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Journal articles on the topic 'Networking and computing resources'
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1
Yasodha K, M. Shabitha Sree, Krishna Sri. S, Kirubha Shakthi. J, Karthick. K, and Kanishka. N. "Networking in Cloud Computing: Embracing Contests and Seizing Prospects." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 3 (2024): 53–63. http://dx.doi.org/10.32628/ijsrset241138.
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Cloud computing presents the concept of utility computing, which allows users to access computing, storage, and networking resources as needed, with a usage-based pricing model. However, consumers have limited control over network resources, and cloud-computing providers confront a number of issues when operating infrastructure as a service (IaaS) environments. This research investigates the networking difficulties and federation challenges inherent in IaaS, as well as unique software-defined networking (SDN) concepts that could provide efficient solutions for future deployments.
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Babu, R., K. Jayashree, and R. Abirami. "Fog Computing Qos Review and Open Challenges." International Journal of Fog Computing 1, no. 2 (2018): 109–18. http://dx.doi.org/10.4018/ijfc.2018070104.
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Internet of Things (IoT) enables inters connectivity among devices and platforms. IoT devices such as sensors, or embedded systems offer computational, storage, and networking resources and the existence of these resources permits to move the execution of IoT applications to the edge of the network and it is known as fog computing. It is able to handle billions of Internet-connected devices and is well situated for real-time big data analytics and provides advantages in advertising and personal computing. The main issues in fog computing includes fog networking, QoS, interfacing and programming model, computation offloading, accounting, billing and monitoring, provisioning and resource management, security and privacy. A particular research challenge is the Quality of Service metric for fog services. Thus, this paper gives a survey of cloud computing, discusses the QoS metrics, and the future research directions in fog computing.
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HamaAli, Kurdistan Wns, and Subhi R. M. Zeebaree. "Resources Allocation for Distributed Systems: A Review." International Journal of Science and Business 5, no. 2 (2021): 76–88. https://doi.org/10.5281/zenodo.4462088.
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Resource allocation in a distributed system is the process of allocating the workload across multiple resources to optimize the required performance criteria. Different techniques are used to manage resource allocation in such distributed systems. The resource allocation for distributed systems such as cloud computing, cellular network, Software-Defined Networking (SDN), Radar Imaging and 5G Networks are used. In this paper many resource allocation algorithms in different environment and area that mentioned before are reviewed, compared and summarized. for instance, Cloud-based computation algorithm implementations in cloud computing, Direction Method of Multipliers (ADMM) algorithm for SDN Networking, FFR algorithm application in cellular network, Fairness-based Distributed Resource Allocation (FDRA) algorithm for 5G networks, then each the results in each area are discussed in critique point of view.
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Thilak Raj Surendra Babu. "Advancing Edge Computing: A Technical Analysis of Lightweight Network Virtualization." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 1089–96. https://doi.org/10.32996/jcsts.2025.7.4.123.
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This technical analysis examines a novel lightweight network virtualization architecture specifically designed for resource-constrained edge environments. As computational resources increasingly shift toward network edges, traditional virtualization approaches optimized for well-resourced data centers face fundamental limitations when deployed in edge scenarios. The architecture addresses these challenges through several innovative approaches: distributing decision-making capabilities to edge nodes with local caching mechanisms, implementing streamlined packet processing pipelines, employing dynamic resource allocation techniques, and reimagining security implementations for resource efficiency. These architectural innovations enable sophisticated networking capabilities on hardware platforms with significant constraints in processing power, memory bandwidth, connectivity, and energy availability. The architecture has demonstrated practical effectiveness across diverse deployment scenarios including community mesh networks, remote healthcare clinics, disaster response systems, and industrial IoT environments. By fundamentally rethinking virtualization through the lens of extreme resource constraints, this approach extends advanced networking capabilities to previously underserved environments, potentially transforming how organizations deploy network services in remote locations, disaster response scenarios, and developing regions with limited infrastructure.
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Arwa Mohamed, Mosab Hamdan, Ahmed Abdelazizb, and Sharief F. Babiker. "Dynamic Resource Allocation in Cloud Computing Based on Software-Defined Networking Framework." Open Journal of Science and Technology 3, no. 3 (2020): 304–13. http://dx.doi.org/10.31580/ojst.v3i3.1668.
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cloud computing has become more powerful with the inclusion of software-defined networking (SDN) in its environment. In Cloud Data Centers (CDCs), an important research issue is how to forecast and allocate resources efficiently whilst achieving Quality of Service (QoS) of users request with minimal overall power consumption; taking into account the frequent changes in resource requirements. In this paper, we propose a Supervisor Controller-based Software-Defined Cloud Data Center (SC-boSD-CDC) framework for dynamic resource allocation and prediction of cloud computing-based SDN. In this proposed module, Genetic Algorithm (GA) is proposed to deal with the multi-objective problem of dynamically forecasting the utilization of resources in both compute nodes and links bandwidth of network as well as energy consumption in the Cloud Data Center (CDC). Furthermore, a Virtual Machines (VMs) placement algorithm is also proposed to allocate computing resources and routing algorithms to choose the proper bandwidth links between switches; resulting in increased CPU and memory utilization and reduction in overall power consumption.
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Thapliyal, Shivankur. "A Newly Proposed Prospective and Robust Computer Networking Model Architecture Based on the Infrastructure of Cloud Computing Contrivance." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 331–37. http://dx.doi.org/10.22214/ijraset.2021.37901.
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Abstract: Computer Networking Play’s a major role for data communication or data sharing and data transmissions from one location to another, which are geographically differ, but in today’s scenario where the main and primary major concerns are not to data transfer but also utilize all resources with greater efficiency and also preserves the confidentiality and integrity of the messages with respect to speed and time with lower Bandwidth and also consume a very low computational costs with low power supply and redirect to optimality. Cloud Computing also play’s a significant role to access data at geographically different locations. So In this paper we create a fusion of Computer Networking Architecture and Cloud Computing Architecture and released a very much superior fundamentally strong Cloud computing based Computer Networking model, which works on the concepts of ‘Virtualization’. Because when the number of hardware components (Servers) drastically increases all factors which are responsible to make possible networking among nodes are also consume each resources at extreme level, and networking becomes complex and slow, that’s why we used the concept of Virtual Machine. In this paper we proposed a Computer Networking model using the concepts of Cloud Computing. This model also suitable for data transmission but also take concern the most significant feature of Computer Networking, which is Data Security. This model also used some Proxy servers/ firewalls to take concern some security mechanisms. In this paper we also proposed Communication Oriented model among the Intercluster domains that how one node which belongs to another CLOUD cluster make possible communication among other InterCLOUD clusters with respect to data security measures. In this paper we proposed three models related to this networking model, which is CLOUD Networking Infrastructure, Connection Oriented model, Communication Oriented model. The detailed description of all three models are in the upcoming sections of this paper. Keywords: Cloud computing based computer networking model, A virtual model for computer networking, Computer Networking model based on virtualization, Virtualization based computer networking model.
