大规模网络近优监控的快速近似算法
Fast Approximation Algorithms for Near-optimal Large-scale Network Monitoring
摘要：
我们研究了大规模网络中的最优流量预测和监控问题。我们的目标是确定K个链路的哪个子集进行监控，以便“最好”预测网络中剩余链路上的流量。我们考虑几个最优性准则。这可以用公式表示为属于子集选择问题族的组合优化问题。在统计学、机器学习和信号处理中也出现了类似的NP难题。一些包括回归的子集选择，变量选择和稀疏近似。精确解在计算上是不允许的。我们提出了新的算法，以及新的高效算法实现经典的贪婪启发式-通常用于解决这样的组合问题。我们的方法利用连接到主成分分析（PCA），并产生新类型的性能下限，不需要子模块的目标函数。我们表明，集成方法应用到我们的新的随机启发式算法，往往优于经典的贪婪启发式在实践中。我们在几个大规模的网络，包括现实生活中的网络，我们的算法进行评估。
英文摘要：
We study the problem of optimal traffic prediction and monitoring in large-scale networks. Our goal is to determine which subset of K links to monitor in order to "best" predict the traffic on the remaining links in the network. We consider several optimality criteria. This can be formulated as a combinatorial optimization problem, belonging to the family of subset selection problems. Similar NP-hard problems arise in statistics, machine learning and signal processing. Some include subset selection for regression, variable selection, and sparse approximation. Exact solutions are computationally prohibitive. We present both new heuristics as well as new efficient algorithms implementing the classical greedy heuristic - commonly used to tackle such combinatorial problems. Our approach exploits connections to principal component analysis (PCA), and yields new types of performance lower bounds which do not require submodularity of the objective functions. We show that an ensemble method applied to our new randomized heuristic algorithm, often outperforms the classical greedy heuristic in practice. We evaluate our algorithms under several large-scale networks, including real life networks.
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