The Implementation of a Cloud-Edge Computing Architecture Using OpenStack and Kubernetes for Air Quality Monitoring Application

Kristiani, Endah; Yang, Chao-Tung; Huang, Chin‐Yin; Wang, Yuan-Ting; Ko, Patrick Chow In

doi:10.1007/s11036-020-01620-5

Cited by 37 publications

(26 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A study presented in [35] proposed using fog computing to monitor the environmental parameters and response in real-time based on the decision-making process that can be done on fog nodes. As an extension of cloud computing, in [36,37], fog computing has been promoted due to its inherent property of bringing the intelligence to the proximity of the edge of the network, and the effects on the performance of the service execution. The proposed AQM system based on fog computing presented in [38] introduces a distributed fog computing layer to effectively process the air pollutants' data sent by the sensor layer.…”

Section: Related Workmentioning

confidence: 99%

“…The selection of viewpoints, used for the classification of reviewed system solutions, was performed to reveal the critical aspects regarding the research approach, research directions and intended applications of the proposed solutions. The majority of reviewed system solutions given in Table 1 [20][21][22][23][24][25][26]35,36,40,43] utilize a sensor-cloud approach given as an infrastructure that allows persistent system operation using sensors as an interface between physical and cyber worlds, the cloud computing services as the cyber backbone and the internet as the communication medium. Although architecture review can be found as a part of the associated research papers [22,35,36], the research novelty in these papers is more often related to the hardware design of sensing devices and algorithms for data processing.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Popović

Radovanović

Vajs

et al. 2022

Sensors

View full text Add to dashboard Cite

Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration of a huge number of sensors brings many challenges. The volume, velocity and processing requirements regarding the management of the sensor life cycle and the operation of system services overcome the capabilities of the centralized cloud model. In this paper, we present the methodology and the architectural framework for building large-scale sensing infrastructure for air quality monitoring applicable in urban scenarios. The proposed tiered architectural solution based on the adopted fog computing model is capable of handling the processing requirements of a large-scale application, while at the same time sustaining real-time performance. Furthermore, the proposed methodology introduces the collection of methods for the management of edge-tier node operation through different phases of the node life cycle, including the methods for node commission, provision, fault detection and recovery. The related sensor-side processing is encapsulated in the form of microservices that reside on the different tiers of system architecture. The operation of system microservices and their collaboration was verified through the presented experimental case study.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Popović

Radovanović

Vajs

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The basic decision method is that by matching the resource information with the application requirements, the MEO can select the target ME host [20]. To implement of MEO, Kristiani et al [21] built a set of an intelligent air-quality monitoring system in Tunghai University. The authors use the Ganglia monitoring system to collect relevant information such as CPU, memory, network to monitor the power consumption and make a measurement and evaluation for Kubernetes Pods [21].…”

Section: Related Workmentioning

confidence: 99%

Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

et al. 2020

View full text Add to dashboard Cite

In the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

show abstract

“…With the recent technical and scientific progress in artificial intelligence, Internet of Things (IoT), and cloud computing, research efforts are moving towards facilitating communication among different devices. Cloud computing as an innovative paradigm develops an environment to provide unlimited virtual applications and resources via the Internet, which are ubiquitously accessible, rapidly provisioned, customizable, and available on-demand [1]. Computing resources such as servers, storage, and computer networks are provided in four main forms, such as Software as a Service (SaaS) [2], Infrastructure as a Service (IaaS) [3], Platform as a Service (PaaS) [4], and Expert as a Service (EaaS) [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

et al. 2022

View full text Add to dashboard Cite

Nowadays, cloud computing is growing daily and has been developed as an effective and flexible paradigm in solving large-scale problems. It has been known as an Internet-based computing model in which computing and virtual resources, such as services, applications, storage, servers, and networks, are shared among numerous cloud users. Since the number of cloud users and their requests are increasing rapidly, the loads on the cloud systems may be underloaded or overloaded. These situations cause different problems, such as high response time and power consumption. To handle the mentioned problems and improve the performance of cloud servers, load balancing methods have a significant impact. Generally, a load balancing method aims to detect under-loaded and overloaded nodes and balance the load among them. In the recent decade, this problem has attracted a lot of interest among researchers, and several solutions have been proposed. Considering the important role of fault-tolerant in load balancing algorithms, there is a lack of an organized and in-depth study in this field yet. This gap prompted us to provide the current study aimed to collect and review the available papers in the field of fault tolerance load balancing methods in cloud computing. The existing algorithms are divided into two categories, namely, centralized and distributed, and reviewed based on vital qualitative parameters, such as scalability, response time, reliability, availability, throughput, and overhead. In this regard, other criteria such as the type of detected faults and adopted simulation tools are taken into account.

show abstract

The Implementation of a Cloud-Edge Computing Architecture Using OpenStack and Kubernetes for Air Quality Monitoring Application

Cited by 37 publications

References 30 publications

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

Contact Info

Product

Resources

About