Analysis of data center SDN controller architectures: Technology and business impacts

Krishnan, Ram; Figueira, Norival

doi:10.1109/iccnc.2015.7069324

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…A controller remains as a potential high value target of the attackers because of multiple reasons: firstly, it is the "brain" [5] of an SDN which is logically centralized by design, secondly, there are multiple interfaces that are open to external application usage which an intruder can exploit and gain control over the network.…”

Section: Introductionmentioning

confidence: 99%

Security analysis of OpenDaylight, ONOS, Rosemary and Ryu SDN controllers

Arbettu

Khondoker

Bayarou

et al. 2016

2016 17th International Telecommunications Network Strategy and Planning Symposium (Networks)

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There is an immense expectation on Software-Defined Networking (SDN) in industry as a novel approach towards potentially replacing conventional network management and control. However, SDN is not immune to security vulnerabilities which currently exist in the legacy systems or which may newly arise due to change in the network design. Since the beginning of SDN development, primary focus of research was on separation of control plane from data plane by keeping performance and operational flexibility unchanged. In the due course of achieving this, security aspects of an SDN have taken a back seat. Even though separation of control plane from a data plane is a great step towards simplification of network management, it subjects the network into a potential two way target for intruders to gain control. Due to the centralized design of SDN, compromising security of a controller will be as good as compromising the security of a whole network. Enterprises which are moving towards adapting SDN are concerned about security issues and the resulting problems. In this paper, we analyze the security issues of few of the widely used controllers. We found that the OpenDayLight controller is the most secure one compared to the others. In addition, this paper also provides a snapshot of current development in security aspect of SDN controllers such that it may help SDN controller developers to identify the issues and rectify the same in future releases.

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Section: Introductionmentioning

confidence: 99%

Security analysis of OpenDaylight, ONOS, Rosemary and Ryu SDN controllers

Arbettu

Khondoker

Bayarou

et al. 2016

2016 17th International Telecommunications Network Strategy and Planning Symposium (Networks)

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“…Before discussing the algorithm procedure, we prefer describe the representations of RL daemons with respect to Qlearning algorithm for our approach as follows, State space: The state space, 'S' in our approach represents nodes, controller load and utilized data rate over edges. We indicate the parameters for state space as shown in expression (1),…”

Section: Intelligent Routing Module (Ir)mentioning

confidence: 99%

“…The SDN implementation over DCN facilitates major advantages like simplifying network management, flexible network reconfiguration, network scaling and increased effective network resource distribution among the available devices in the network [1]. The Software defined network provisions controller, a software running in server node which shoulders the responsibility of programming the data plane devices in a centralized manner.…”

Section: Introductionmentioning

confidence: 99%

Design of intelligent traffic load balancing (ITLB) mechanism for software defined data center (SDDC)

Balakiruthiga

Deepalakshmi

2022

Preprint

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The reinforcement learning strategy is adopted for software defined data centers, since it involves software agents to accomplish decision making through continuous learning of environment during drastic traffic. In this research work, we are proposing an intelligent routing algorithm by considering controller capacity and link capacity as the routing metrics. In addition, our approach takes care of migrating switches whenever a controller is over utilized. Distinct reinforcement learning agents are used to handle path computation and switch migration phase individually. Our key objective is to provide intelligent traffic load balancing approach in software defined data center using two distinct software agents. We observe that our proposed approach has achieved nearly 1.2 to 2.1 milliseconds of less latency, 2.5 milliseconds of less response time and low packet loss percentage around 1 to 2.5% and hence overall network throughput improvement is about attained 30 to 50% than other existing approaches through simulation.

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“…Software defined network provides separation of control plane and data plane which promotes operation simplification and cost reduction (Krishnan and Figueira, 2015). The Cisco ACI VXLAN header provides a tagging mechanism to identify properties that are associated with frames forwarded through the Cisco ACI fabric.…”

Section: Section 1 Underlay and Overlay Control Plane And Data Planementioning

confidence: 99%

Implementing application centric infrastructure to build a scalable secure data center

Xi¹

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Analysis of data center SDN controller architectures: Technology and business impacts

Cited by 8 publications

References 9 publications

Security analysis of OpenDaylight, ONOS, Rosemary and Ryu SDN controllers

Security analysis of OpenDaylight, ONOS, Rosemary and Ryu SDN controllers

Design of intelligent traffic load balancing (ITLB) mechanism for software defined data center (SDDC)

Implementing application centric infrastructure to build a scalable secure data center

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