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Chen, Jing, Tao Li, and Min Huang. "The privacy protection of the internet of vehicles resource transaction details based on blockchain." PLOS ONE 20, no. 1 (2025): e0312854. https://doi.org/10.1371/journal.pone.0312854.
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The rapid development of Internet of Things technology has promoted the popularization of Internet of Vehicles, and its safety and reliability have become the focus of intelligent transportation system research. Vehicle-road collaboration relies on the collaborative computing and storage resources of the vehicle on-board unit (OBU), which are usually limited. When the vehicle in the edge area needs to do computing tasks such as intelligent driving, but its own computing resources are insufficient. Therefore, it needs other computing resources from idle vehicles and road side unit (RSU). This resource sharing can get additional computing resources to complete the task, and can be more convenient to complete the computing task quickly. Most current studies consider this type of resource sharing as a vehicle-to-vehicle (V2V) network transaction, aiming to stimulate the enthusiasm of vehicle sharing and optimize the utilization of computing resources in edge areas. However, the traditional blockchain transaction mode exposes serious privacy disclosure risks in vehicle networking resource transactions, including the openness and transparency of user identity, transaction details, and transaction addresses, which poses great challenges to data security. Therefore, this study innovatively proposed a blockchain-based privacy protection scheme for vehicle networking resource transaction details. By introducing committed value protection, zero-knowledge proof technology and constructing temporary transaction addressed mechanism. The scheme realized the comprehensive privacy protection of transaction funds, transaction details and transaction addresses, which could effectively avoid the disclosure of users’ sensitive information. Compared with the existing methods, the proposed scheme not only greatly enhanced the privacy protection capability, but also ensured the efficiency and security of transaction verification through zero-knowledge proof, avoiding the direct exposure of private keys. Meanwhile, the experimental verification demonstrates that the scheme not only enhances the level of privacy protection but also does not augment the supplementary processing burden. Furthermore, it is evident that the scheme meets the rigorous requirements for real-time resource transactions in the Internet of Vehicles.
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Jichkar, Gaurav. "Transforming Data Sharing via Advanced Peer-to-Peer Communication and Networking." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46199.
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Abstract 5G networks promise super-fast internet, but efficiently sharing resources is a big challenge, especially when users directly connect with each other (Peer-to-Peer). This research explores a new way to allocate resources in 5G P2P communication. We use machine learning to predict how much data will be used and adjust resource sharing in real-time. This means that users with the most urgent needs get priority, and resources are distributed fairly. Our method significantly improved network performance. Simulations showed a 30% increase in data transfer speeds, a 25% reduction in delays, and an 18% overall efficiency boost. Compared to older methods, our approach better balances the load and minimizes data loss. Extensive testing in a 5G environment confirmed the system's effectiveness, demonstrating its ability to handle many users and adapt to changing conditions. In conclusion, our research shows that P2P communication, combined with intelligent resource allocation, can unlock the full potential of 5G networks for data transfer. Keywords: 5G Networks, Peer-to-Peer Communication, Resource Allocation, Data Transmission, Machine Learning, Internet of Things (IoT), Edge Computing, Network Slicing, Artificial Intelligence (AI), Blockchain, Cloud Computing, Latency Optimization, Spectrum Management, Smart Cities, Wireless Sensor Networks.
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Deekshita, Vishwanath K., L. N. Haritej, S. Amrute Himani, Prakash Keerthana, and P. Jayarekha. "A Survey on Green Networks and Communications." Journal of Network Security Computer Networks 5, no. 3 (2019): 37–41. https://doi.org/10.5281/zenodo.3538884.
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<em>Green computing is the efficient use of computers and their resources, so that electronic devices can be environmentally viable and ecofriendly. It encompasses a broad range of disciplines like design, engineering, manufacturing, usage and disposal of computing devices. Green computing involves the efficient use of servers, CPU and network peripherals with resource management and proper disposal techniques to lessen e-waste generated.</em><em> Adoption of Green computing assures minimal functional expenses in any organization as well as lower the detrimental environmental impacts while ascertaining maximal profits. It covers all aspects of a network architecture like PCs, switches, routers, peripherals and communication media. Green networking is the term used to refer to the process of making computer networks more environmentally friendly and energy efficient. It includes processes that will directly or indirectly reduce the energy usage and consumption thus reducing the negative impact on the environment. Some of the best practices used in green networking are: Incorporating the concept of virtualization, High availability servers obtained by consolidation, Upgradation of equipment, managing systems in an efficient way, Using efficient techniques like video conferencing VPN’s over physical travel. Green communications can be defined as the adoption of eco-friendly, energy-efficient communication and networking advancements to truncate the usage of resources in every possible aspects of communication.</em>
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Li, Xi, Thomas DeiB, Josep Mangues-Bafalluy, et al. "Automating Vertical Services Deployments over the 5GT Platform." IEEE Communications Magazine 58, no. 7 (2020): 44–50. https://doi.org/10.1109/MCOM.001.1900582.
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This article presents a system for 5G networks that makes it possible to meet the diverse needs of vertical industries simultaneously sharing the same physical infrastructure. Orchestration, network slicing, edge computing, and federation are key technologies enabling industry verticals to have their own virtual networks, which might require aggregating transport networking and computing fabric, from the edge up to the core and cloud. Three novel building blocks are defined to meet these challenges in an automated manner: a vertical slicer as the entry point to create services and request slices, a service orchestrator to manage the services and decide their placement and allocation of required resources, and a mobile transport and computing platform virtualizing infrastructure networking and computing resources in an integrated manner. An experimental evaluation of the developed system shows its feasibility and confirms some of the benefits expected.
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Križanović, Višnja, Jelena Vlaović, Drago Žagar, and Snježana Rimac-Drlje. "Distribution and Allocation of Network Resources Based on Predictive Analyses of Time-Series Telecommunications Data." International journal of electrical and computer engineering systems 13, no. 4 (2022): 253–67. http://dx.doi.org/10.32985/ijeces.13.4.2.
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With the fast development of different communication technologies, applications, and services, the adoption of advanced sensory and computing solutions, such as the various Internet of Things (IoT) and mobile computing solutions, is continuously growing. The massive adoption of mobile computing and IoT sensory devices encouraged the continuous growth of generated network traffic. Therefore, the selection of adequate solutions for efficient data processing became necessary. Despite numerous advantages arising from effective data processing, operators and enterprises working within the ICT domain have only limited amounts of available networking resources to store, process, and use valuable information extracted from large quantities of gathered data. In this paper, an optimal planning process and prediction of usage of network resources is examined. It takes into consideration the results of predictive modeling processes based on available sets of time series telecommunications data. The given forecasts enable effective selection of network architectures, as well as the distribution and allocation of network resources considering the cloud, edge, and fog networking concepts.
