Explicit Congestion Notification-Based Congestion Control Algorithm for High-Performing Data Centers

Gilliard, Ezekia; Sharif, Kashif; Raza, Arif; Karim, Md Monjurul

doi:10.1109/africon55910.2023.10293272

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Addressing the coexistence of mouse and elephant flows in DCNs requires innovative approaches. Previous research, including our work on the use of explicit congestion notification (ECN) to control congestion, 5 has attempted to tackle this challenge. However, the dynamic nature of data center environments necessitates continual advancements in technology and tactics to ensure smooth operation in DCNs.…”

Section: Introductionmentioning

confidence: 99%

Intelligent load balancing in data center software‐defined networks

Gilliard,

Liu,

Aliyu

et al. 2024

Trans Emerging Tel Tech

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In response to the increasing demand for efficient resource utilization in data center networks (DCNs), the development of intelligent load‐balancing algorithms has become crucial. This article introduces the dual double deep Q network (DDQN) algorithm, designed for software‐defined networking (SDN) environments within data centers. By leveraging deep reinforcement learning, DDQN addresses the challenges posed by dynamic traffic patterns, diverse flow requirements, and the coexistence of elephant and mice flows. Our algorithm adopts a comprehensive SDN approach, evaluating the network's status by analyzing switch load and bandwidth utilization. Using convolutional neural networks for elephant and mice flows in DCN, our algorithm enables adaptive learning and training tailored to the specific demands of elephant flows. Employing a double deep Q network architecture (DDQN), DDQN optimizes paths for both elephant and mice flows independently. Real‐time adaptation mechanisms make routing decisions based on the robust learning capabilities of DDQN, enhancing network utilization and reducing packet loss by generating optimal forwarding paths according to the current network state and traffic patterns. Simulations conducted in a Mininet environment with RYU as the controller, utilizing a fat‐tree data center topology, validate the efficacy of DDQN. The results demonstrate its effectiveness in achieving higher throughput, lower latency, and superior load balancing compared to traditional algorithms like equal‐cost multipath and Hedera.

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Section: Introductionmentioning

confidence: 99%

Intelligent load balancing in data center software‐defined networks

Gilliard,

Liu,

Aliyu

et al. 2024

Trans Emerging Tel Tech

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“…The expansion of Tanzania's online presence has concurrently increased its vulnerability to cyberattacks. Sophisticated phishing scams targeting mobile money accounts and malware threats risking critical infrastructure and sensitive data underscore the urgent need to address cybersecurity concerns [2], [3], [4]. However, the rapid digitization has also exposed the nation to a myriad of cybersecurity challenges.…”

Section: Introductionmentioning

confidence: 99%

Cybersecurity in Tanzania: Opportunities and Challenges

2024

IJFMR

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Tanzania, like many developing nations, is undergoing rapid digitalization. While this brings numerous benefits, it also opens the door to increased cybersecurity threats. This abstract explores the unique landscape of cybersecurity in Tanzania, highlighting both the opportunities in emerging digital technology, government initiatives, increased awareness and collaborative approach. On the other hand it describe the challenges that lie ahead such as cybercrime landscape, limited digital literacy, data privacy concerns, inadequate resources and infrastructures.

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Knowledge graph reasoning for cyber attack detection

Gilliard,

Liu,

Aliyu

2024

IET Communications

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In today's digital landscape, cybercriminals are constantly evolving their tactics, making it challenging for traditional cybersecurity methods to keep up. To address this issue, this study explores the potential of knowledge graph reasoning as a more adaptable and sophisticated approach to identify and counter network attacks. By leveraging graph structures imbued with human‐like thinking, this method enhances the resilience of cybersecurity systems. The study focuses on three critical aspects: data preparation, semantic foundations, and knowledge graph inference techniques. Through an in‐depth analysis of these components, the research aims to reveal how knowledge graph reasoning can improve cyberattack detection and enhance the overall efficacy of cybersecurity measures, including intrusion detection systems. The proposed approach has undergone extensive experimentation to validate its effectiveness compared to existing methods. The results of the experiment have shown a remarkable advancement in accuracy, speed, and recall for recognition, surpassing current methods. This achievement is a notable contribution in the realm of managing big data in cybersecurity. The study establishes a foundation for the automation of network attack detection, ultimately enhancing overall network security.

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Explicit Congestion Notification-Based Congestion Control Algorithm for High-Performing Data Centers

Cited by 4 publications

References 21 publications

Intelligent load balancing in data center software‐defined networks

Intelligent load balancing in data center software‐defined networks

Cybersecurity in Tanzania: Opportunities and Challenges

Knowledge graph reasoning for cyber attack detection

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