). Furthermore, the proposed task-offloading propagation model is theoretically analyzed using edge compartment theory, and the propagation dynamics equations are established to derive the saddle point and critical conditions leading to task overload in a large number of UT, providing theoretical guidance for preventing network paralysis. Finally, simulation results show that the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:mi>S</mml:mi><mml:mi>A</mml:mi><mml:mi>R</mml:mi><mml:mi>H</mml:mi></mml:mrow></mml:math> model effectively describes the task-offloading propagation process in edge networks, and by controlling key factors such as the proportion of UT selecting D2D-assisted MEC synergistic task-offloading, network connectivity density, and network degree distribution heterogeneity, network paralysis can be avoided.