A New Processing Approach for Scheduling Time Minimization in 5G-IoT Networks

Mahmood, Ali Majeed; Kareem, Azad R.

doi:10.22266/ijies2021.0630.40

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…Part of the driver's work is to analyze, distribute and schedule work across the executors. At the same time, the executors are responsible for carrying out the work assigned to them by the driver [17].…”

Section: Sparkmentioning

confidence: 99%

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

Samaan

Jeiad

2023

IJ-AI

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Precise traffic classification is essential to numerous network functionalities such as routing, network management, and resource allocation. Traditional classification techniques became insufficient due to the massive growth of network traffic that requires high computational costs. The arising model of software defined networking (SDN) has adjusted the network architecture to get a centralized controller that preserves a global view over the entire network. This paper proposes a model for SDN traffic classification based on machine learning (ML) using the Spark framework. The proposed model consists of two phases; learning and deployment. A ML pipeline is constructed in the learning phase, consisting of a set of stages combined as a single entity. Three ML models are built and evaluated; decision tree, random forest, and logistic regression, for classifying a well-known 75 applications, including Google and YouTube, accurately and in a short time scale. A dataset consisting of 3,577,296 flows with 87 features is used for training and testing the models. The decision tree model is elected for deployment according to the performance results, which indicate that it has the best accuracy with 0.98. The performance of the proposed model is compared with the state-of-the-art works, and better accuracy result is reported.

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Section: Sparkmentioning

confidence: 99%

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

Samaan

Jeiad

2023

IJ-AI

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“…In [22], a joint scheduling problem of eMBB and uRLLC traffic is addressed for the purpose of achieving service improvement for eMBB users while satisfying random demand on URLC UEs. For uRLLC and eMBB services, a non-orthogonal coexistence scheme was proposed in [23,24] by having the processing of uRLLC traffic performed independently by users, while the control of eMBB traffic is implemented centrally in the network considering the uplink and downlink. However, the aforementioned studies were only limited to using one licensed spectrum as a single resource.…”

Section: Related Workmentioning

confidence: 99%

Radio Control Optimization for Enhanced QoS-based Spectrum Selection in Heterogeneous 5G Environments with eMBB and uRLLC

2023

IJIES

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5G mobile networks provide two main uses: enhanced mobile broadband (eMBB) and ultra-reliable lowlatency communications (uRLLC) are two critical services. While eMBB focuses on maximum data rate, uRLLC prioritizes latency and reliability. However, sharing radio resources between these two use cases creates a complex planning problem due to the trade-off between these different factors. Our proposal introduces an efficient control strategy that optimizes the use of licensed and unlicensed radio resources across various services i.e., eMBB with guaranteed bit rate (eMBB-GBR) and uRLLC ensuring that the traffic requirements of each service are met. The main objective of the proposed control strategy is to maximize the utilization of resources by ensuring the availability of resources, reducing delays, and achieving high throughput. The simulation results clearly show the superiority of the proposed algorithm compared to the reference scheme. This significantly improves the performance of the network by improving the utilization of resources, reducing latency service, increasing the rate of successful packets, and reducing the probability of blocking. The algorithm shows significant improvements, particularly with an 8%, 22%, and 28 % increase in effective capacity for the eMBB-GBR service compared to the reference approaches when the session arrival rate is 1.2. In addition, it significantly reduces the probability of session drops for eMBB-GBR service by about 0.05, 0.12, and 0.17 compared to the reference approaches. Moreover, the proposed control strategy has a significant relative latency for uRLLC service of 13%, 21 %, and 26% compared to reference approaches when URLLC density is around 1000 UEs.

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“…Here comes the need for a distributed machine learning framework to handle these problems efficiently. Developed on top of Spark, MLlib is a library that provides preprocessing, model training, and making predictions at scale on data [9]. Various machine learning tasks can be performed using MLlib like classification, regression, clustering, deep learning, and dimensionality reduction.…”

Section: Machine Learning With Sparkmentioning

confidence: 99%

Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark

Samaan

Jeiad

2023

Bulletin EEI

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Recently, software defined networking (SDN) has been deployed extensively in diverse practical domains, providing a new direction in network management by separating the control plane from the data plane. Nevertheless, SDN is vulnerable to distributed denial of service (DDoS) attacks resulting from its centralized controller. Several studies have been suggested to address the DDoS attacks in SDN utilizing machine learning approaches. However, these approaches are resource-intensive and cause performance degradation since they cannot perform effectively in large-scale SDN networks that generate vast traffic statistics. To handle all these challenges, we build a DDoS attack detection model in SDN using Spark as a big data tool to overcome the limitations of conventional data processing methods. Four machine learning algorithms are employed. The decision tree (DT) is elected to be used for real-time deployment based on the performance results, which indicates that it has the best accuracy of 0.936. The model performance is compared with state-of-the-art and shows an overall better performance.

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A New Processing Approach for Scheduling Time Minimization in 5G-IoT Networks

Cited by 6 publications

References 22 publications

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

Radio Control Optimization for Enhanced QoS-based Spectrum Selection in Heterogeneous 5G Environments with eMBB and uRLLC

Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark

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