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Khan, Nawsher, A. Noraziah, Elrasheed I. Ismail, Mustafa Mat Deris, and Tutut Herawan. "Cloud Computing." International Journal of E-Entrepreneurship and Innovation 3, no. 2 (2012): 51–59. http://dx.doi.org/10.4018/jeei.2012040104.
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Cloud computing is fundamentally altering the expectations for how and when computing, storage, and networking resources should be allocated, managed, consumed, and allow users to utilize services globally. Due to the powerful computing and storage, high availability and security, easy accessibility and adaptability, reliable scalability and interoperability, cost and time effective cloud computing is the top, needed for current fast growing business world. A client, organization or a trade that adopting emerging cloud environment can choose a well suitable infrastructure, platform, software, and a network resource, for any business, where each one has some exclusive features and advantages. The authors first develop a comprehensive classification for describing cloud computing architecture. This classification help in survey of several existing cloud computing services developed by various projects globally such as Amazon, Google, Microsoft, Sun and Force.com and by using this survey’s results the authors identified similarities and differences of the architecture approaches of cloud computing.
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Abouelela, Mohamed, and Mohamed El-Darieby. "Multidomain Hierarchical Resource Allocation for Grid Applications." Journal of Electrical and Computer Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/415182.
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Geographically distributed applications in grid computing environments are becoming more and more resource intensive. Many applications require the collaboration between different domains, may be independently administrated domains, to exchange data and share computing and storage resources. This collaboration should be done in a way that maintains the privacy of each participant domain. This calls for new architectures and approaches to deal with such multidomain environments. We propose a hierarchical-based architecture as well as multidomain hierarchical resource allocation approach. The resource allocation is performed in a distributed way among different domains such that each participant domain keeps its internal topology and private data hidden while sharing abstracted information with other domains. Both computing and networking resources are jointly scheduled while optimizing the application completion time taking into account data transfer delays. Simulation results show the scalability and feasibility of the proposed approach.
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Song, Inseok, Prohim Tam, Seungwoo Kang, Seyha Ros, and Seokhoon Kim. "DRL-Based Backbone SDN Control Methods in UAV-Assisted Networks for Computational Resource Efficiency." Electronics 12, no. 13 (2023): 2984. http://dx.doi.org/10.3390/electronics12132984.
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The limited coverage extension of mobile edge computing (MEC) necessitates exploring cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future computation-intensive and mission-critical applications. Moreover, the workflow for task offloading in software-defined networking (SDN)-enabled 5G is significant to tackle in UAV-MEC networks. In this paper, deep reinforcement learning (DRL) SDN control methods for improving computing resources are proposed. DRL-based SDN controller, termed DRL-SDNC, allocates computational resources, bandwidth, and storage based on task requirements, upper-bound tolerable delays, and network conditions, using the UAV system architecture for task exchange between MECs. DRL-SDNC configures rule installation based on state observations and agent evaluation indicators, such as network congestion, user equipment computational capabilities, and energy efficiency. This paper also proposes the training deep network architecture for the DRL-SDNC, enabling interactive and autonomous policy enforcement. The agent learns from the UAV-MEC environment through experience gathering and updates its parameters using optimization methods. DRL-SDNC collaboratively adjusts hyperparameters and network architecture to enhance learning efficiency. Compared with baseline schemes, simulation results demonstrate the effectiveness of the proposed approach in optimizing resource efficiency and achieving satisfied quality of service for efficient utilization of computing and communication resources in UAV-assisted networking environments.
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Chaudhari, Vaishnavi, Poonam Dhake, and Snehal Salunkhe. "Securing the Skies- A Critical Analysis of Cloud Infrastructure Vulnerabilities." International Journal of Innovative Research in Computer Science and Technology 12, no. 3 (2024): 87–95. http://dx.doi.org/10.55524/ijircst.2024.12.3.14.
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Cloud computing offers storage, infrastructure, computing, networking, databases, platform, software, and analytics services over the Internet. It provides numerous benefits including scalability, cost management, broad access to resources, elasticity, resource pooling. Though cloud computing is mature and widely adopted computing model in software as well as non-software industries, it has several issues regarding security as it provides most of the cloud services over the public infrastructure. Denial of service (DOS), malware injection, insecure APIs, data loss, data breaches, hypervisor vulnerabilities, VM escape are a few major issues in cloud computing. In this paper, authors tried to provide a comprehensive analysis of critical security issues in cloud computing. Furthermore, this paper critically analyses the existing solutions to the various security issues in the cloud computing model.
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Liyanage, Mohan, Chii Chang, and Satish Narayana Srirama. "Adaptive mobile Web server framework for Mist computing in the Internet of Things." International Journal of Pervasive Computing and Communications 14, no. 3/4 (2018): 247–67. http://dx.doi.org/10.1108/ijpcc-d-18-00023.
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Purpose The distant data centre-centric Internet of Things (IoT) systems face the latency issue especially in the real-time-based applications, such as augmented reality, traffic analytics and ambient assisted living. Recently, Fog computing models have been introduced to overcome the latency issue by using the proximity-based computational resources, such as the computers co-located with the cellular base station, grid router devices or computers in local business. However, the increasing users of Fog computing servers cause bottleneck issues and consequently the latency issue arises again. This paper aims to introduce the utilisation of Mist computing (Mist) model, which exploits the computational and networking resources from the devices at the very edge of the IoT networks. Design/methodology/approach This paper proposes a service-oriented mobile-embedded Platform as a Service (mePaaS) framework that allows the mobile device to provide a flexible platform for proximal users to offload their computational or networking program to mePaaS-based Mist computing node. Findings The prototype has been tested and performance has been evaluated on the real-world devices. The evaluation results have shown the promising nature of mePaaS. Originality/value The proposed framework supports resource-aware autonomous service configuration that can manage the availability of the functions provided by the Mist node based on the dynamically changing hardware resource availability. In addition, the framework also supports task distribution among a group of Mist nodes.
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Alharbi, Abdullah, Hashem Alyami, Poongodi M, Hafiz Tayyab Rauf, and Seifedine Kadry. "Intelligent scaling for 6G IoE services for resource provisioning." PeerJ Computer Science 7 (October 26, 2021): e755. http://dx.doi.org/10.7717/peerj-cs.755.
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The proposed research motivates the 6G cellular networking for the Internet of Everything’s (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module.
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Jewargi, Kiran. "What is Cloud computing Benfits and challenges of Cloud." Global Journal of Computer Sciences: Theory and Research 13, no. 1 (2023): 28–33. http://dx.doi.org/10.18844/gjcs.v13i1.8622.
