Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

Choi, Jonghwa; Ahn, Sanghyun

doi:10.3390/s21041506

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Choi et al 30 proposed two approaches for provisioning service images on fog/edge cells that called the maximal coverage‐service provisioning (MC‐SP) mechanism and the flexible coverage‐service provisioning (FC‐SP) mechanism. The MC‐SP mechanism provisions service images on fog/edge cells based on the number of service requests from end devices and the FC‐SP mechanism based on end devices placement.…”

Section: Related Workmentioning

confidence: 99%

Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

2022

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Internet of Things (IoT) represents a new generation of information and communication technology for anyone, anytime and anywhere. Cloud service‐based IoT applications significantly increase latency and network utilization. The fog environment is closer to the user to perform computing, communication, and storage tasks on network edge devices. Therefore, it can greatly reduce the latency of real‐time applications. It is an essential feature of fog computing and its most important advantage compared to cloud computing. This study proposed a new approach to service placement generated by running applications on IoT devices in the fog computing. IoT devices send applications to the fog environment that each application contains a set of services. The purpose of solving the IoT services placement problem is to efficiently deploy these services on fog cells. For this purpose, it is assumed that the received services from the IoT applications are received as a directed acyclic graph that depicts the communication between the cells within the graph that shows the communication between the services. Then, the imperialist competitive algorithm is used to place and select the destination for IoT services. The simulation results of the iFogSim simulator in different experiments showed that the imperialist competitive algorithm with the proposed graph partitioning approach has improved service placement on the fog infrastructure compared to the genetic algorithm and best‐fit algorithm.

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Section: Related Workmentioning

confidence: 99%

Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

2022

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“…This protocol relies on bilinear pairing cryptography for the generation of the session that ensures secure communication between the communicating parties. A new framework that facilitates remote sensing and monitoring in real-time for making prognosis and diagnosis was presented [29]. The major factors in managing IoT systems in health care are computation offloading, interoperability, and load balancing/distribution.…”

Section: B Related Workmentioning

confidence: 99%

A Fog Computing Framework in IoT Healthcare Environment: Towards A New Method Based on Tasks Significance

Shanshool¹,

Abbas²

2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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The Internet-of-Things (IoT) is an important technology and is considered the future of the Internet. Healthcare is described as one of the important areas in IoT used for remote patient monitoring. Real-time remote monitoring health applications are important as delays in data transfer between the cloud, and the application may be unacceptable. Fog computing refers to a geographically distributed computing system with several devices connected to the same network to achieve flexible and collaborative computation, storage, and communication services. Fog computing is mainly used for efficient data processing between sensors and cloud computing as it reduces the volume of data exchanged between sensors and the cloud, thereby improving the whole system's efficiency. Wireless sensor networks (WSN) are also used in health monitoring systems to simultaneously transfer huge data volumes (of different priority levels and length values) to the fog computing system. Hence, there is a need to appropriately implement a task scheduling mechanism that can accurately prioritize tasks irrespective of their length. This study aims to systematically review the existing fog computing technologies in the Internet of things HealthCare (IoTH) systems and improve the performance of the available static task scheduling algorithms using the Tasks Classification (TC) method where task importance is paramount. The performance of the suggested approach was evaluated based on the Max-Min scheduling algorithm (SA).

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“…Meanwhile, at the edge layer, processes that require fewer resources are kept. Such sharing solves the problems of response time and data congestion that were common when only using the cloud layer [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Availability of an RFID Object-Identification System in IoT Environments

Corches

Daraban

Miclea

2021

Sensors

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Through the latest technological and conceptual developments, the centralized cloud-computing approach has moved to structures such as edge, fog, and the Internet of Things (IoT), approaching end users. As mobile network operators (MNOs) implement the new 5G standards, enterprise computing function shifts to the edge. In parallel to interconnection topics, there is the issue of global impact over the environment. The idea is to develop IoT devices to eliminate the greenhouse effect of current applications. Radio-frequency identification (RFID) is the technology that has this potential, and it can be used in applications ranging from identifying a person to granting access in a building. Past studies have focused on how to improve RFID communication or to achieve maximal throughput. However, for many applications, system latency and availability are critical aspects. This paper examines, through stochastic Petri nets (SPNs), the availability, dependability, and latency of an object-identification system that uses RFID tags. Through the performed analysis, the optimal balance between latency and throughput was identified. Analyzing multiple communication scenarios revealed the availability of such a system when deployed at the edge layer.

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Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

Cited by 8 publications

References 20 publications

Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

A Fog Computing Framework in IoT Healthcare Environment: Towards A New Method Based on Tasks Significance

Availability of an RFID Object-Identification System in IoT Environments

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