SDNProbe: Lightweight Fault Localization in the Error-Prone Environment

Ke, Yu-Ming; Hsiao, Hsu-Chun; Kim, Tiffany Hyun-Jin

doi:10.1109/icdcs.2018.00055

Cited by 13 publications

(17 citation statements)

References 21 publications

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“…Path-based ATPG [3] 1 Active ( ) ( ) Monocle [5] Active ( ) × RuleScope [18] Active ( ) × RuleChecker [19] 2 Active ( ) × SDNProbe [50] Active ( ) ( ) FOCES [49] Passive ( ) ( ) VeriDP [20] Passive ( ) ( ) NetSight [21] Passive ( ) ( ) PathQuery [22] Passive ( ) ( ) CherryPick [23] 3 Passive ( ) ( ) PathDump [24] Active ( ) ( ) SDNTraceroute [25] Active ( ) ( ) TPP [51] 4 Active ( ) ( ) PAZZ Active, Passive presence of a fault on the data plane.…”

Section: Rule-basedmentioning

confidence: 99%

“…In the case of Type-p match faults ( §2.3.1) when the path is same even if different rule is matched, path trajectory tools [20][21][22][23][24][25]49] fail. The approaches based on active-probing [3,5,18,19,25,50,51] do not detect the Type-a faults ( §2.3.2) caused by hidden or misconfigured rules on the data plane which only match the fuzz traffic. These tools, however, only generate the probes to test the rules known or synced to the controller.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Toward Consistent SDNs: A Case for Network State Fuzzing

Shukla

Saidi

Schmid

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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The conventional wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Nevertheless, bugs, misconfigurations, faults or attacks can introduce inconsistencies that undermine correct operation. Previous work in this area, however, lacks a holistic methodology to tackle this problem and thus, addresses only certain parts of the problem. Yet, the consistency of the overall system is only as good as its least consistent part.Motivated by an analogy of network consistency checking with program testing, we propose to add active probe-based network state fuzzing to our consistency check repertoire. Hereby, our system, PAZZ, combines production traffic with active probes to continuously test if the actual forwarding path and decision elements (on the data plane) correspond to the expected ones (on the control plane). Our insight is that active traffic covers the inconsistency cases beyond the ones identified by passive traffic. PAZZ prototype was built and evaluated on topologies of varying scale and complexity. Our results show that PAZZ requires minimal network resources to detect persistent data plane faults through fuzzing and localize them quickly.

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Section: Rule-basedmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Toward Consistent SDNs: A Case for Network State Fuzzing

Shukla

Saidi

Schmid

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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show abstract

“…We opted to use a simple method for generating test cases that applies the Felix tool [12] and is talked about in Section 3 in more detail. We note that there are specific tools for the purpose of finding test paths in networks adhering to the SDN concept to achieve the best test coverage [13][14][15][16]. These could also be integrated with our framework to enhance test path generation capabilities, but this was not a goal for the current iteration of our work.…”

Section: Network Specificationmentioning

confidence: 99%

“…Many operations mentioned above depend on h, the number of tested host pairs, as the main contributing factor. Other works (e.g., [13][14][15][16]) that target test packet generation methods showed that analyzing packet forwarding behavior on the level of forwarding rules can help in reducing the space of required test packets. Since our framework uses the network's NetKAT specification, forwarding rules are readily available, and conversely, these methods can be used to reduce host pairs to test as well.…”

Section: Performancementioning

confidence: 99%

“…However, the number of such entries is less than the maximum degree of the network's graph which, in a usual case, adds only a small amount of new entries to flow tables. The number of test packets to send can be reduced by either applying the methods discussed in [13][14][15][16] or taking into consideration the non-test traffic between hosts. The framework can instruct Felix's measurement service to detect packets generated in either way.…”

Section: Performance Of a Single Testmentioning

confidence: 99%

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An Extensible Automated Failure Localization Framework Using NetKAT, Felix, and SDN Traceroute

Pelle

Gulyás

2019

Future Internet

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Designing, implementing, and maintaining network policies that protect from internal and external threats is a highly non-trivial task. Often, troubleshooting networks consisting of diverse entities realizing complex policies is even harder. Software-defined networking (SDN) enables networks to adapt to changing scenarios, which significantly lessens human effort required for constant manual modifications of device configurations. Troubleshooting benefits SDN’s method of accessing forwarding devices as well, since monitoring is made much easier via unified control channels. However, by making policy changes easier, the job of troubleshooting operators is made harder too: For humans, finding, analyzing, and fixing network issues becomes almost intractable. In this paper, we present a failure localization framework and its proof-of-concept prototype that helps in automating the investigation of network issues. Like a controller for troubleshooting tools, our framework integrates the formal specification (expected behavior) and network monitoring (actual behavior) and automatically gives hints about the location and type of network issues by comparing the two types of information. By using NetKAT (Kleene algebra with tests) for formal specification and Felix and SDN traceroute for network monitoring, we show that the integration of these tools in a single framework can significantly ease the network troubleshooting process.

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Audit‐based correction mechanism for malicious statistics information of data plane

et al. 2023

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Summary In software‐defined networking (SDN), the controller relies on the information collected from the data plane for route planning, load balancing, and other functions. Statistics information is the most important kind of information among them, so the correctness of statistics information is the key to the proper operation of the network. Most of the current research on data plane focuses on policy consistency, rule redundancy, forwarding anomalies, and so on, and little attention is paid to whether the statistics information uploaded by the switches to the controller is correct. However, incorrect statistics information inevitably leads the controller to make wrong decisions. Therefore, this paper proposes an audit‐based malicious information correction mechanism to address the problem of wrong statistics information uploaded by the switches. This mechanism audits the statistics information and locates malicious switches before uploading the statistics information to the controller. It identifies and corrects the statistics information errors by combining flow path and statistics information. We have performed simulations on Nsfnet, Abilene, and Fat‐Tree, and the results show that our method can correct about 70% of the statistical information errors with less computational cost. To the best of our knowledge, this paper is the first malicious statistics information correction scheme for wildcard rules.

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SDNProbe: Lightweight Fault Localization in the Error-Prone Environment

Cited by 13 publications

References 21 publications

Toward Consistent SDNs: A Case for Network State Fuzzing

Toward Consistent SDNs: A Case for Network State Fuzzing

An Extensible Automated Failure Localization Framework Using NetKAT, Felix, and SDN Traceroute

Audit‐based correction mechanism for malicious statistics information of data plane

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