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Cloud computing provides on-demand delivery of computer resources ranging from, compute, data storage, networking, software, and other IT services over the internet in exchange for pay as you go billing model. The study discusses various types of cloud computing available, cloud computing advantages, cloud computing implementation models, cost-effective IT solution, IT service agility, challenges, and features of cloud computing in this growing digital world.
 Keywords: cloud, data storage, compute, digital, on-demand billing model.
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Sakariya, Harsh Bipinbhai, and Ganesh D. "Taxonomy of Load Balancing Strategies in Distributed Systems." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 03 (2024): 1796–802. http://dx.doi.org/10.15680/ijircce.2024.1203070.
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Large-scale parallel and distributed computing systems are becoming more popular as a result of falling hardware prices and improvements in computer networking technologies. Improved performance and resource sharing are potential benefits of distributed computing systems. We have provided a summary of distributed computing in this essay. The differences between parallel and distributed computing, terms related to distributed computing, task distribution in distributed computing, performance metrics in distributed computing systems, parallel distributed algorithm models, benefits of distributed computing, and distributed computing's application domain were all covered in this paper.
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Gauresh Dilip Vanjare. "Telco Edge Architecture for Deterministic Experience: A Research-Driven Analysis." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 362–75. https://doi.org/10.32996/jcsts.2025.7.5.46.
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Telecommunications networks are evolving from connectivity providers to computational platforms supporting mission-critical applications that demand deterministic performance guarantees. Traditional cloud architectures introduce unpredictability through centralization, while edge computing offers a solution by positioning resources closer to users and data sources. This article examines how telco edge architectures deliver deterministic networking capabilities through architectural components, deployment strategies, and performance characteristics. The integration of Multi-access Edge Computing with 5G Service-Based Architecture creates a foundation for guaranteed service levels, while network slicing enables isolated virtual networks with tailored performance metrics. Edge deployments demonstrate significant improvements in latency, jitter, reliability, and throughput across various implementation scenarios. Commercial deployments showcase diverse approaches to implementing deterministic edge computing, from API-based QoS control to AI-enhanced industrial platforms. However, several limitations remain concerning economic viability, with infrastructure sharing models emerging to address capital requirements; energy efficiency challenges, requiring careful optimization of distributed resources; and operational complexity, necessitating sophisticated automation for effective management at scale. Despite these challenges and additional concerns in performance consistency, resource contention, and security, the telco edge represents a transformative architecture that enables deterministic experiences for next-generation applications. The convergence of edge computing with deterministic networking principles opens new possibilities for applications requiring strict timing guarantees, reshaping how telecommunications infrastructure supports critical services while balancing performance benefits against implementation costs and operational considerations.
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Meng, Xiangli, Lingda Wu, and Shaobo Yu. "Research on Resource Allocation Method of Space Information Networks Based on Deep Reinforcement Learning." Remote Sensing 11, no. 4 (2019): 448. http://dx.doi.org/10.3390/rs11040448.
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The space information networks (SIN) have a series of characteristics, such as strong heterogeneity, multiple types of resources, and difficulty in management. Aiming at the problem of resource allocation in SIN, this paper firstly establishes a hierarchical and domain-controlled SIN architecture based on software-defined networking (SDN). On this basis, the transmission, caching, and computing resources of the whole network are managed uniformly. The Asynchronous Advantage Actor-Critic (A3C) algorithm in deep reinforcement learning is introduced to model the process of resource allocation. The simulation results show that the proposed scheme can effectively improve the expected benefits of unit resources and improve the resource utilization efficiency of the SIN.
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Li, Zhiyuan, and Ershuai Peng. "Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking." Sensors 21, no. 3 (2021): 955. http://dx.doi.org/10.3390/s21030955.
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With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.
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Mukherjee, Debangshu, Kevin M. Roccapriore, Anees Al-Najjar, et al. "A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy." Microscopy Today 30, no. 6 (2022): 10–19. http://dx.doi.org/10.1017/s1551929522001286.
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Abstract:The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. Here we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.
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Neha Dhadiwal, Nayana Joshi, Renuka Jadhav, Nisha Satpute, Shilpa Chavan, and Madhuri Shahapurkar. "SSTF Algorithm in Cloud Storage Management." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1530–32. https://doi.org/10.47392/irjaeh.2025.0218.
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Shortest Seek Time First (SSTF) is one of the disk scheduling algorithms which is used in memory management systems. This algorithm is primarily used in disk scheduling within an operating system that has the core principle of prioritizing requests based on proximity can be adapted for cloud storage management, particularly in optimizing data retrieval and resource allocation. Cloud computing is on-demand delivery of computing resources such as servers, storage, databases, networking, and software, over the internet which allows the users to access and use them without managing the underlying infrastructure. SSTF and Cloud computing has many real-life applications in day to day activities such as-Content Delivery Networks (CDNs), Database Systems, Object Storage
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M. Ponnavaikko, C. Anuradha,. "Efficient of Floading Approach for Resource Allocation and Optimization in Mobile Edge Computing Environment." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 2581–93. http://dx.doi.org/10.17762/turcomat.v12i6.5704.
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Cloud computing provides a platform for services and resources over the internet for users. The large pool of data resources and services has enabled the emergence of several novel applications such as smart grids, smart environments, and virtual reality. However, the state-of-the-art of cloud computing faces a delay constraint, which becomes a major barrier for reliable cloud services. This constraint is mostly highlighted in the case of smart cities (SC) and the Internet of Things (IoT). Therefore, the recent cloud computing paradigm has poor performance and cannot meet the low delay, navigation, and mobility support requirements.Machine-to-machine (M2M) connectivity has drawn considerable interest from both academia and industry with a growing number of machine-type communication devices (MTCDs). The data links with M2M communications are usually small but high bandwidth, unlike conventional networking networks, demanding performance management of both energy consumption and computing. The main challenges faced in mobile edge computing are task offloading, congestion control, Resource allocation, security and privacy issue, mobility and standardization .Our work mainly focus on offloading based resource allocation and security issues by analyzing the network parameters like reduction of latency and improvisation of bandwidth involved in cloud environment. The cloudsim simulation tool has been utilized to implement the offload balancing mechanism to decrease the energy consumption and optimize the computing resource allocation as well as improve computing capability.
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Kaur, Jasmine. "MECHANISMS AND TOOLS USED FOR RESOURCE ALLOCATION IN THE CLOUD." International Journal of Advanced Research in Computer Science 14, no. 03 (2023): 80–86. http://dx.doi.org/10.26483/ijarcs.v14i3.7006.
