Name
Abstract | ||
|---|---|---|
Today’s data center operators deploy network policies in both physical (e.g., middleboxes, switches) and virtualized (e.g., virtual machines on general purpose servers) network function boxes (NFBs), which reside in different points of the network, to exploit their efficiency and agility respectively. Nevertheless, such heterogeneity has resulted in a great number of independent network nodes that can dynamically generate and implement inconsistent and conflicting network policies, making correct policy implementation a difficult problem to solve. Since these nodes have varying capabilities, services running atop are also faced with profound performance unpredictability. In this paper, we propose a
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eterogeneous netw
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rk
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olicy
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nforcement (HOPE) scheme to overcome these challenges. HOPE guarantees that network functions (NFs) that implement a policy chain are optimally placed onto heterogeneous NFBs such that the network cost of the policy is minimized. We first experimentally demonstrate that the processing capacity of NFBs is the dominant performance factor. This observation is then used to formulate the Heterogeneous Network Policy Placement problem, which is shown to be
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. To solve the problem efficiently, an online algorithm is proposed. Our experimental results demonstrate that HOPE achieves the same optimality as
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optimization but is 3 orders of magnitude more efficient. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TPDS.2018.2871845 | IEEE Transactions on Parallel and Distributed Systems |
Keywords | Field | DocType |
Feedback amplifiers,Servers,Random access memory,Hardware,Middleboxes,Noise measurement,Heterogeneous networks | Online algorithm,Chaining,Virtual machine,Network security policy,Computer science,Server,Node (networking),Heterogeneous network,Data center,Distributed computing | Journal |
Volume | Issue | ISSN |
30 | 4 | 1045-9219 |
Citations | PageRank | References |
3 | 0.40 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Cui | 1 | 66 | 8.35 |
| Fung Po Tso | 2 | 212 | 22.16 |
| Song Guo | 3 | 3431 | 278.71 |
| Weijia Jia | 4 | 2656 | 221.35 |
| Kaimin Wei | 5 | 128 | 7.81 |
| Wei Zhao | 6 | 3532 | 404.01 |