SDN Load Prediction Algorithm Based on Artificial Intelligence

Volkov, Artem; Proshutinskiy, Konstantin; Adam, Abuzar B. M.; Ateya, Abdelhamied A.; Muthanna, Ammar; Koucheryavy, Andrey

doi:10.1007/978-3-030-36625-4_3

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…However, in addition, due to the inherent level of abstraction from physical resources, it is possible to create management applicationsservices that implement the system and business logic of the operator for managing devices of the controlled infrastructure and resources. In this case, as has already been stated in several scientific articles [16][17][18][19], as well as documents of international standardizing organizations (SDO's), the path of automation, as well as intellectualization of the network, lies through the development of services, data processing algorithms that belong to the Artificial Intelligence class. Thus, the monitoring and control system, which is an infrastructure application (software complex), which implements in itself algorithms of machine intelligence as algorithms, makes it possible to automate to a certain extent and, moreover, make the infrastructure intelligent, without the need for constant control and management by people , highly qualified specialists.…”

Section: Proposed Framework Of Distributed Dynamic Fog Computing System (Dd-fog)mentioning

confidence: 92%

DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework

et al. 2021

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Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.

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Section: Proposed Framework Of Distributed Dynamic Fog Computing System (Dd-fog)mentioning

confidence: 92%

DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework

et al. 2021

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“…Separation enables the network controller to have a global view, facilitate the management at run time, reduce data traffic, and increase network flexibility. An SDN traditionally solves the issues of configuring routers and switches, leading to being error prone and not fully utilizing the network infrastructure capability [32]. It provides programmability for deploying network applications by breaking down the control and data planes through virtualization [33].…”

Section: Software-defined Network (Sdn)mentioning

confidence: 99%

Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research Directions

Ali,

Elsaid,

Ateya

et al. 2023

Future Internet

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The concept of smart cities, which aim to enhance the quality of urban life through innovative technologies and policies, has gained significant momentum in recent years. As we approach the era of next-generation smart cities, it becomes crucial to explore the key enabling technologies that will shape their development. This work reviews the leading technologies driving the future of smart cities. The work begins by introducing the main requirements of different smart city applications; then, the enabling technologies are presented. This work highlights the transformative potential of the Internet of things (IoT) to facilitate data collection and analysis to improve urban infrastructure and services. As a complementary technology, distributed edge computing brings computational power closer to devices, reducing the reliance on centralized data centers. Another key technology is virtualization, which optimizes resource utilization, enabling multiple virtual environments to run efficiently on shared hardware. Software-defined networking (SDN) emerges as a pivotal technology that brings flexibility and scalability to smart city networks, allowing for dynamic network management and resource allocation. Artificial intelligence (AI) is another approach for managing smart cities by enabling predictive analytics, automation, and smart decision making based on vast amounts of data. Lastly, the blockchain is introduced as a promising approach for smart cities to achieve the required security. The review concludes by identifying potential research directions to address the challenges and complexities brought about by integrating these key enabling technologies.

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“…NFVs and SDNs are considered as the most significant and distinguished pillars in 5G networking. These two pillars form the basis of a software-based automation model of the latest networking methods and allow the provision of network slicing [24][25][26].…”

Section: Related Workmentioning

confidence: 99%

Intent-Based End-to-End Network Service Orchestration System for Multi-Platforms

et al. 2020

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On-demand service is the main feature of the 5G network, and Network Function Virtualization (NFV) provides it by virtualizing the existing 5G network infrastructure. NFV crafts various virtual networks on a shared physical network, but one of the core challenges in future 5G networks is to automate the modeling of Virtualized Network Functions (VNFs) and end-to-end Network Service (NS) orchestration with less human interaction. Traditionally, the descriptor of VNF and NS is created manually, which requires expert-level skills. This manual approach has a big threat of human error, which can be avoided by using the Intent-Based Networking (IBN) approach. The IBN approach eliminates the requirement of expertise for designing VNFs and NS by taking users’ intentions as an input. In this paper, the proposed system presents the Intent Management System for VNF modeling and end-to-end NS orchestration for multi-platforms. This system takes the high-level information related to a specific service, configures it accordingly, and converts it into the selected platform. The proposed system is tested using Mobile Central Office Re-architected as Data Center (M-CORD) and Open-Source Management and Orchestration (OSM) orchestrators. The results section shows that the proposed system reduces the effort of the end-user in creating network slices and provides seamless end-to-end service orchestration.

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SDN Load Prediction Algorithm Based on Artificial Intelligence

Cited by 11 publications

References 8 publications

DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework

DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework

Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research Directions

Intent-Based End-to-End Network Service Orchestration System for Multi-Platforms

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