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Pay-as-you-go access to computer resources is a major selling point of the cloud computing model. Cloud tenants demand complete networking of their dedicated resources to simply implement network functions and services, in addition to the conventional computer resources. The flexibility and convenience of on-demand resource provisioning make cloud computing a compelling computing platform. The key to meeting fluctuating needs and maximizing return on investment from Cloud-supporting infrastructure is dynamic resource allocation and reallocation. For traditional IaaS, we offer an energy-efficient resource allocation strategy based on bin packing. In this paper, we present an accurate energy-conscious method for initial resource allocation by casting the issue of energy-efficient resource allocation as a bin-packing model. The available VMs (virtual machines) employ a modified version of the max-min scheduling technique, which saves money and resources. The results of this study give a framework for comparing and contrasting the many different resource distribution approaches that have been proposed by other researchers. The importance of efficient data centers for the cloud is growing. Power consumption has been a major problem due to its expanding size and widespread usage. The overarching purpose of this effort is to create models and algorithms for resource allocation that are both energy-efficient and take into account a variety of relevant factors
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Bagul, Prof. Tejal D. "The Integration of Cloud Computing and Web Services." International Journal of Advance and Applied Research 6, no. 25(A) (2025): 54–56. https://doi.org/10.5281/zenodo.15300572.
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<strong>Abstract:</strong> Web services and cloud computing have completely changed how people and organizations use and access technology in recent years. Web services offer a standardized method of integrating different software systems, while cloud computing offers scalable computer resources via the internet. Instead of building and maintaining computing infrastructures in-house, cloud computing allows businesses to use compute resources as a utility, similar to energy. For both enterprises and end consumers, cloud computing offers a number of alluring advantages. Web services are software programs that facilitate machine-to-machine communication via a network. They offer a platform-neutral method of system-to-system communication through protocols. This paper explores the advantages, difficulties, and potential developments of cloud computing and web services working together. Businesses have been able to innovate, cut expenses, and increase efficiency thanks to the integration of these technologies, which has made them crucial in the age of digital transformation.
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Abdullahi, Abdulrahman Yusuf, Mukhtar Ibrahim Bello, Sadiku Aminu Sani, Amina Ibraim, and Muhammad Ahmad Baballe. "STUDYING COMPARATIVE CLOUD COMPUTING TECHNOLOGY." Far East Journal of Electronics and Communications 27 (October 11, 2023): 41–50. http://dx.doi.org/10.17654/0973700623003.
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In order to provide quicker innovation, adaptable resources, and scale economies, cloud computing is the supply of computing services, including servers, storage, databases, networking, software, analytics, and intelligence, over the Internet. As technology advanced in recent years, cloud technologies were increasingly utilized. Cloud technologies are appealing because data may be stored, transmitted to numerous locations at once, and accessed at any time. It is a system that enables data storage on the Internet, releasing users from the constraints of physical settings and storage devices and enabling access from any location at any time.
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A., Y. Abdullahi, I. Bello M., A.Sani S., A.Ibraim, and A. Baballe M. "Studying Cloud Computing Technology Comparatively." Global Journal of Research in Engineering & Computer Sciences 3, no. 4 (2023): 36–39. https://doi.org/10.5281/zenodo.8274638.
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In order to provide quicker innovation, adaptable resources, and scale economies, cloud computing is the supply of computing services, including servers, storage, databases, networking, software, analytics, and intelligence, over the Internet. As technology advanced in recent years, cloud technologies were increasingly utilized. Cloud technologies are appealing because data may be stored, transmitted to numerous locations at once, and accessed at any time. It is a system that enables data storage on the Internet, releasing users from the constraints of physical settings and storage devices and enabling access from any location at any time.
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Sarkar, Nurul I. "Enhancing Teaching and Learning Wi-Fi Networking using Limited Resources to Undergraduates." International Journal of Web-Based Learning and Teaching Technologies 8, no. 4 (2013): 1–18. http://dx.doi.org/10.4018/ijwltt.2013100101.
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Motivating students to learn Wi-Fi (wireless fidelity) wireless networking to undergraduate students is often difficult because many students find the subject rather technical and abstract when presented in traditional lecture format. This paper focuses on the teaching and learning aspects of Wi-Fi networking using limited hardware resources. It provides a walk-through tutorial on setting up Wi-Fi networks using wireless laptops and access points. Students can easily set up and configure Wi-Fi networks using relatively few computing resources to learn networking concepts more effectively. By measuring the key performance metrics such as network throughput and response times, students are able to gain a deeper understanding of Wi-Fi network performance and related issues. The effectiveness of these Wi-Fi practical learning activities has been evaluated both formally by students and informally in discussion within the teaching team. This paper describes the overall effectiveness of teaching and learning Wi-Fi network fundamentals using limited resources.
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Cheng, Yong, Jie Du, Yonggang Yang, et al. "Designing Collaborative Edge Computing for Electricity Heterogeneous Data Based on Social IoT Systems." International Journal of Distributed Systems and Technologies 13, no. 7 (2022): 1–22. http://dx.doi.org/10.4018/ijdst.307955.
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Power generation, transmission, maintenance costs, and electricity prices are heavily influenced by accurate load forecasts at energy suppliers' operation centers. Every aspect of our life has been transformed by the social internet of things (SIoT). Collaborative edge computing (CEC) has emerged as a new paradigm for meeting the demands of the internet of things by alleviating resource congestion (IoT). Remote devices can connect to CEC's processing, storage, and network resources. About short-term electrical load forecasting, this study explores the application of feed-forward deep neurological networking (FF-DNN) and recurrent deep neuronal networking (R-DNN) methods and analyzes their accuracy and computing performance. A dynamic prediction system using a deep neural network (DPS-DNN) is proposed in this research. The recently unveiled smartgrid with the results shows the higher performance of the proposed DPS-DNN model than the existing models with an enhancement of 93.15% based on collaborative edge networks based on SIoT.
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Kumar, Brajesh, and Kumari Deepa Rani. "ROLE OF CLOUD COMPUTING PLATFORMS IN PROMOTING E–LEARNING IN INDIA." International Journal of Innovations & Research Analysis 04, no. 02(I) (2024): 136–42. http://dx.doi.org/10.62823/ijira/4.2(i).6601.
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Cloud computing platforms provide on-demand availability of computing resources over the internet. These platforms offer various services including data storage, processing power, and networking. Major cloud computing providers include Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and IBM Cloud. Cloud computing platforms have revolutionized various sectors, including education. In India, these platforms are playing a crucial role in promoting e-learning by providing scalable, flexible, and cost-effective solutions for educational institutions and learners. This research explores the impact of cloud computing on e-learning in India, highlighting its benefits, challenges, and future prospects.
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Sepideh, Sheikhi Nejad, Khademzadeh Ahmad, Masoud Rahmani Amir, and Broumandnia Ali. "Resource allocation for fog computing based on software-defined networks." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 7099–107. https://doi.org/10.11591/ijece.v13i6.pp7099-7107.
