Resource Allocation in Combined Fog-Cloud Scenarios by Using Artificial Intelligence

Abedi, Masoud; Pourkiani, Mohammadreza

doi:10.1109/fmec49853.2020.9144693

Cited by 25 publications

(19 citation statements)

References 18 publications

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“…The bandwidth used to discharge the Fog data is less than that used to discharge data into the cloud. The reference [75] proposed a method that notably minimized the internet traffic and response time compared to the Fog-based and cloudbased approaches. The authors in [8] provided guidelines to select an appropriate Fog computing distributed architecture while considering the final application's requirements.…”

Section: Discussionmentioning

confidence: 99%

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A Study of Moving from Cloud Computing to Fog Computing

Abdulqadir

Zeebaree

Shukur

et al. 2021

QAJ

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The exponential growth of the Internet of Things (IoT) technology poses various challenges to the classic centralized cloud computing paradigm, including high latency, limited capacity, and network failure. Cloud computing and Fog computing carry the cloud closer to IoT computers in order to overcome these problems. Cloud and Fog provide IoT processing and storage of IoT items locally instead of sending them to the cloud. Cloud and Fog provide quicker reactions and better efficiency in conjunction with the cloud. Cloud and fog computing should also be viewed as the safest approach to ensure that IoT delivers reliable and stable resources to multiple IoT customers. This article discusses the latest in cloud and Fog computing and their convergence with IoT by stressing deployment's advantages and complexities. It also concentrates on cloud and Fog design and new IoT technologies, enhanced by utilizing the cloud and Fog model. Finally, transparent topics are addressed, along with potential testing recommendations for cloud storage and Fog computing, and IoT.

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Section: Discussionmentioning

confidence: 99%

“…Abedi and Pourkiani [75] proposed a Task Distribution (AI) algorithm to minimize internet traffic and response times by spreading tasks between cloud and fog servers. Their case study was a delay-sensitive program that runs if the computer capability of the fog servers is limited.…”

Section: Related Workmentioning

confidence: 99%

A Study of Moving from Cloud Computing to Fog Computing

Abdulqadir

Zeebaree

Shukur

et al. 2021

QAJ

View full text Add to dashboard Cite

show abstract

“…In [21], the authors addressed the issue of protection of data and performance by proposing a model for trust translation, introducing an access control for fog nodes and coming up with a service for the management of alterations in users and their locations. The authors of [22] discussed an Artificial Intelligence algorithm whose goal is to decrease the time it takes to respond and network traffic, by assigning various tasks to the cloud and fog servers. Experimental results showed that this technique significantly decreases the time for response when compared to existing approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Blockchain-Based Security Mechanism for the Medical Data at Fog Computing Architecture of Internet of Things

et al. 2021

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The recent developments in fog computing architecture and cloud of things (CoT) technology includes data mining management and artificial intelligence operations. However, one of the major challenges of this model is vulnerability to security threats and cyber-attacks against the fog computing layers. In such a scenario, each of the layers are susceptible to different intimidations, including the sensed data (edge layer), computing and processing of data (fog (layer), and storage and management for public users (cloud). The conventional data storage and security mechanisms that are currently in use appear to not be suitable for such a huge amount of generated data in the fog computing architecture. Thus, the major focus of this research is to provide security countermeasures against medical data mining threats, which are generated from the sensing layer (a human wearable device) and storage of data in the cloud database of internet of things (IoT). Therefore, we propose a public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer. The blockchain technology approach also helps to mitigate these issues of latency, centralization, and scalability in the fog model.

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“…In the work of Abedi & Pourkiani (2020) , the authors introduce a service placement algorithm based on an artificial neural network aimed to minimize response times of applications while distributing them in the fog landscape. Their approach is simulated with MATLAB, and therefore it is not clear how long it takes to produce such a neural network in the real world.…”

Section: Related Workmentioning

confidence: 99%

FogFrame: a framework for IoT application execution in the fog

Skarlat¹,

Schulte²

2021

PeerJ Computer Science

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Recently, a multitude of conceptual architectures and theoretical foundations for fog computing have been proposed. Despite this, there is still a lack of concrete frameworks to setup real-world fog landscapes. In this work, we design and implement the fog computing framework FogFrame—a system able to manage and monitor edge and cloud resources in fog landscapes and to execute Internet of Things (IoT) applications. FogFrame provides communication and interaction as well as application management within a fog landscape, namely, decentralized service placement, deployment and execution. For service placement, we formalize a system model, define an objective function and constraints, and solve the problem implementing a greedy algorithm and a genetic algorithm. The framework is evaluated with regard to Quality of Service parameters of IoT applications and the utilization of fog resources using a real-world operational testbed. The evaluation shows that the service placement is adapted according to the demand and the available resources in the fog landscape. The greedy placement leads to the maximum utilization of edge devices keeping at the edge as many services as possible, while the placement based on the genetic algorithm keeps devices from overloads by balancing between the cloud and edge. When comparing edge and cloud deployment, the service deployment time at the edge takes 14% of the deployment time in the cloud. If fog resources are utilized at maximum capacity, and a new application request arrives with the need of certain sensor equipment, service deployment becomes impossible, and the application needs to be delegated to other fog resources. The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.

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Resource Allocation in Combined Fog-Cloud Scenarios by Using Artificial Intelligence

Cited by 25 publications

References 18 publications

A Study of Moving from Cloud Computing to Fog Computing

A Study of Moving from Cloud Computing to Fog Computing

Blockchain-Based Security Mechanism for the Medical Data at Fog Computing Architecture of Internet of Things

FogFrame: a framework for IoT application execution in the fog

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