Design of SDN-Enabled Cloud Data Center

Hwang, Ren-Hung; Tseng, Huei-Ping; Tang, Y.

doi:10.1109/smartcity.2015.193

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…In [29], Cloud Sim SDN is a simulation of hosts and networks, and requests latency usage in SDN-enabled cloud data centers. Cloud Sim SDN supports computing power which both hosts and switches consume.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Using metaheuristics to improve the placement of multi-controllers in software-defined networking enabled clouds

Radam¹,

Al-Janabi²,

Jasim³

2022

PEN

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SDN is a model that separates the control and the data levels in an arrangement to enhance capability to program and configure the network in a more agile and efficient manner. Multiple controller modules have been used in the SDN engineering to empower programmable and adaptable configurations such as improving scalability and reliability. The distance and time calculations and other performance measures have to be considered in solving the Multi-Controller Position Problem (MCPP). This paper investigates the use of metaheuristic algorithms to build an MCPP mathematical model. Both the symmetric Harmony Search (HS) modelling and the Particle Swarm Optimization (PSO) algorithm are considered in this respect. Thus, our hybrid approach is proposed and known as Harmony Search with Particle Swarm Optimization (HSPSO) is applied and we compared the extracted results with the state-of-the-art techniques in the previous literature. Besides the development of the mathematical model, a simulation study has been done considering the relevant parameters including the link distance description and the access time between the SDN entities. The console simulation uses NetBeans with CloudsimSDN procedure files in the SDN-based cloud environment.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Using metaheuristics to improve the placement of multi-controllers in software-defined networking enabled clouds

Radam¹,

Al-Janabi²,

Jasim³

2022

PEN

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“…The simulation model implements a network topology considering host-to-host data flow transfers between two or more SDN-enabled distributed data centers. For the scope of our research, the experimental setup considers a small-medium network topology based on three-layer for data centers [32]. This topology consists of the access layer to connect hosts (compute nodes), an aggregation or region layer in the middle to connect the access switches and a core layer in the root of the tree.…”

Section: Simulation Modelmentioning

confidence: 99%

QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics

et al. 2020

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When Internet of Things (IoT) big data analytics (BDA) require to transfer data streams among software defined network (SDN)-based distributed data centers, the data flow forwarding in the communication network is typically done by an SDN controller using a traditional shortest path algorithm or just considering bandwidth requirements by the applications. In BDA, this scheme could affect their performance resulting in a longer job completion time because additional metrics were not considered, such as end-to-end delay, jitter, and packet loss rate in the data transfer path. These metrics are quality of service (QoS) parameters in the communication network. This research proposes a solution called QoSComm, an SDN strategy to allocate QoS-based data flows for BDA running across distributed data centers to minimize their job completion time. QoSComm operates in two phases: (i) based on the current communication network conditions, it calculates the feasible paths for each data center using a multi-objective optimization method; (ii) it distributes the resultant paths among data centers configuring their openflow Switches (OFS) dynamically. Simulation results show that QoSComm can improve BDA job completion time by an average of 18%.

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“…In addition, the large scale of the clouds itself, the advent of mobile devices with direct access to cloud infrastructure amplify cloud vulnerabilities and threats. As a cloud computing is more extended and its utilization increases, it becomes prone to network infrastructure related potential and successful and potential attacks such as Distributed Denial of Service, hacking, stealing sensitive information, performing malicious code execution and compromising vulnerable virtual machines (VMs) which can happen in a high possibility in cloud compared to the traditional computing [7,8].…”

Section: Introductionmentioning

confidence: 99%

Moving Target Defense for SDN-Based Cloud Datacenter Network Protection

2021

IJATCSE

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The significant advance of software Defined Networking (SDN) technology has enabled several complex system operations to be highly dynamic, flexible and robust; particularly in terms of programmability and controllability with the help of SDN controllers. Accordingly, many security operations have utilized this capability to be optimally deployed in a complex network using the SDN functionalities. Moving target defense (MTD) has emerged as an adaptive and proactive defense mechanism aiming to thwart a potential attacker. The key underlying idea of MTD is to increase uncertainty and confusion for attackers by changing attack surface (i.e., system or network configurations) that can invalidate the intelligence collected by the attackers and interrupt attack execution; ultimately leading to attack failure. In this research, by leveraging the advanced SDN technology, the model of MTD using SDN-based system framework design is proposed. The model uses a runtime model that allows the proposed framework to infer the current state of the system. Based on the obtained information, the MTD mechanism using SDN can provide proactive, adaptive and affordable defense services for the exploitable aspects of the cloud datacenter network to increase uncertainty and complexityto the attackers and reduce the likelihood of an attack and minimize cloud security risk. The research also validates the outperformance of the proposed MTD technique in attack success rate via simulation on SDN-based cloud datacenter network experiments in a virtualized environment

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Design of SDN-Enabled Cloud Data Center

Cited by 8 publications

References 9 publications

Using metaheuristics to improve the placement of multi-controllers in software-defined networking enabled clouds

Using metaheuristics to improve the placement of multi-controllers in software-defined networking enabled clouds

QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics

Moving Target Defense for SDN-Based Cloud Datacenter Network Protection

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