A Computing Resource Allocation Optimization Strategy for Massive Internet of Health Things Devices Considering Privacy Protection in Cloud Edge Computing Environment

Wang, Jianxi; Wang, Liutao

doi:10.1007/s10723-021-09558-y

Cited by 20 publications

(6 citation statements)

References 31 publications

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“…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). In the most general and comprehensive architecture, fog computing includes (a) fog colonies (i.e., fog cells, configuration control nodes, IoT devices) and (b) cloud and fog control firmware.…”

Section: Iot Service Placement Problemmentioning

confidence: 99%

“…We calculated this metric using simulation of link links between sources. Cloud computing efficiency (computational fog) is calculated by Equation (11).…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…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: 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%

“…We calculated this metric using simulation of link links between sources. Cloud computing efficiency (computational fog) is calculated by Equation (11).…”

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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“…Wang et al [17] proposed an optimization approach to compute resource allocation of huge IoHT devices to consider privacy protection from cloud EC platforms. Initially, a 5G heterogeneous cloud EC network is created.…”

Section: Literature Reviewmentioning

confidence: 99%

Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

Alhebaishi¹,

Alshareef²,

Hasanin³

et al. 2022

Computers, Materials &Amp; Continua

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In recent times, internet of things (IoT) applications on the cloud might not be the effective solution for every IoT scenario, particularly for time sensitive applications. A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices. Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge. One of the considerations of the edge computing environment is resource management that involves resource scheduling, load balancing, task scheduling, and quality of service (QoS) to accomplish improved performance. With this motivation, this paper presents new soft computing based metaheuristic algorithms for resource scheduling (RS) in the edge computing environment. The SCBMA-RS model involves the hybridization of the Group Teaching Optimization Algorithm (GTOA) with rat swarm optimizer (RSO) algorithm for optimal resource allocation. The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way, that the client's necessities are satisfied with the minimum number of possible resources and optimal energy consumption. The problem is formulated based on the availability of VMs, task characteristics, and queue dynamics. The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center. For experimental validation, a comprehensive set of simulations were performed using the CloudSim tool. The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

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“…In order to improve the user experience and reduce latency, Xu et al [3] proposed an enhanced service framework based on microservice management and a client support provider for efficient user experiments in the edge computing environment, in order to provide the edge computing service and management in the network edge. Wang et al [4] proposed an optimization strategy for computing resource allocation in massive IoT devices considering privacy protection in a cloud edge computing environment. Since quality of service (QoS) can be affected by the choice of fog nodes and wireless transmission, Zhao et al [5] proposed a novel data analytics framework for edge computing.…”

Section: Related Workmentioning

confidence: 99%

Self-Adaptive Learning of Task Offloading in Mobile Edge Computing Systems

Huang

Deng

Kang

et al. 2021

Entropy

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Mobile edge computing (MEC) focuses on transferring computing resources close to the user's device, and it provides high-performance and low-delay services for mobile devices. It is an effective method to deal with computationally intensive and delay-sensitive tasks. Given the large number of underutilized computing resources for mobile devices in urban areas, leveraging these underutilized resources offers tremendous opportunities and value. Considering the spatiotemporal dynamics of user devices, the uncertainty of rich computing resources and the state of network channels in the MEC system, computing resource allocation in mobile devices with idle computing resources will affect the response time of task requesting. To solve these problems, this paper considers the case in which a mobile device can learn from a neighboring IoT device when offloading a computing request. On this basis, a novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system. SAda adopts a distributed working mode and has a perception function to adapt to the dynamic environment in reality; it does not require frequent access to equipment information. Extensive simulations demonstrate that SAda achieves preferable latency performance and low learning error compared to the existing upper bound algorithms.

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A Computing Resource Allocation Optimization Strategy for Massive Internet of Health Things Devices Considering Privacy Protection in Cloud Edge Computing Environment

Cited by 20 publications

References 31 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

Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

Self-Adaptive Learning of Task Offloading in Mobile Edge Computing Systems

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