Online Scheduling of Traffic Diversion and Cloud Scrubbing with Uncertainty in Current Inputs
Online Scheduling of Traffic Diversion and Cloud Scrubbing with Uncertainty in Current Inputs
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DOI：
10.1145/3323679.3326525
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Lei Jiao;Ruiting Zhou;Xiaojun Lin;Xu Chen
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文献类型：
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作者：
Lei Jiao;Ruiting Zhou;Xiaojun Lin;Xu Chen
在大规模网络中运行分布式清洗中心（SC）以缓解大规模分布式拒绝服务（DDoS）流量面临着严峻的挑战。运营商需要确定网络中的转移规则安装和消除，以及SC中的清洗资源激活和撤销，同时在不知道恶意流量的确切数量的情况下最小化长期成本和累积决策切换惩罚。我们的模型和制定这个问题作为一个在线的非线性整数规划。与许多其他在线问题相比，未来的输入是未知的，但至少当前的输入是已知的，这里的一个关键的新挑战是，即使是部分当前的输入是未知的，当决策。为了在线“学习”最佳决策，我们通过间隙保持近似将我们的问题转化为仅具有已知输入的在线优化问题，该问题进一步放松并解耦为一系列可在各个时隙中求解的一次性凸规划。为了克服这一困难，我们设计了一个渐进舍入算法，在不违反约束的情况下将分数判决转换为整数判决。我们的竞争力的比例，我们的方法作为我们的问题的关键参数的函数。我们使用真实世界的数据进行评估，并确认我们的算法优于事实上的做法和最先进的方法。
Operating distributed Scrubbing Centers (SCs) to mitigate massive Distributed Denial of Service (DDoS) traffic in large-scale networks faces critical challenges. The operator needs to determine the diversion rule installation and elimination in the networks, as well as the scrubbing resource activation and revocation in the SCs, while minimizing the long-term cost and the cumulative decision-switching penalty without knowing the exact amount of the malicious traffic. We model and formulate this problem as an online nonlinear integer program. In contrast to many other online problems where future inputs are unknown but at least current inputs are known, a key new challenge here is that even part of the current inputs are unknown when decisions are made. To "learn" the best decisions online, we transform our problem via a gap-preserving approximation into an online optimization problem with only the known inputs, which is further relaxed and decoupled into a series of one-shot convex programs solvable in individual time slots. To overcome the intractability, we design a progressive rounding algorithm to convert fractional decisions into integral ones without violating the constraints. We characterize the competitive ratio of our approach as a function of the key parameters of our problem. We conduct evaluations using real-world data and confirm our algorithms' superiority over de facto practices and state-of-the-art methods.