Fog Computing Potentials, Applications, and Challenges

Haouari, Fatima; Faraj, Ranim; Alja’am, Jihad Mohamad

doi:10.1109/comapp.2018.8460182

Cited by 28 publications

(23 citation statements)

References 31 publications

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“…Algorithms in Fog Computing autonomy, availability, hierarchy programmability, serviceability, reliability, openness and security [5]. There are enormous potentials for the Fog as a complement to the Cloud.…”

Section: A Comparative Study Of Various Machine Learningmentioning

confidence: 99%

A Comparative Study of Various Machine Learning Algorithms in Fog Computing

2021

IJATCSE

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Internet of Things is the reality of the future. It encompasses a vast prototype. The applications are limitless and the research potential is huge. Several unanswered questions still stand in the way. One of these challenging questions is the Network layer implementation of IoT. Cloud offers one perspective solution. Cloud services face the down side of connectivity and latency. Fog computing, as a complementing technology for Cloud answers the issues of latency and connectivity in the Cloud. Being novel idea in itself, Fog computing offers promising future growth. A major concept in Fog networks is its ability to learn and adopt to changing environment. This ability of the Fog devices to self-learn and improve itself is the core of machine-learning. Hence, machine-learning algorithms occupy central place in visualizing robust and effective Fog networks. This paper presents a comparative study of various machine-learning algorithms and their applications in the Fog network. The paper also projects the undiscovered research paradigms and future directions

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Section: A Comparative Study Of Various Machine Learningmentioning

confidence: 99%

A Comparative Study of Various Machine Learning Algorithms in Fog Computing

2021

IJATCSE

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“…For decentralized platform like Internet of Things, heterogenic services and applications are sent over the cloud platform and the problem of latency is becoming highly complex. In order to run the time-sensitive applications and scale up the computational resources, it is important for IoT to leverage the cloud resources and overcome the high latency" [19]. Some of the factors that are the main cause of high latency in cloud computing are as below:  Distributed Computing: The enterprise datacentres contain all the data and infrastructure of an organization and is being distributed across the globe.…”

Section: Factors Causing High Latency In Cloud-environmentmentioning

confidence: 99%

Fog-based Spider Web Algorithm to Overcome Latency in Cloud Computing

Dar

Ravindran²,

Islam³

2020

eijs

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The cloud-users are getting impatient by experiencing the delays in loading the content of the web applications over the internet, which is usually caused by the complex latency while accessing the cloud datacenters distant from the cloud-users. It is becoming a catastrophic situation in availing the services and applications over the cloud-centric network. In cloud, workload is distributed across the multiple layers which also increases the latency. Time-sensitive Internet of Things (IoT) applications and services, usually in a cloud platform, are running over various virtual machines (VM’s) and possess high complexities while interacting. They face difficulties in the consolidations of the various applications containing heterogenetic workloads. Fog computing takes the cloud computing services to the edge-network, where computation, communication and storage are within the proximity to the end-user’s edge devices. Thus, it utilizes the maximum network bandwidth, enriches the mobility, and lowers the latency. It is a futuristic, convenient and more reliable platform to overcome the cloud computing issues. In this manuscript, we propose a Fog-based Spider Web Algorithm (FSWA), a heuristic approach which reduces the delays time (DT) and enhances the response time (RT) during the workflow among the various edge nodes across the fog network. The main purpose is to trace and locate the nearest f-node for computation and to reduce the latency across the various nodes in a network. Reduction of latency will enhance the quality of service (QoS) parameters, smooth resource distribution, and services availability. Latency can be an important factor for resource optimization issues in distributed computing environments. In comparison to the cloud computing, the latency in fog computing is much improved.

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“…An example topology for fog computing is illustrated in Figure 1. Fog computing typically aims to reduce the latency and the load on the cloud, and is often used in the context of IoT, in which large amounts of data are collected for analysis and processing [67,71,133,134,160].…”

Section: Fog and Edge Computingmentioning

confidence: 99%

Resource Management in a Containerized Cloud: Status and Challenges

et al. 2019

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Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research.

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Fog Computing Potentials, Applications, and Challenges

Cited by 28 publications

References 31 publications

A Comparative Study of Various Machine Learning Algorithms in Fog Computing

A Comparative Study of Various Machine Learning Algorithms in Fog Computing

Fog-based Spider Web Algorithm to Overcome Latency in Cloud Computing

Resource Management in a Containerized Cloud: Status and Challenges

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