2021· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
The evaluation of the proposed architecture through analytical models and simulation-based evaluations shows that the proposed architecture can reduce the latency onto 65 percent of the time relative to the conventional cloud-based architecture, affirm the claim that edge computing is an essential enabler of the next-generation applications that demand deterministic response time, high reliability and localized intelligence.
Abstract
The high rate of increase in the number of latency-sensitive services, including autonomous driving, remote surgery, augmented reality, industrial automation, and smart grids, has outlined the weaknesses of conventional cloud computing models. Although advantageous and scalable, centralized cloud data centers involve a high arguably serious latency because of network impacts, lengthy transmission paths, and delays while processing data. Edge computing has become a new paradigm that pulls the services of computation, storage, and intelligence back to the data source hence, allowing the creation of ultra-low latency services and real-time decision-making. This research paper is a detailed analysis of the edge computing architecture of ultra-low latency applications. The work explores the transformation of centralized cloud to distributed edge cloud ecosystems, examines key supporting technologies, 5G, software defined networking (SDN), network function virtualization (NFV), and containerized micro-services, and discusses such performance metrics as latency, throughput, reliability, and energy efficiency. It is being proposed that hierarchical edge computing architecture is based on integrating device edge, access edge, and regional edge layers in order to deploy them in a manner that is scaled and resilient. It is followed by the detailed literature survey analysis to evaluate the state-of-the-art architectures, schedule mechanisms, orchestration frameworks, as well as optimization techniques. In addition, there is also a methodological framework of the latency-aware task offloading, resource allocation, and service placement. The evaluation of the proposed architecture through analytical models and simulation-based evaluations shows that the proposed architecture can reduce the latency onto 65 percent of the time relative to the conventional cloud-based architecture. These findings affirm the claim that edge computing is an essential enabler of the next-generation applications that demand deterministic response time, high reliability and localized intelligence. A systematic architectural structure, performance discussion, and implementation of a guideline concerning the creation of subsequent ultra-low latency systems are presented in this paper.
The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
Alan Bundy· International Journal of Mod...· 0 citations
The rapid expansion of cloud computing, big data analytics, and artificial intelligence has positioned data centers as the backbone infrastructure of modern computing. Data center networks (DCNs) play a critical role in determining overall system performance and reliability. Existing DCN architectures face limitations, such as difficulties in balancing throughput and latency, insufficient fault-tolerance, and high expansion costs. To address these challenges, we propose ACDC (Augmented Cube-based Data Center), a novel server-centric DCN topology based on augmented cubes that achieves superior performance while maintaining cost-effectiveness through exclusive use of dual-port servers and low-port commodity switches. Firstly, we analyze the key features and properties of ACDC, with a focus on scalability and network diameter, establishing rigorous theoretical foundations for the proposed architecture. Secondly, we present comprehensive routing algorithms including ARouting for fault-free scenarios and AFR for fault-tolerant communication. Finally, extensive experimental evaluations demonstrate ACDC’s superior performance compared to state-of-the-art DCN architectures. Experimental results show that ACDC achieves a network diameter approximately 75% smaller than HSDC and 50% smaller than AQDN. Furthermore, ACDC maintains comparable throughput to the Fat-Tree under random traffic scenarios, while demonstrating substantial advantages under high-density all-to-all communication patterns, achieving at least 69.1% improvement in average throughput and at least 40.7% reduction in flow completion time compared to AQDN, DCell and FiConn. These confirm that ACDC strikes a good balance among performance, cost-efficiency, scalability, and fault-tolerance in contrast to the state-of-the-art DCN architectures.
