Distributed Information and Control system reliability enhancement by fog-computing concept application

Melnik, E. V.; Klimenko, Anna; Ivanov, Donat

doi:10.1088/1757-899x/327/2/022070

Cited by 7 publications

(4 citation statements)

References 4 publications

(6 reference statements)

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“…Overhead costs and load balancing to improve fault tolerance by optimizing the probability of failure-free operation are considered by the authors in [8]. The authors conclude that for an information and control system processing distributed data, a distributed structure is better suited (in terms of the uptime probability).…”

Section: моделирование систем и процессовmentioning

confidence: 99%

Model of a distributed information system solving tasks with the required probability

Gryzunov

2022

ICS

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Introduction: Distributed in space-time Networks: IIoT and IoT, fog and edge computing tend to penetrate into all spheres of human activity. Enterprises, government, law enforcement agencies, etc. depend on the quality of those technologies. Purpose: To determine the composition of the Network that provides the required uptime probability. Methods: According to the concept of structural and functional synthesis, a distributed Network is presented as an unstable queuing system in which servicing devices are connected and disconnected at an arbitrary point in time. A simulation model of the Network has been built. Results: The state of the Network depends on the number of devices and tasks, their performance and lifetimes. The model does not use these quantities themselves, but their ratios. The values of the uptime probability of the Network are calculated for all possible combinations of ratios. The confidence interval has been calculated with a confidence level of 0.95. From the data obtained, it is clear: 1) what should be the minimum composition of the Network in order to provide the required probability; 2) what probability the current composition of the Network can provide; 3) what flow of tasks is admissible in order to solve tasks with the required probability. It is shown that the dependence of the mean tasks residence time on the Network on the composition of the Network has two inflection points. Using information about these points, the Network Management System forms pools of devices or increases the number of devices. Discussion: It is assumed that the Net has a fully connected structure. Consequently, for practical application, it is necessary: to expand the model with an adjacency matrix describing the connections between nodes, and hence the paths of propagation of tasks over the Network or consider that each node is a relay and is capable of transmitting the task to any other node on the Network. Overhead costs arising from this are taken into account by adjusting the original data. Practical relevance: The results allow minimizing costs in the design and operation of distributed systems, maximizing the likelihood of system uptime under given constraints for resource.

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Section: моделирование систем и процессовmentioning

confidence: 99%

Model of a distributed information system solving tasks with the required probability

Gryzunov

2022

ICS

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“…Fog Computing has wide usage in our live and easy to implementation, especially in the industry. A study proposed a model for deploying gateways [111] to minimize the overall installation expense under the limits of maximum specifications, maximum duration, coverage, and maximum computer capacities. In addition, two meta-heuristic algorithms were proposed to solve the NP-hard problems: the discrete monkey algorithm to search for good quality solutions and the genetic algorithm to increase computational efficiency.…”

Section: Cost Optimizationmentioning

confidence: 99%

Fog Computing Advancement: Concept, Architecture, Applications, Advantages, and Open Issues

et al. 2021

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Internet of Things (IoT) is proliferating in our real world, and it is a promising technology that serves a very comfortable service to the users. IoT's underlying technology is to connect to central Cloud Computing (CC), which is a huge data center collecting the generated data by IoT devices, and it is located in different areas on demand. However, cloud computing lacks data transmission because of the infrastructure and limitations of networks which enormously decrease its performance. Therefore, a new paradigm has been founded to act as a middleware between the Cloud and IoT, termed Fog Computing (FC) Technology. Considering Fog as a cloud extension that provides computing service at the edge of the network, Fog Computing placement enables this technology to deal with numerous data locally. In this study, we surveyed Fog computing with in-depth analysis and covered the latest studies to address and overcome the existing challenges in FC. We reviewed fog computing technology conceptually and defined it based on the existing studies in the literature, together with its architecture, applications, advantages, and open issues with optimization methods being performed to obtain the optimal services. INDEX TERMS Fog Computing Applications, Fog Computing Concept, Fog Computing Open Issues, Cloud computing, Internet of Things (IoT).

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“…Melnik et al have designed an SA-aware RPS strategy in edge computing. 123 The SA technique is used to schedule the applications and finds suitable edge devices with minimum communication and processing time for those applications. The objectives of this strategy are to minimize the processing delay and cost of the incoming applications.…”

Section: Resource Placement and Schedulingmentioning

confidence: 99%

A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

2021

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Driven by the vision of real-time applications and smart communication, recent years have witnessed a paradigm shift from centralized cloud computing toward distributed edge computing. The main features of edge computing are to drag the cloud services toward the network edge with dramatic reductions of latency while increasing the resource utilization of the network and computing devices. Being the natural extension of cloud computing, edge computing inherits a variety of research challenges and brings forth different new issues to solve.These challenges are dealing with solving complex optimization problems including scheduling and processing real-time applications. Nature-inspired meta-heuristic (NIMH) algorithm is an overarching term in the field of an optimization problem that provides robust solutions to the NP-complete problems, from computationally tractable approximate solutions to real-time optimization strategies. Nowadays, different NIMH algorithms have been applied in the field of edge computing for solving various research challenges including resource placement and scheduling, communication, mobility, and edge controlling with higher efficiency. In this survey, we classify the existing NIMH into three categories based on their nature of works and included fuzzy logic and systems in the field of edge networks along with different research challenges. Further, we introduce different challenges and future directions to identify promising research works in edge computing.

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Distributed Information and Control system reliability enhancement by fog-computing concept application

Cited by 7 publications

References 4 publications

Model of a distributed information system solving tasks with the required probability

Model of a distributed information system solving tasks with the required probability

Fog Computing Advancement: Concept, Architecture, Applications, Advantages, and Open Issues

A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

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