Sampling-based Smoothed Analysis for network algorithm evaluation

Accurate performance evaluation for network algorithms is vital to meet various requirements of different applications, such as QoS, network security, traffic engineering. Although worst-case and average-case analysis are widely used in algorithm evaluation, they are often insufficient due to the lack of practicality. Smoothed Analysis (SA) introduces a new concept of smoothed complexity, remedying the shortcomings in worst-case and average-case analysis. However, recent research towards SA focuses on theoretical evaluation, and thus those methods tend to be too complicated for the analysis of network algorithms. To address the problem, Sampling-based Smoothed Analysis (SSA) for network algorithm evaluation is proposed. SSA extends feasibility for practical performance evaluation and achieves promising experimental results. As examples, two algorithms for typical network problem are evaluated using the proposed SSA framework, and the results explicitly illustrate their significant performance difference in spite of the same theoretical worst-case complexity. Besides evaluation accuracy, SSA also provide more insight for algorithms to facilitate current algorithms improvement and new algorithms design.