Xuanli Liu, Weibei Fan, Zhenjiang Dong et al.· IEEE Transactions on Network...· 0 citations
The fifth generation (5G) of mobile communication is expected to deliver ultra-low latency, massive connectivity, and multi-gigabit throughput, but these benefits are hard to achieve with computation being centralized in a remote cloud data center. To overcome this limitation, Multi-access Edge Computing (MEC) moves the computation, storage and application logic to the edge of the network near the location where the data is generated. In this paper, the performance of an Edge-Computing-assisted 5G network is analyzed using the MATLAB 5G Toolbox in combination with the MATLAB 5G System Level and Link Level simulation framework. The layered architecture is proposed and mathematically modeled with regard to queuing delay, task-offloading decisions and energy consumption, considering 5G New Radio (NR) access network, Multi-access Edge Computing (MEC) layer and centralized cloud layer. The simulation is designed as a discrete-event system-level simulation with realistic 5G NR numerology, a large number of user equipment (UEs) and varying edge-server capacities to measure latencies, throughput, packet delivery ratio (PDR), response time, energy consumption, bandwidth utilization and reliability. Edge-assisted offloading shows an average end-to-end delay of 46-58% lower than a traditional cloud-only architecture and a maximum of 61% delay reduction in response time compared to the conventional cloud-only architecture, while maintaining high packet delivery ratio and reliability under heavy user density, while consuming a small amount of extra energy at the edge servers. These results validate MEC as a key enabling complementary technology for 6G networks with its supporting use cases for Ultra-Reliable Low-Latency Communication (URLLC); and the paper also reveals open research challenges in adaptive resource allocation, security and AI-based orchestration for future edge architectures in 6G.
Jayant Pratap, Amandeep, Dharmender Kumar, Suraj S· International Journal of Adv...· 0 citations
The rapid growth of Internet of Things (IoT) devices, together with the increasing demand for real-time data processing, has exposed the limitations of traditional centralized cloud computing architectures. Edge computing has emerged as a transformative paradigm that brings computation, storage, and networking closer to the data source, thereby reducing latency, conserving bandwidth, and enhancing privacy. This paper presents a comprehensive review of edge computing as a modern trend in information technology. We systematically examine the architectural foundations, enabling technologies, and deployment models that underpin edge computing ecosystems. Furthermore, we analyze prominent application domains including autonomous vehicles, smart cities, industrial IoT (IIoT), healthcare, and augmented or virtual reality where edge computing delivers measurable performance improvements. A critical assessment of open challenges such as security vulnerabilities, resource constraints, interoperability, and orchestration complexity is also provided. Finally, we outline future research directions, including the convergence of edge computing with artificial intelligence (Edge AI), 6G networks, digital twins, and serverless edge architectures. This review aims to serve as a foundational reference for researchers and practitioners seeking to understand the current state and future direction of edge computing within the broader information technology landscape.
Keywords: Edge Computing, Internet of Things, Fog Computing, Cloud-Edge Continuum, Latency Reduction, Edge AI, Distributed Systems, Real-Time Processing.
Awiti Gideon Appiah, Dr. Lazarus Kwao, Benjamin Opoku Atuahene· International Journal of Cre...· 0 citations
The rapid growth of Internet-of-Things (IoT) devices has increased the need for computing support close to end users, particularly for applications that cannot tolerate long processing delays or excessive energy consumption. Fog computing has emerged as a practical extension of the cloud to address these requirements, yet real deployments often involve a mix of devices with different processing abilities, communication characteristics, and power constraints. These differences make it difficult to decide when and where tasks should be offloaded.
This study introduces a task-offloading approach that adapts to changing conditions in a heterogeneous fog environment. The method continuously observes factors such as processor utilization, task size, communication delay, and the remaining energy of participating devices. Using this information, the system determines whether a task should run on the originating device, a nearby fog node, or the cloud. The approach aims to limit unnecessary transfers while striking a balance between energy use and execution delay.
Simulation experiments conducted in iFogSim indicate that the proposed strategy consistently improves performance over conventional static or energy-unaware schemes. The results show notable reductions in overall energy usage and significant improvements in task-completion success under varying network loads. These findings suggest that integrating real-time monitoring with adaptive decision-making can strengthen the efficiency and responsiveness of fog-based IoT systems.
Ashish Bagla, Deepak Dagar, Pratik Srivastava· International Journal For Mu...· 0 citations
The proposed hybrid methodology integrates workload prediction, adaptive scheduling, and resource consolidation, demonstrating significant energy savings without compromising system performance is proposed.
Seshagiri N· International Journal of Dat...· 0 citations
