DOLPHIN: Dynamically Optimized and Load Balanced Path for Inter-Domain SDN Communication

Latif, Zohaib; Sharif, Kashif; Li, Fan; Karim, Md Monjurul; Biswas, Sujit; Shahzad, Madiha; Mohanty, Saraju P.

doi:10.1109/tnsm.2020.3045725

Cited by 13 publications

(6 citation statements)

References 35 publications

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“…Ref. [14] proposed an inter-domain communication system (DOLPHIN) which is a tailored solution for various SDN controllers. It expands the programmability of wireless devices, such as the Internet of Things or vehicle networks, beyond the virtual switch components in intra and interdomain communication.…”

Section: Related Workmentioning

confidence: 99%

A Fog-Cluster Based Load-Balancing Technique

et al. 2022

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The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

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Section: Related Workmentioning

confidence: 99%

A Fog-Cluster Based Load-Balancing Technique

et al. 2022

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“…Moufakir et al [35] proposed a novel collaborative multi-domain routing framework that can efficiently route incoming flows across multiple domains, as well as a QoS method that considers delay and bandwidth and has maximum overall network utilization. Latif et al [31] proposed a path-determining traffic engineering method for inter-domain traffic distribution in a multi-domain environment.…”

Section: Multi-domain Sdn Qosmentioning

confidence: 99%

“…We implemented a network simulator to conduct performance comparisons using Riverbed Modeler 18.9 (formerly OPNET Modeler) with Opendaylight 0.8.4 SDN controllers in order to verify the performance of the proposed method. We compared the proposed method (Proposed) with the Dijkstra algorithm without considering QoS (Dijkstra) and DOLPHIN, which supports QoS in a multi-domain SDN environment [31] (DOLPHIN). We constructed three network topologies using the Abilene, GEANT, and Synth200 topologies with a variable number of hosts per domain, as shown in Figure 6.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…To support end-to-end QoS for services passing through multiple domains, complex traffic engineering is required because the quality level that can be provided according to the resource conditions in these domains should be comprehensively considered [23]. Numerous studies have been conducted to support end-to-end QoS in multi-domain SDN networks [24][25][26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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QoS Support Path Selection for Inter-Domain Flows Using Effective Delay and Directed Acyclic Graph in Multi-Domain SDN

Lee

Roh

2022

Electronics

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Currently, network applications, such as audio, video, and augmented reality, have different stringent service requirements. They require service provision through end-to-end connections via other networks with different operating environments or service conditions. Therefore, network operators require information on their own and other networks to provide end-to-end services traversing several networks while guaranteeing their quality of service (QoS) requirements. This study proposes an inter-domain flow decision method that satisfies QoS requirements using a directed acyclic graph (DAG) in multi-domain and hierarchical software defined networking (SDN) networks. There are multiple local networks with SDN controllers that are connected to the global SDN controller. The flow decision in the proposed method is based on the effective bandwidth theory of the martingale process. The effectiveness of the proposed method is demonstrated by comparing it with existing SDN-based path selection methods using the Riverbed Modeler (older, OPNET) and OpenDaylight SDN controllers.

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“…For example, the switch matches the packet header with flow entries in the flow table when a new packet arrives and forwards the packet to the particular port if a rule is satisfied. Alternatively, the packet is either dropped or forwarded to the controller as a request to install a flow entry on the switch to transfer the packet [ 6 ]. Programmable networks have become an indispensable feature of most modern-day networks, including the Internet of things (IoT) and 5G networks [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks

Latif

Umer

Lee

et al. 2022

Sensors

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Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A software-based controller enforces rules and policies on the requests sent by forwarding elements; however, it cannot detect anomalous patterns in the network traffic. Due to this, the controller may install the flow rules against the anomalies, reducing the overall network performance. These anomalies may indicate threats to the network and decrease its performance and security. Machine learning (ML) approaches can identify such traffic flow patterns and predict the systems’ impending threats. We propose an ML-based service to predict traffic anomalies for software-defined networks in this work. We first create a large dataset for network traffic by modeling a programmable data center with a signature-based intrusion-detection system. The feature vectors are pre-processed and are constructed against each flow request by the forwarding element. Then, we input the feature vector of each request to a machine learning classifier for training to predict anomalies. Finally, we use the holdout cross-validation technique to evaluate the proposed approach. The evaluation results specify that the proposed approach is highly accurate. In contrast to baseline approaches (random prediction and zero rule), the performance improvement of the proposed approach in average accuracy, precision, recall, and f-measure is (54.14%, 65.30%, 81.63%, and 73.70%) and (4.61%, 11.13%, 9.45%, and 10.29%), respectively.

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DOLPHIN: Dynamically Optimized and Load Balanced Path for Inter-Domain SDN Communication

Cited by 13 publications

References 35 publications

A Fog-Cluster Based Load-Balancing Technique

A Fog-Cluster Based Load-Balancing Technique

QoS Support Path Selection for Inter-Domain Flows Using Effective Delay and Directed Acyclic Graph in Multi-Domain SDN

A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks

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