IoT Data Replication and Consistency Management in Fog Computing

Naas, Mohammed; Lemarchand, Laurent; Raipin, Philippe; Boukhobza, Jalil

doi:10.1007/s10723-021-09571-1

Cited by 22 publications

(7 citation statements)

References 37 publications

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“…As you know, it processes IoT services near data sources and data sinks, which reduces communication latency and optimizes the utilization of computational storage resources and network resources. 6,10 Therefore, efficient solutions are needed to provide resources, virtualization, and how to distribute these services over resources available in fog. 11 In general, it is possible to place service and resources in fog environments that properly understand its architecture and its infrastructure connections (Figure 1).…”

Section: Iot Service Placement Problemmentioning

confidence: 99%

“…For each application, we consider the response time and the deadline, so we can calculate the latency in providing the deadline as Equation (10).…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Fog computing is a new topic in processing IoT applications, and it is a highly virtualized platform capable of providing services such as processing, storage, and services between end devices and traditional cloud computing data centers. As you know, it processes IoT services near data sources and data sinks, which reduces communication latency and optimizes the utilization of computational storage resources and network resources 6,10 . Therefore, efficient solutions are needed to provide resources, virtualization, and how to distribute these services over resources available in fog 11 .…”

Section: Introductionmentioning

confidence: 99%

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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: Iot Service Placement Problemmentioning

confidence: 99%

“…For each application, we consider the response time and the deadline, so we can calculate the latency in providing the deadline as Equation (10).…”

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…The authors [4] use the delay time of the Network's response to a service request by the user. It is proposed to reduce latency by replicating da-…”

Section: Research Overviewmentioning

confidence: 99%

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

Gryzunov

2022

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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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“…Gesto [17] is an causal consistency model of edge-cloud storage that combines high throughput and low migration latency, reducing metadata overhead for system management, and client migration latency by using multi-part timestamp. In 2021, MI Naas [18] et al proposed two strategies to manage IoT data replication and consistency in fog infrastructure. Their strategy selects the correct replica number and its location for each data to reduce data access latency and replica synchronization costs.…”

Section: Introductionmentioning

confidence: 99%

CCECGP: causal consistency model of edge–cloud collaborative based on grouping protocol

2022

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At present, most causal consistency models based on cloud storage can no longer meet the needs of delay-sensitive applications. Moreover, the overhead of data synchronization between replicas is too high. This paper proposes a causal consistency model of edge-cloud collaborative based on grouping protocol. The model based on the edge-cloud collaboration architecture, partitions cloud data centers and groups edge nodes by distributed hash tables, and stores a subset of the complete data set in nodes located at the edge of the network. Thereby realize partial geo-replication in edge-cloud collaboration environment. At the same time, we design a group synchronization algorithm called Imp_Paxos, so that the update only needs to be synchronized to the main group, which reduces the visibility delay of the update and decreases the data synchronization overhead.Besides, a sort timestamp is proposed in this paper, which generates different timestamps according to the type of update to track causality, keeping the amount of metadata managed in a relatively stable and low state.Threrfore, the proposed model reduces the overhead of metadata for system management, and improves throughput quantity of system. Experiments show that, our model performs well in terms of throughput, operation latency, and update visibility latency compared with existing causal consistency models.

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IoT Data Replication and Consistency Management in Fog Computing

Cited by 22 publications

References 37 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

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

CCECGP: causal consistency model of edge–cloud collaborative based on grouping protocol

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