A Review on Fog Computing for the Internet of Things

Javed, Waheed; Parveen, Gulnaz; Aabid, Fatima; Rubab, Syeda Um e; Ikram, Sidra; Rehman, Khawaja Ubaid ur; Danish, Muhammad

doi:10.1109/icic53490.2021.9692966

Cited by 8 publications

(3 citation statements)

References 50 publications

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“…It is the method of choice for solving the optimization problem. In [14] introduced the Multi-Objective Task Scheduling algorithm, which is based on providing efficient resource utilization to improve throughput. This technique reduces the execution time in software as a service (SaaS).…”

Section: Related Workmentioning

confidence: 99%

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DLBRT: Dynamic Load-Balance Real-Time Scheduling Model in Fog-Cloud Colony

احمد¹

2022

النشرة المعلوماتیة فی الحاسبات والمعلومات

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In recent years, the massive growing of the usage of Internet of Things (IoT) applications expose different challenges in centralized cloud computing paradigm such as; network failure and inadmissible latency for real-time services. The fog layer is added to address these challenges. The nodes in fog computing layer are similar to cloud node, except that it is nearby the IoT devices. Fog node is added to supply IoT devices with the desired resources with low delay. Hence, the performance of IoT application is based on the task scheduling strategy in cloud, or fog computing systems. Hence, the scheduling strategy should maximize resource utilization and increase the resources availability. Most of the previous scheduling methods are suffer from centralization, which consequently represents a performance bottleneck and a single point of failure. Also, some of these scheduling methods ignore the urgency type of the services, which consequently will not be suitable for real-time services. This paper proposes a new load balancing model, which has two main features. The first feature is the dynamic allocation quota for the resource allocation. The second feature is decentralization in fog resource management. In another word, DLBRT manages the IoT service requests based on the service urgency level with maintaining the load among the fog node balanced. The dynamic load balancing strategy in DLBRT is based on redistribution of the less-urgent service requests to the lowest fog node load. In another word, the redistribution of the service requests is depending on the urgency of the real-time service and the workload in every fog. Finally, a simulation model is created to evaluate DLBRT in a fog-cloud colony. Also, the proposed scheduling model is examined with four scheduling models, namely; The FCFS, Max-Min, Real-Time Efficient Scheduling (RETS), and Based Autonomic Task Scheduling (PBATS). We demonstrate through thorough simulations that the performance metrics of turnaround time, waiting time, throughput, and task failure test are enhanced by our suggested approach.

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Section: Related Workmentioning

confidence: 99%

“…It can handle large-scale distributed devices and systems while also supporting their heterogeneity. Consequently, as illustrated in Figure 1, the fog-cloud model reduces network delay and, as a result, energy consumption for all nodes in fog layers [13,14].…”

Section: Introductionmentioning

confidence: 99%

DLBRT: Dynamic Load-Balance Real-Time Scheduling Model in Fog-Cloud Colony

احمد¹

2022

النشرة المعلوماتیة فی الحاسبات والمعلومات

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“…Additionally, the paper highlighted the sustained necessity for research and development in IoT system classification and robust multi-platform support, stressing these elements as crucial for the future evolution and efficiency of IoT technologies. (Javed et al, 2021) This comprehensive review, set in the backdrop of IoT's rapid expansion, thoroughly examined the integration challenges posed by traditional cloud computing, particularly issues like latency, space constraints, and network failures. Fog computing emerged as a strategic solution, bridging the gap with its proximity to data sources, thus facilitating faster and superior-quality outcomes.…”

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confidence: 99%

Navigating the Landscape of IoT, Distributed Cloud Computing: A Comprehensive Review

A. H. Alkurdi,

R. M. Zeebaree

2024

ACAD J NAWROZ UNIV

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This comprehensive academic exploration delves into the revolutionary convergence of the Internet of Things (IoT) with distributed cloud computing, redefining the realms of data processing, storage, and communication. The paper critically analyzes scholarly work from reputable journals, providing profound insights into this integration's multifaceted applications and underlying technological frameworks. The relevance of IoT, a network of interconnected devices and sensors, is emphasized through its significant impact on diverse sectors, including healthcare, education, agriculture, and smart cities. This impact is magnified by its extensive data collection, processing, and analysis capabilities, enabled through cloud computing platforms. The objective of the paper is to methodically compare and contrast contemporary scholarly contributions, shedding light on the diverse applications and technological infrastructures of IoT in conjunction with distributed cloud computing. This endeavor encompasses an examination of IoT-based cloud infrastructure, detailed analysis of specific needs, implementations, and applications of IoT-based cloud computing, and a review of various IoT cloud platforms. The paper also highlights the benefits of integrating IoT with cloud computing, elucidating significant advantages and potential future directions of this technology. Through this scholarly inquiry, the paper aims to offer an in-depth perspective on the state-of-the-art developments in IoT and distributed cloud computing. It underscores their significance and potential in shaping the future of digital technology and its applications across various domains.

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Fog Computing

Swarnakar

2024

Fog Computing for Intelligent Cloud IoT Systems

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A Review on Fog Computing for the Internet of Things

Cited by 8 publications

References 50 publications

DLBRT: Dynamic Load-Balance Real-Time Scheduling Model in Fog-Cloud Colony

DLBRT: Dynamic Load-Balance Real-Time Scheduling Model in Fog-Cloud Colony

Navigating the Landscape of IoT, Distributed Cloud Computing: A Comprehensive Review

Fog Computing

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