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With the emergence of cloud computing as a processing backbone for internet of thing (IoT), fog computing has been proposed as a solution for delay-sensitive applications. According to fog computing, this is done by placing computing servers near IoT. IoT networks are inherently very dynamic, and their topology and resources may be changed drastically in a short period. So, using the traditional networking paradigm to build their communication backbone, may lower network performance and higher network configuration convergence latency. So, it seems to be more beneficial to employ a software-defined network paradigm to implement their communication network. In software-defined networking (SDN), separating the network’s control and data forwarding plane makes it possible to manage the network in a centralized way. Managing a network using a centralized controller can make it more flexible and agile in response to any possible network topology and state changes. This paper presents a softwaredefined fog platform to host real-time applications in IoT. The effectiveness of the mechanism has been evaluated by conducting a series of simulations. The results of the simulations show that the proposed mechanism is able to find near to optimal solutions in a very lower execution time compared to the brute force method.
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Mitra, Manu. "Cloud Computing and its Features." ACTA Computer Sciences 2, no. 1 (2019): 1–3. https://doi.org/10.5281/zenodo.3560213.
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The use cloud computing is on demand availability of computer system resources for usage of computing power [1]. Cloud computing are divided in various subcategories based on requirements and physical location of computing resources. Few of cloud service models are 1) Software as a Service (SaaS) – it is a software transfer method that provides access to software and its functions remotely as a Web based service. 2) Platform as a Service (PaaS) – it is a computing platform being provided as a service. This is outsourced in place of a company or data center purchasing and managing its own hardware and software layers. 3) Infrastructure as a Service (IaaS) – computer infrastructure for instance, servers, storage and networking is provided as a service. IaaS is common with enterprises that have the convenience of having the cloud vendor manage their Information Technology infrastructure [2]. This editorial article gives brief insights about few from plenty of various cloud computing and its features.
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Chou, Chih-Lun, Gwo-Jiun Horng, Chieh-Ling Huang, and Wei-Chun Hsieh. "Multicriteria Resource Brokering in Cloud Computing for Streaming Service." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/823609.
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By leveraging cloud computing such as Infrastructure as a Service (IaaS), the outsourcing of computing resources used to support operations, including servers, storage, and networking components, is quite beneficial for various providers of Internet application. With this increasing trend, resource allocation that both assures QoS via Service Level Agreement (SLA) and avoids overprovisioning in order to reduce cost becomes a crucial priority and challenge in the design and operation of complex service-based platforms such as streaming service. On the other hand, providers of IaaS also concern their profit performance and energy consumption while offering these virtualized resources. In this paper, considering both service-oriented and infrastructure-oriented criteria, we regard this resource allocation problem as Multicriteria Decision Making problem and propose an effective trade-off approach based on goal programming model. To validate its effectiveness, a cloud architecture for streaming application is addressed and extensive analysis is performed for related criteria. The results of numerical simulations show that the proposed approach strikes a balance between these conflicting criteria commendably and achieves high cost efficiency.
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Alhazeem, Housam Ghanim. "Principles of Cloud Computing Infrastructure IaaS." Journal of engineering sciences and information technology 8, no. 2 (2024): 21–26. http://dx.doi.org/10.26389/ajsrp.a010123.
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Infrastructure as a Service (IaaS) represents a cornerstone in contemporary cloud computing, providing essential on-demand computing resources, including servers, storage, and networking. This paper explores the foundational principles of IaaS, highlighting its ability to facilitate dynamic server management through practices like auto-scaling and ephemeral server utilization. The use of custom images in IaaS ensures minimal downtime and scalable deployment, while loose coupling enhances fault tolerance and adaptability. Emphasizing high availability through auto-scaling, IaaS supports continuous operations even in geographically vulnerable areas. The security framework of IaaS, encompassing a shared responsibility model, ensures robust data protection and compliance with industry standards. The paper also addresses cost optimization strategies and the integration of hybrid cloud architectures to maximize resource efficiency and maintain data integrity. Overall, IaaS is integral to modern IT infrastructure, offering flexible, scalable, and cost-effective solutions that align with the evolving demands of the digital landscape.
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Husain, Nor Azmawati, Zulkefli Mohamad Hanapiyah, Mastura Roni, Norlela Abas, Ahmad Fadhly Arham, and Norshiba Norhisham. "Social Networking and Technological Entrepreneurial Intention: Demand-Resource Mechanism." Information Management and Business Review 16, no. 2(I) (2024): 111–24. http://dx.doi.org/10.22610/imbr.v16i2(i).3854.
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Social networking (SN) has changed the landscape of businesses, especially for entrepreneurs. Hence, this paper is conducted to identify the indirect effect of social networking in association with technological entrepreneurial intention (EI). The study was undertaken purposefully with two objectives. The first objective is determining the relationship between university support, risk, and student entrepreneurial intention. The second objective is related to the mediating role of social networking. This study utilized a quantitative form of research using a questionnaire as a data collection method. The population of this study is 300 technology entrepreneurship students from two different faculties: the Faculty of Plantation and Agrotechnology (FPA) and the College of Computing, Informatics & Mathematics (KPPIM). The response return is 224, which represents a 74.7% response rate. Data analysis utilized structural equation modeling in AMOS software to measure the effect among variables and answer all research questions. University support, risk, and social networking significantly and directly affect entrepreneurial intention among students. The mediating impact of social networking on the relationship between university support and entrepreneurial intention is significant. Social networking significantly and indirectly affects the relationship between risk and entrepreneurial intention. This recently proposed framework can explain the influences of resources (i.e., university support, social networking) and demand (i.e., risk) on the intention to become technological entrepreneurs.
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Nahro Kamal Saeed, Khalid A. Asaad, and Arkan A. Saffer. "Optimized Resource Allocation in Vehicular Fog Computing Environments Using Hybrid MOSP Algorithm." Zanco Journal of Pure and Applied Sciences 36, no. 6 (2024): 118–31. https://doi.org/10.21271/zjpas.36.6.13.
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Due to the appearance of new concepts such as fog computing and rapid progress toward the Internet of Vehicles (IOV), cloud computing becomes faced with the problem of resource allocation. Fog computing offers a solution by providing and offering computing storage and networking facilities near to the end-users and the connected devices. This work mainly focuses on the resource management for parked vehicles in via vehicular fog computing so as to improve resource utilization, QoS, delay, and energy consumption. The algorithm that is called MOSP and implemented the Multi-objective Grey Wolf Optimizer (MOGWO) solves the problem of allocating the resources for the parked and slow-moving vehicles taking into consideration the limitations concerning computation, storage, and mobility of the fog nodes. For the purpose of comparison, the performance of the proposed MOSP algorithm is compared with other approaches available in the literature. The evaluation of the performance has revealed the successful achievement of less energy consumption and considerable elimination of delays, which are critical issues in vehicular fog computing environments. This paper offers an original approach to resource management in V2V fog computing for parked cars through the employment of MOSP algorithm that enhances resource efficiency while enhancing QoS, delay, and energy consumption.
