Multilayer networked systems are ubiquitous in nature and engineering, and the robustness of these systems against failures is of great interest. A main line of theoretical pursuit has been percolation induced cascading failures, where interdependence between network layers is conveniently and tacitly assumed to be symmetric. In the real world, interdependent interactions are generally asymmetric. To uncover and quantify the impact of asymmetry in interdependence on network robustness, we focus on percolation dynamics in double-layer systems and implement the following failure mechanism: once a node in a network layer fails, the damage it can cause depends not only on its position in the layer but also on the position of its counterpart neighbor in the other layer. We find that the characteristics of the percolation transition depend on the degree of asymmetry, where the striking phenomenon of a switch in the nature of the phase transition from first-to second-order arises. We derive a theory to calculate the percolation transition points in both network layers, as well as the transition switching point, with strong numerical support from synthetic and empirical networks. Not only does our work shed light upon the factors that determine the robustness of multilayer networks against cascading failures, but it also provides a scenario by which the system can be designed or controlled to reach a desirable level of resilience. *

To develop effective control strategies to enhance the robustness of multilayer networks against largescale failures is of significant value. We articulate the idea of 'remote control' whereby adaptive perturbations to one network layer are able to enhance the resilience of not only itself but also other interconnected network layers. We analyze the principle of remote control using percolation dynamics by showing analytically and numerically that, with the adaptive generation of a small number of new links in the control layer, not only is this layer but also other layers become dramatically more resistant to cascading failures. We also find that remote control is more effective for scale-free than for random networks. Remote intervention of multilayer network systems through adaptation has real-world applications, which we illustrate using the rail and coach transportation system in the Great Britain.