List of Articles Fog computing

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  1 - Workflow Scheduling on Hybrid Fog-Cloud Environment Based on a Novel Critical Path Extraction Algorithm
  Fatemeh Davami Sahar Adabi Ali Rezaee Amir Masoud Rahamni
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  2 - Intelligent Resource Allocation in Fog Computing: A Learning Automata Approach
  Alireza Enami Javad Akbari Torkestani
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  3 - Energy-aware and Reliable Service Placement of IoT applications on Fog Computing Platforms by Utilizing Whale Optimization Algorithm
  Yaser Ramzanpoor Mirsaeid Hosseini Shirvani Mehdi GolSorkhTabar
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  4 - An Autonomous Planning Model for Deploying IoT Services In Fog Computing
  Mansoureh Zare Yasser Elmi sola Hesam Hasanpour
  10.30495/jce.2023.1975916.1186
  IoT-based devices are constantly sending data to the cloud. However, the centralization of cloud data centers and the long distance to the location of data sources has reduced the efficiency of this paradigm in real-time applications. Fog computing can provide the resou More

  IoT-based devices are constantly sending data to the cloud. However, the centralization of cloud data centers and the long distance to the location of data sources has reduced the efficiency of this paradigm in real-time applications. Fog computing can provide the resources needed by Internet of Things devices in a distributed manner at the edge of the network without involving the cloud. Therefore, processing, analysis and storage are closer to the source of data and end users cause the delay is reduced. Every Internet of Things program includes a set of Internet of Things services with different quality of service requirements, whose required resources can be provided by deploying on cloud nodes. This study deals with the challenge of locating Internet of Things services as an autonomous planning model in fog computing. We develop the colonial competition algorithm as a meta-heuristic approach to solve this problem. Since fog nodes with enough resources can host several IoT services, we consider resource distribution in the localization process. The proposed algorithm prioritizes Internet of Things services to reduce delay and solves the multi-objective positioning problem. The results of the experiments show that our algorithm can effectively improve the performance of the system and have 15% to 31% better effectiveness than the best results of the advanced algorithms in the literature. Manuscript profile
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  5 - Solving the Multi-Objective Problem of IoT Service Placement in Fog Computing Using Reinforcement Learning Approaches
  Mani Zarei Zahra Saadati
  Introduction: The data generated in the Internet of Things (IoT) ecosystem requires continuous and timely processing. Transferring generated data to cloud data centers is costly and unsuitable for real-time applications. To increase the speed of service delivery, resour More

  Introduction: The data generated in the Internet of Things (IoT) ecosystem requires continuous and timely processing. Transferring generated data to cloud data centers is costly and unsuitable for real-time applications. To increase the speed of service delivery, resources should be placed as close as possible to the user, i.e. at the edge of the network. A new paradigm called fog computing was introduced and added as a layer in the IoT architecture to meet this challenge. Fog computing provides the processing and storage of IoT data locally in the vicinity of IoT devices rather than in the cloud. Fog computing can provide less latency and better service quality for real-time applications than cloud computing. In general, there are theoretical foundations for fog computing, but the issue of locating IoT services to fog nodes remains a challenge and has attracted a great deal of research. Method: In this research, a conceptual computing framework based on cloud-fog control software is proposed to optimally locate IoT services. The proposed model is formulated as an autonomous planning model for managing service requests due to some constraints, considering the heterogeneity of programs and resources. To solve the problem of locating IoT services, an autonomous evolutionary approach based on enhanced learning approaches has been proposed with the aim of making maximum use of fog-based resources and improving service quality. A heterogeneous advantage operator-criterion algorithm is used as a new reinforcement learning approach aimed at maximizing long-term cumulative reward. Results: The results of the comparisons showed that the proposed reinforcement learning-enabled framework performs better than the advanced methods of the literature. The results of the proposed method compared to FSP-ODMA, SPP-GWO, CSA-FSPP, and GA-FSP methods indicate 4.6%, 2.4%, 3.4%, and 1.1% improvement, respectively. Discussion: Experimental studies were performed on a simulated artificial environment based on various metrics including fog usage, services performed, response time, and service delay. The proposed reinforcement learning-enabled framework outperforms the previous works and shows better scalability. Analysis of parallel heuristic algorithms to find a more accurate localization than evolutionary approaches is another aspect of future work. We intend to consider new reinforcement learning approaches such as the Asynchronous Advantage Actor Critic (A3C) algorithm along with the long-term cumulative reward maximization policy for locating services. Also, future efforts will explore reinforcement learning approaches for failure recovery towards Cloud-Fog-IoT architecture, where parallel processing architecture of IoT services can be considered in the location process. Manuscript profile
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  6 - A Reinforcement Learning Method for Joint Minimization of Energy Consumption and Delay in Fog Computing
  Reza Besharati Mohammad Hossein Rezvani Mohammad Mehdi Gilanian Sadeghi
  10.22094/jcr.2022.1963008.1274
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  7 - Increase the Efficiency of the Offloading Algorithm in Fog Computing by Particle Swarm Optimization Algorithm
  Seyed Ebrahim Dashti Hoasain Zare
  Edge computing is a computing paradigm that extends cloud services to devices at the edge. This processing model refers to technologies that allow computing and storage to be performed on devices at the edge of the network. In this architecture, computing and storage op More

  Edge computing is a computing paradigm that extends cloud services to devices at the edge. This processing model refers to technologies that allow computing and storage to be performed on devices at the edge of the network. In this architecture, computing and storage operations take place close to objects and data sources. In order to reduce latency and network traffic between end devices and cloud centers, groups at the edge have processing capabilities, perform a large number of processing and computing tasks, including data processing, temporary storage, device management, decision making, and privacy protection. Since the number of edge devices is large, there must be a mechanism to select these tasks and offload them to the cloud. The problem to be decided is that which one of the available edge devices should be selected for unloading and then unloaded. This problem is classified as one of the hard non-polynomial problems and by using deterministic algorithms simply and in polynomial time, it is not possible to find a suitable and efficient solution for it found. Manuscript profile
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  8 - Task Scheduling in Fog Computing: A Survey
  Abbas Najafizadeh Afshin Salajegheh Amir Masoud Rahmani Amir Sahafi
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  9 - Application of Big Data Analytics in Power Distribution Network
  Foroogh Sedighi Mohammadreza Jabbarpour Sheyda Seyedfarshi
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