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Lamb, Zachary, and Dharma Agrawal. "Analysis of Mobile Edge Computing for Vehicular Networks." Sensors 19, no. 6 (2019): 1303. http://dx.doi.org/10.3390/s19061303.
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Vehicular ad-hoc Networks (VANETs) are an integral part of intelligent transportation systems (ITS) that facilitate communications between vehicles and the internet. More recently, VANET communications research has strayed from the antiquated DSRC standard and favored more modern cellular technologies, such as fifth generation (5G). The ability of cellular networks to serve highly mobile devices combined with the drastically increased capacity of 5G, would enable VANETs to accommodate large numbers of vehicles and support range of applications. The addition of thousands of new connected devices not only stresses the cellular networks, but also the computational and storage requirements supporting the applications and software of these devices. Autonomous vehicles, with numerous on-board sensors, are expected to generate large amounts of data that must be transmitted and processed. Realistically, on-board computing and storage resources of the vehicle cannot be expected to handle all data that will be generated over the vehicles lifetime. Cloud computing will be an essential technology in VANETs and will support the majority of computation and long-term data storage. However, the networking overhead and latency associated with remote cloud resources could prove detrimental to overall network performance. Edge computing seeks to reduce the overhead by placing computational resources nearer to the end users of the network. The geographical diversity and varied hardware configurations of resource in a edge-enabled network would require careful management to ensure efficient resource utilization. In this paper, we introduce an architecture which evaluates available resources in real-time and makes allocations to the most logical and feasible resource. We evaluate our approach mathematically with the use of a multi-criteria decision analysis algorithm and validate our results with experiments using a test-bed of cloud resources. Results demonstrate that an algorithmic ranking of physical resources matches very closely with experimental results and provides a means of delegating tasks to the best available resource.
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Scarpiniti, Michele, Enzo Baccarelli, and Alireza Momenzadeh. "VirtFogSim: A Parallel Toolbox for Dynamic Energy-Delay Performance Testing and Optimization of 5G Mobile-Fog-Cloud Virtualized Platforms." Applied Sciences 9, no. 6 (2019): 1160. http://dx.doi.org/10.3390/app9061160.
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It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emerging Mobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is a MATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering; and (v) its MATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared.
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Nejad, Sepideh Sheikhi, Ahmad Khademzadeh, Amir Masoud Rahmani, and Ali Broumandnia. "Resource allocation for fog computing based on software-defined networks." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 7099. http://dx.doi.org/10.11591/ijece.v13i6.pp7099-7107.
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<p><span lang="EN-US">With the emergence of cloud computing as a processing backbone for internet of thing (IoT), fog computing has been proposed as a solution for delay-sensitive applications. According to fog computing, this is done by placing computing servers near IoT. IoT networks are inherently very dynamic, and their topology and resources may be changed drastically in a short period. So, using the traditional networking paradigm to build their communication backbone, may lower network performance and higher network configuration convergence latency. So, it seems to be more beneficial to employ a software-defined network paradigm to implement their communication network. In software-defined networking (SDN), separating the network’s control and data forwarding plane makes it possible to manage the network in a centralized way. Managing a network using a centralized controller can make it more flexible and agile in response to any possible network topology and state changes. This paper presents a software-defined fog platform to host real-time applications in IoT. The effectiveness of the mechanism has been evaluated by conducting a series of simulations. The results of the simulations show that the proposed mechanism is able to find near to optimal solutions in a very lower execution time compared to the brute force method.</span></p>
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Szabo, Marton, David Hajay, and Mark Szalayz. "Cost-Efficient Resource Allocation Method for Heterogeneous Cloud Environments." Infocommunications journal, no. 1 (2018): 15–21. http://dx.doi.org/10.36244/icj.2018.1.3.
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In this paper we present a novel on-line NFV (Network Function Virtualization) orchestration algorithm for edge computing infrastructure providers that operate in a heterogeneous cloud environment. The goal of our algorithm is to minimize the usage of computing resources which are offered by a public cloud provider (e.g., Amazon Web Services), while fulfilling the required networking related constraints (latency, bandwidth) of the services to be deployed.We propose a reference network architecture which acts as a test environment for the evaluation of our algorithm. During the measurements, we compare our results to the optimal solution provided by an ILP-based solver.
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Javed, Zainab, and Waqas Mahmood. "A Survey Based Study on Fog Computing Awareness." International Journal of Information Technology and Computer Science 13, no. 2 (2021): 49–62. http://dx.doi.org/10.5815/ijitcs.2021.02.05.
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In this day and age, the rise in technological advancements has the potential to improve and transform our lives every day. The rapid technology innovation can have a great impact on our business operations. Currently, Cloud computing services are popular and offer a wide range of opportunities for their customers. This paper presents a survey on a more recent computing architecture paradigm known as Fog Computing. Fog networking is a beneficial solution that offers the greater facility of data storage, enhanced computing, and networking resources. This new concept of fog complements cloud solution by facilitating its customers with better security, real-time analysis improved efficiency. To get a clear picture and understanding of how fog computing functions, we have performed an extensive literature review. We also presented a comparative study of fog computing with cloud and grid computing architectures. In this study, we have conducted a survey that led us to the conclusion that fog computing solution is still not applicable and implemented in most of the IoT industries due to the lack of awareness and the high architecture’s cost. Results of the study also indicate that optimized data storage and security are a few of the factors that can motivate organizations to implement the Fog computing architecture. Furthermore, the challenges related to fog computing solution are reviewed for progressive developments in the future.
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Aluvalu, RajaniKanth, Vanraj Kamliya, and Lakshmi Muddana. "HASBE access control model with Secure Key Distribution and Efficient Domain Hierarchy for cloud computing." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (2016): 770. http://dx.doi.org/10.11591/ijece.v6i2.8919.
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Cloud computing refers to the application and service that run on a distributed system using virtualized resources and access by common internet protocol and networking standard. Cloud computing virtualizes system by pooling and sharing resources. System and resources can be monitored from central infrastructure as needed. It requires high security because now day’s companies are placing more essential and huge amount of data on cloud. Hence traditional access control models are not sufficient for cloud computing applications. So encryption based on Attribute (“ABE”-“Attribute based encryption”) has been offered for access control of subcontracted data in cloud computing with complex access control policies. Traditional HASBE provides Flexibility, scalability and fine-grained access control but does not support hierarchical domain structure. In this paper, we had enhanced “Hierarchical attribute-set-based encryption” (“HASBE”) access control with a hierarchical assembly of users, with flexible domain Hierarchy structure and Secure key distribution with predefined policy
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Aluvalu, RajaniKanth, Vanraj Kamliya, and Lakshmi Muddana. "HASBE access control model with Secure Key Distribution and Efficient Domain Hierarchy for cloud computing." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (2016): 770. http://dx.doi.org/10.11591/ijece.v6i2.pp770-777.
