Online and Predictive Coordinated Cloud-Edge Scrubbing for DDoS Mitigation

To mitigate Distributed Denial-of-Service (DDoS) attacks towards enterprise networks, we study the problem of scheduling DDoS traffic through on-premises scrubbing at the local edge and on-demand scrubbing in the remote clouds. We model this problem as a nonlinear mixed- integer program, which is characterized by the inputs of arbitrary dynamics and the trade-offs between staying at suboptimal scrubbing locations and using different best locations with switching overhead. We first design a prediction-oblivious online algorithm which consists of a carefully-designed fractional algorithm to pursue the long-term total cost minimization but avoid excessive switching overhead over time, and a randomized rounding algorithm to derive the flow-based, integral decisions. We next design a prediction-aware online algorithm which leverages the predicted inputs and can make even better scheduling decisions through invoking our prediction-oblivious online algorithm and improving its solutions via re-solving the original problem slice over each prediction window. We further extend our study to prioritize local scrubbing, and adapt our algorithms to this case correspondingly. Then, we rigorously prove the worst-case, constant competitive performance guarantees of our online algorithms. Finally, we conduct extensive evaluations and validate the superiority of our approach over multiple existing alternatives approaches.