概要

• 在sdn下，控制平面基于網絡測量的的數據控制網絡，而細粒度的管理得益于細粒度的測量數據。針對sdn環境下的細粒度測量（識別具體應用程序），可以實現對細粒度的流量管控。
• 設計了識別系統SBAR，對數據流進行測量，識別出具體應用程序并得到它們的網絡數據。針對流分類，SBAR以應用程序協議為參照，使用了機器學習的算法。 針對web和加密流量，使用深度包檢測檢測包的負載以識別應用程序。同時SBAR利用了OpenFlow提供的測量數據識別應用，交換機利用南向接口向控制器傳輸數據。最后使用GUI對得到的數據進行整合處理得到全網測量數據。經過驗證，SBAR檢測的準確率可以達到90%以上（由于使用DPI和ML可能有較大的資源開銷和時延）。

Background

• In the Software-Defined Networking (SDN) paradigm, it is essential to perform comprehensive traffic monitoring in order to provide the control plane with an accurate view of the network state.
• This enables to perform such an effective fine-grained network management
  with different purposes (e.g., traffic engineering, security).

Related Work

• NetFlow/IPFIX:There are a plenty of tools based on Netflow that harness the flow-level measurement.
• Flows are often labeled (e.g., by protocol)
  using port-based classification techniques which is gradually obsoleted beacause it is quite common to find very diverse applications operating over
  the same application protocols（無法從端口號分辨出具體的應用程序）
• QoE:the QoE perceived by end-users significantly depends on the type of application and the QoS level provided by the network (e.g., bandwidth, delay).（QoE感知取決于應用程序類型以及QoS級別）
• Deep Packet Inspection (DPI)
  typically achieves very accurate traffic classification by inspecting the packet payloads. However, applying DPI over all the packets traversing a network is often too resource consuming （根據負載分類，資源開銷過大）
• Machine Learning (ML) classifiers were proposed with the aim of alleviating the
  processing burden.Use **features* up to the transport layer to classify the traffic, useless when applied to distinguish among different applications generating traffic over the same protocol（根據特征分類，無法從相同協議分辨出不同程序）

Solution

• We present SBAR, a monitoring system compliant with OpenFlow that provides flow-level measurement
• Classify the traffic at two different levels:In the Software-Defined Networking (SDN) paradigm, it is essential to perform comprehensive traffic monitoring in order to provide the control plane with an accurate view of the network state. This
  enables to perform such an effective fine-grained network management with different purposes (e.g., traffic engineering, security).
• (i) every monitored flow is classified by application protocol,

• (ii) for web and encrypted traffic, we apply specific DPI techniques to identify the applications （端口號相同要通過負載分辨出具體的應用程序）
  generating each flow
  

• Reduce the processing overhead in the controller(s) and the memory consumption in switches to maintain the measurements

Implement

Openflow

• Leverage the particularities of OpenFlow networks to efficiently implement a combination of techniques based on ML and DPI to accurately classify the traffic in the controller.
• Leverage the support of OpenFlow to maintain the flow measurements (# of
  packets and bytes, and duration) in the flow tables of the switches
• OpenFlow provides an interface that permits to report the measurements to the controller(s) when some predefined timeouts (idle and hard) expire
• Make use of ultiple tables of OpenFlow
  to decouple the operation of this module from other modules executing
  different network tasks (e.g., forwarding) in the controller.

Others

• Flow sampling using only native features of OpenFlow, which enables to address
  common scalability issues in OpenFlow-based networks.

Per-flow classification by application protocols (e.g., SMTP, SSH) using a ML model

For web and encrypted flows, it applies specific DPI techniques [1, 2] to identify the applications (e.g., Netflix, Facebook) generating traffic.

• 通過給控制器指定規則，只提取前幾個HTTP等協議的包頭信息，然后根據某種算法推斷出對應的應用程序，節省了開銷
• GUI用于處理SBAR得到的flow-level reports infer high-level traffic
  statistics

Advantages

the classification accuracy acheives 90% or higher.

Disadvantages

Because of the DPI and ML, the resource overhead and latency probably are high(I guess)

轉載于:https://www.cnblogs.com/vancasola/p/9435975.html

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