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Cloud computing refers to the application and service that run on a distributed system using virtualized resources and access by common internet protocol and networking standard. Cloud computing virtualizes system by pooling and sharing resources. System and resources can be monitored from central infrastructure as needed. It requires high security because now day’s companies are placing more essential and huge amount of data on cloud. Hence traditional access control models are not sufficient for cloud computing applications. So encryption based on Attribute (“ABE”-“Attribute based encryption”) has been offered for access control of subcontracted data in cloud computing with complex access control policies. Traditional HASBE provides Flexibility, scalability and fine-grained access control but does not support hierarchical domain structure. In this paper, we had enhanced “Hierarchical attribute-set-based encryption” (“HASBE”) access control with a hierarchical assembly of users, with flexible domain Hierarchy structure and Secure key distribution with predefined policy
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Amadeo, Marica, and Giuseppe Ruggeri. "Exploring In-Network Computing with Information-Centric Networking: Review and Research Opportunities." Future Internet 17, no. 1 (2025): 42. https://doi.org/10.3390/fi17010042.
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The advent of 6G networks and beyond calls for innovative paradigms to address the stringent demands of emerging applications, such as extended reality and autonomous vehicles, as well as technological frameworks like digital twin networks. Traditional cloud computing and edge computing architectures fall short in providing their required flexibility, scalability, and ultra-low latency. Cloud computing centralizes resources in distant data centers, leading to high latency and increased network congestion, while edge computing, though closer to data sources, lacks the agility to dynamically adapt to fluctuating workloads, user mobility, and real-time requirements. In-network computing (INC) offers a transformative solution by integrating computational capabilities directly into the network fabric, enabling dynamic and distributed task execution. This paper explores INC through the lens of information-centric networking (ICN), a revolutionary communication paradigm implementing routing-by-name and in-network caching, and thus emerging as a natural enabler for INC. We review state-of-the-art advancements involving INC and ICN, addressing critical topics such as service naming, executor selection strategies, compute reuse, and security. Furthermore, we discuss key challenges and propose research directions for deploying INC via ICN, thereby outlining a cohesive roadmap for future investigation.
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Sarkar, Mekhla, and Prasan Kumar Sahoo. "Leveraging Edge Computing for Video Data Streaming in UAV-Based Emergency Response Systems." Sensors 24, no. 15 (2024): 5076. http://dx.doi.org/10.3390/s24155076.
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The rapid advancement of technology has greatly expanded the capabilities of unmanned aerial vehicles (UAVs) in wireless communication and edge computing domains. The primary objective of UAVs is the seamless transfer of video data streams to emergency responders. However, live video data streaming is inherently latency dependent, wherein the value of the video frames diminishes with any delay in the stream. This becomes particularly critical during emergencies, where live video streaming provides vital information about the current conditions. Edge computing seeks to address this latency issue in live video streaming by bringing computing resources closer to users. Nonetheless, the mobile nature of UAVs necessitates additional trajectory supervision alongside the management of computation and networking resources. Consequently, efficient system optimization is required to maximize the overall effectiveness of the collaborative system with limited UAV resources. This study explores a scenario where multiple UAVs collaborate with end users and edge servers to establish an emergency response system. The proposed idea takes a comprehensive approach by considering the entire emergency response system from the incident site to video distribution at the user level. It includes an adaptive resource management strategy, leveraging deep reinforcement learning by simultaneously addressing video streaming latency, UAV and user mobility factors, and varied bandwidth resources.
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Baghrous, Mohamed, Abdellatif Ezzouhairi, Mouhajir Manal, Zerifi Manare, Rabah Yahya, and Youssef Errafik. "A module placement scheme for fog-based smart farming applications." A module placement scheme for fog-based smart farming applications, no. 6 (December 1, 2023): 7089–98. https://doi.org/10.11591/ijece.v13i6.pp7089-7098.
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As in Industry 4.0 era, the impact of the internet of things (IoT) on the advancement of the agricultural sector is constantly increasing. IoT enables automation, precision, and efficiency in traditional farming methods, opening up new possibilities for agricultural advancement. Furthermore, many IoT-based smart farming systems are designed based on fog and edge architecture. Fog computing provides computing, storage, and networking services to latency-sensitive applications (such as Agribots-agricultural robots-drones, and IoT-based healthcare monitoring systems), instead of sending data to the cloud. However, due to the limited computing and storage resources of fog nodes used in smart farming, designing a modules placement scheme for resources management is a major challenge for fog based smart farming applications. In this paper, our proposed module placement algorithm aims to achieve efficient resource utilization of fog nodes and reduce application delay and network usage in Fog-based smart farming applications. To evaluate the efficacy of our proposal, the simulation was done using iFogSim. Results show that the proposed approach is able to achieve significant reductions in latency and network usage
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Bouchair, Abderrahim, Belabbas Yagoubi, and Sid Ahmed Makhlouf. "A Driven Modern Portfolio Theory for Virtual Network Embedding in SDN-Enabled Cloud." Scalable Computing: Practice and Experience 23, no. 4 (2022): 421–39. http://dx.doi.org/10.12694/scpe.v23i4.2010.
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Network virtualization (NV) has evolved systematically through the urge to share computing resources and improve service deployment in a large-scale environment. Virtual network embedding (VNE) is a well-established technology applied to reinforce the NV process, providing a devoted implementation for a particular case study. In cloud computing, integration of software-defined networking (SDN) has proved to be a practical support to the principal cloud utilities. In return, the SDN-enabled cloud offers innovative deployment techniques for network-based services, which increase the opportunity to efficiently incorporate new network management policies that solve the VNE problem. In this paper, the authors proposed a transition of modern portfolio theory (MPT) into a VNE approach that optimally addresses the selection and ranking of resources in data center networks (DCNs). Results analysis demonstrates the VNE approach's better performance versus similar methods in terms of acceptance ratio, runtime, and substrate resource utilization.
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Sfiligoi, Igor, John Graham, and Frank Wuerthwein. "Characterizing network paths in and out of the clouds." EPJ Web of Conferences 245 (2020): 07059. http://dx.doi.org/10.1051/epjconf/202024507059.
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Commercial Cloud computing is becoming mainstream, with funding agencies moving beyond prototyping and starting to fund production campaigns, too. An important aspect of any scientific computing production campaign is data movement, both incoming and outgoing. And while the performance and cost of VMs is relatively well understood, the network performance and cost is not. This paper provides a characterization of networking in various regions of Amazon Web Services, Microsoft Azure and Google Cloud Platform, both between Cloud resources and major DTNs in the Pacific Research Platform, including OSG data federation caches in the network backbone, and inside the clouds themselves. The paper contains both a qualitative analysis of the results as well as latency and peak throughput measurements. It also includes an analysis of the costs involved with Cloud-based networking.