Fog data management: A vision, challenges, and future directions

Sadri, Ali Akbar; Rahmani, Amir Masoud; Saberikamarposhti, Morteza; Hosseinzadeh, Mehdi

doi:10.1016/j.jnca.2020.102882

Cited by 43 publications

(33 citation statements)

References 46 publications

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“…The real-time applications including the Internet of Things (IoT) integrate billions of embedded systems with computer-based systems, enabling things to analyze and operate smartly. 26 One of the efficient computer-based paradigms is cloud computing. Cloud computing consists of a CDC with a diverse set of resources as a form of computing servers, which are connected through a high-speed network.…”

Section: Overview Of Edge Computingmentioning

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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Section: Overview Of Edge Computingmentioning

confidence: 99%

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

2021

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“…Since distinct behaviour and responses can be described for various situations, identifying data contexts in fog nodes in order to solve problems correctly under the right conditions is another problem [105]. (4) Storage and computational resources: complex data analysis or long-term data storage is difficult to achieve on fog devices due to storage and computing resource limitations [106].…”

Section: Challenges In Fog Computingmentioning

confidence: 99%

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

Molo

Badejo

Adetiba

et al. 2021

Applied Computational Intelligence and Soft Computing

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Cloud computing is a technology that allows dynamic and flexible computing capability and storage through on-demand delivery and pay-as-you-go services over the Internet. This technology has brought significant advances in the Information Technology (IT) domain. In the last few years, the evolution of cloud computing has led to the development of new technologies such as cloud federation, edge computing, and fog computing. However, with the development of Internet of Things (IoT), several challenges have emerged with these new technologies. Therefore, this paper discusses each of the emerging cloud-based technologies, as well as their architectures, opportunities, and challenges. We present how cloud computing evolved from one paradigm to another through the interplay of benefits such as improvement in computational resources through the combination of the strengths of various Cloud Service Providers (CSPs), decrease in latency, improvement in bandwidth, and so on. Furthermore, the paper highlights the application of different cloud paradigms in the healthcare ecosystem.

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“…W ITH the massive boom in Internet of Things (IoT) applications in daily life, such as in industry, healthcare, logistics, and military [1], [2], many IoT devices collect data from the environment and generate huge amounts of data. These data are transferred to and processed at cloud servers located at the network core, resulting in high bandwidth consumption and response time [3].…”

Section: Introductionmentioning

confidence: 99%

Traffic-Aware Horizontal Pod Autoscaler in Kubernetes-Based Edge Computing Infrastructure

2022

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Container-based Internet of Things (IoT) applications in an edge computing environment require autoscaling to dynamically adapt to fluctuations in IoT device requests. Although Kubernetes' horizontal pod autoscaler provides the resource autoscaling feature by monitoring the resource status of nodes and then making pod adjustments if necessary, it evenly allocates pods to worker nodes without considering the imbalance of resource demand between nodes in an edge computing environment. This paper proposes the traffic-aware horizontal pod autoscaler (THPA), which operates on top of Kubernetes to enable real-time traffic-aware resource autoscaling for IoT applications in an edge computing environment. THPA performs upscaling and downscaling actions based on network traffic information from nodes to improve the quality of IoT services in the edge computing infrastructure. Experimental results show that Kubernetes with THPA improves the average response time and throughput of IoT applications by approximately 150% compared to Kubernetes with the horizontal pod autoscaler. This indicates that it is important to provide proper resource scaling according to the network traffic distribution to maximize IoT applications performance in an edge computing environment.INDEX TERMS Kubernetes, horizontal pod autoscaler, network-aware resource provisioning, IoT.

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Fog data management: A vision, challenges, and future directions

Cited by 43 publications

References 46 publications

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

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

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

Traffic-Aware Horizontal Pod Autoscaler in Kubernetes-Based Edge Computing Infrastructure

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