Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures

Santos, Guto Leoni; Endo, Patrícia Takako; Tigre, Matheus Felipe Ferreira da Silva Lisboa; Silva, Leylane Graziele Ferreira da; Sadok, Djamel; Kelner, Judith; Lynn, Theo

doi:10.1186/s13677-018-0118-3

Cited by 54 publications

(26 citation statements)

References 49 publications

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“…Thanks to the rapid progress of wireless communications and wearable devices, the E-Health Monitoring (EHM) System has become a paradigm of IoT applications [13][14][15][16]. A typical EHM system is composed of an IoT sensing subsystem, networking subsystem, cloud data processing and storage subsystem.…”

Section: Motivation Scenariomentioning

confidence: 99%

“…In [14][15][16], the IoT, fog, edge and cloud integrated architecture for EHM system was clearly illustrated. The performance, including the latency, availability, and the potential challenges in EHM systems, were addressed in [13][14][15] respectively. A common characteristic in the EHM system is that the data flow starting from the IoT devices will travel across the fog layer and edge layer before it reaches the cloud data center.…”

Section: Motivation Scenariomentioning

confidence: 99%

“…On the contrary the optimization problem introduced in above studies is one-stage optimization problem. The typical CDF application in IoT-Fog-Edge computing is the E-Health Monitoring System [13][14][15][16], which will be elaborated as the Motivation Scenario in section II. Some efforts were devoted to the performance measurement and optimization of E-Health system [13][14][15][16] from the perspective of architecture and deployment optimization.…”

mentioning

confidence: 99%

“…The typical CDF application in IoT-Fog-Edge computing is the E-Health Monitoring System [13][14][15][16], which will be elaborated as the Motivation Scenario in section II. Some efforts were devoted to the performance measurement and optimization of E-Health system [13][14][15][16] from the perspective of architecture and deployment optimization. To the best of our knowledge, there is little work required to optimize the performance of E-Health monitoring system from the perspective of mathematical modelling and programming.To optimize the energy consumption of IoT devices while subject to the latency constraints, the anomaly should be discovered because the anomalous nodes would cause abnormal latencies and job loss or task failure on transmission paths [17,18].…”

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confidence: 99%

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Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Luo

Qiu

2019

Sensors

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The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since the anomalous nodes in the network will cause the increase of energy consumption, it is necessary to make continuous data flows bypass these nodes as much as possible. At present, the existing research work related to the performance of continuous data-flow is often optimized from system architecture design and deployment. In this paper, a mathematical programming method is proposed for the first time to optimize the runtime performance of continuous data flow applications. A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency. The latency-aware energy consumption optimization for continuous data-flow is modeled as a mixed integer nonlinear programming problem. A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out. The simulation results show that the proposed strategy has better performance than the benchmark strategy.Sensors 2020, 20, 122 2 of 20 aggregation site, etc. [5]. On the other side, the fog computing nodes are not necessarily deployed around these access points. To distinguish these two concepts, in this paper, nodes with sufficient communication and computing resources scattered among IoT devices are referred to as fog nodes. The nano-server clusters deployed at the access points are referred to as MEC nodes, which usually have more resources than fog nodes. The collaborative computing of the IoT-end nodes, the fog nodes, the MEC nodes and the data center are referred to as IoT-Fog-Edge computing in this context.The studies on collaboration of Fog, MEC, and Cloud computing usually focus on finding an optimal solution to allocate the IoT tasks to appropriate virtual machines which are hosted on fog, MEC or cloud nodes. In [10], the authors propose to allocate the workload among local MEC servers, neighborhood MEC servers or cloud servers to minimize the energy consumption of MEC nodes subject to delay constraints. Lyapunov drift-plus-penalty-based dynamic queue evaluation is used for the online allocation algorithm. In [11], an optimal algorithm is put forward to determine that the tasks should be allocated to clouds near the end devices or to the one far from the end devices for the energy efficient big data processing. The delay constraints to tasks in near clouds and far cloud are taken into account. In [12], an optimal algorithm for joint task allocation among mobile devices, the computing access point and the remote cloud is proposed, where computing access point can be treated as an MEC node. The studies introduced above are not correlated with the continuous data-fl...

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Section: Motivation Scenariomentioning

confidence: 99%

Section: Motivation Scenariomentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Luo

Qiu

2019

Sensors

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“…Therefore, it is necessary to adjust the p, q value appropriately in order to make that the feature of symmetric Holder coefficient not only have a better degree of clustering within the class, but also have a larger distance between the classes. At the same time, it can be clearly seen that the complexity of the symmetric Holder coefficient method in type selection will be reduced by half [11][12][13][14]. As shown in Figure 1, it is the process flow of symmetric coefficient method.…”

Section: Recognition Of Signal Sequencementioning

confidence: 99%

Feature Extraction Method of Transmission Signal in Electronic Communication Network Based on Symmetric Algorithm

Song

2019

Symmetry

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Because the existing methods extract the signal characteristics of electronic communication networks, there is a problem of poor extraction. In this paper, a feature extraction method based on symmetric algorithm for transmission signals in electronic communication networks is proposed. The transmission signal in the time domain is decomposed by three-layer wavelet packet decomposition through threshold denoising and data dimension reduction. The adaptive floating threshold is used as a threshold to quantify the wavelet coefficients of the signal, which can effectively remove noise while retaining valuable transmission signal. Secondly, the feature extraction algorithm based on symmetric Holder coefficient is used to transform the transmitted signal from time domain to frequency domain, identify the signal sequence, and classify the signal sequence using neural network classifier. The simulation results show that the proposed method can extract the transmission signal of electronic communication network with the highest accuracy of 98.21%. This method can extract the amplitude and frequency characteristics of the transmission signal accurately under strong vibration environment. It is an efficient method for feature extraction of transmission signal.

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SPIDER: An availability‐aware framework for the service function chain placement in distributed scenarios

et al. 2022

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The network function virtualization (NFV) paradigm replaces hardware-dependent network functions by virtual network functions (VNFs) that can be deployed in commodity hardware, including legacy servers. Consequently, the use of NFV is expected to reduce operating and capital expenses, as well as improve service deployment operation and management flexibility.For many use cases, the VNFs must be visited and invoked following a specific order of execution in order to compose a complete network service, named service function chain (SFC). Nonetheless, despite the benefits from NFV and SFC virtualization technologies, their introduction must not harm network performance and service availability. On the one hand, redundancy is seen by network service planners as a mechanism well established to combat availability issues. At same time, there is a goal to optimize resource utilization in order to reduce operational expenditure. In this article, we share our experience in the design use of a framework, named SPIDER, focused on SFC placement that considers the network infrastructure condition and the required SFC availability to define the placement strategy. The SPIDER monitors the status of infrastructure nodes and links and defines which servers the VNFs should be placed on and the number of redundant replicas needed. We present a proof-of-concept of SPIDER using Kubernetes to launch the VNFs as containers. We also use Kubernetes to forward the traffic between the VNFs, composing the service chain. We perform experiments to evaluate the runtime of SPIDER and the SFC delay under different network conditions.

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Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures

Cited by 54 publications

References 49 publications

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Feature Extraction Method of Transmission Signal in Electronic Communication Network Based on Symmetric Algorithm

SPIDER: An availability‐aware framework for the service function chain placement in distributed scenarios

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