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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
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Multiple Granularity Online Control of Cloudlet Networks for Edge Computing
Operating distributed cloudlets at optimal cost is nontrivial when facing not only the dynamic and unpredictable resource prices and user requests, but also the low efficiency of today's immature cloudlet infrastructures. We propose to control cloudlet networks at multiple granularities - fine-grained control of servers inside cloudlets and coarse-grained control of cloudlets themselves. We model this problem as a mixed-integer nonlinear program with the switching cost over time. To solve this problem online, we firstly linearize, "regularize", and decouple it into a series of one-shot subproblems that we solve at each corresponding time slot, and afterwards we design an iterative, dependent rounding framework using our proposed randomized pairwise rounding algorithm to convert the fractional control decisions into the integral ones at each time slot. Via rigorous theoretical analysis, we exhibit our approach's performance guarantee in terms of the competitive ratio and the multiplicative integrality gap towards the offline optimal integral decisions. Extensive evaluations with real-world data confirm the empirical superiority of our approach over the single granularity server control and the state-of-the-art algorithms.
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- Award ID(s):
- 1703014
- PAR ID:
- 10088814
- Date Published:
- Journal Name:
- 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SAHCN.2018.8397141
