A matching game for tasks offloading in integrated edge‐fog computing systems

Chiti, Francesco; Fantacci, R.; Picano, Benedetta

doi:10.1002/ett.3718

Cited by 20 publications

(17 citation statements)

References 43 publications

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“…In problems ( 4)-( 10), constraint (9) expresses the fact that each SR with a high priority has to be served, while constraints (10) and (11) represent that the VFs allocation has to respect the storage limit of CNs and cloud, respectively.…”

Section: Problem Statementmentioning

confidence: 99%

“…The emergence of new network paradigms such as Edge Computing (EC) [1,2,3,4], for which the limitations typical of the cloud architecture have been bypassed moving computation nodes to the network edges close to the end users, has given rise to a wide range of challenges in many research areas [5,6]. Consequently, several new issues, such as user mobility, heterogeneity in Quality of Service (QoS) or service requirements, massive volume of data, user privacy, diversity on data types and so on, have led to numerous efforts from both academia and industry in providing highly effective and ef icient solutions [7,8,9,10,11]. In particular, there exists a signi icant branch of literature regarding possible solutions to improve EC Network (ECN) performance in order to guarantee a high level of user satisfaction and to provide dynamic and lexible network resource allocation and decision-making strategies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Federated learning for IoE environments: A service provider revenue maximization framework

Picano¹,

Fantacci²,

Pecorella³

et al. 2021

ITU-J FET

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In accordance with the Internet of Everything (IoE) paradigm, millions of people and billions of devices are expected to be connected to each other, giving rise to an ever increasing demand for application services with a strict quality of service requirements. Therefore, service providers are dealing with the functional integration of the classical cloud computing architecture with edge computing networks. However, the intrinsic limited capacity of the edge computing nodes implies the need for proper virtual functions' allocations to improve user satisfaction and service fulfillment. In this sense, demand prediction is crucial in services management and exploitation. The main challenge here consists of the high variability of application requests that result in inaccurate forecasts. Federated learning has recently emerged as a solution to train mathematical learning models on the users' site. This paper investigates the application of federated learning to virtual functions demand prediction in IoE based edge cloud computing systems, to preserve the data security and maximise service provider revenue. Additionally, the paper proposes a virtual function placement based on the services demand prediction provided by the federated learning module. A matching based tasks allocation is proposed. Finally, numerical results validate the proposed approach, compared with a chaos theory prediction scheme.

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Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Federated learning for IoE environments: A service provider revenue maximization framework

Picano¹,

Fantacci²,

Pecorella³

et al. 2021

ITU-J FET

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“…Ghobaei‐Arani et al 37 found a proper tasks allocation plan to reduce both the system energy consumption and the most critical overall task completion time in the fog networks. Chiti et al 38 proposed a scheduling algorithm for the tasks on moth‐flame optimization algorithm to indicate an optimal set of functions to fog nodes. In Reference 39, the authors emphasize the primary purpose of smart cities and discusses the privacy and security issues of the smart cities' applications.…”

Section: Related Workmentioning

confidence: 99%

Analysis on homomorphic technique for data security in fog computing

Murugesan

Balamurali

Al‐Turjman³

et al. 2020

Trans Emerging Tel Tech

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The fog computing model has given new trends of networking with different types of devices providing services at the end‐user point. It inherits most advanced features of cloud computing with localization rather than centralization like a cloud. It builds a platform for the Internet of things of different standards. Similar to the cloud, it is prone to privacy and security threat while sharing resources and services at the network edge. When the request for support is with more sensitive data, for example, as in business or research area, then fog devices face many potential threats resulting in leakage of data. The existing cloud environment provides data sharing service to the legal requestor in a highly secured manner using cryptographic encryption techniques. The uploaded data undergoes encryption and decryption, at the sending and receiving end only on providing the private key. Even more, security can be achieved by further computation on encrypted data. As an extension to fog computing, the data communication between fog nodes‐fog nodes and fog node‐cloud center is done with encryption/decryption for ensuring confidentiality. Homomorphic encryption is a cryptographic technique which allows performing computations on encrypted data without decryption so that the original message need not be disclosed to intermediates (servers) who are the only service provider and not a data user. Our work is motivated by the flaw of security issues in fog computing platforms, which involve heterogeneous devices.

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“…In smart cities, the exchange of information between several connected smart devices along with the environment is increasing day by day. [53][54][55] This type of situation has led to the increase in demand of both computation capabilities and network bandwidth in connected devices for supporting data traffic with real-time constraints. In References 56,57, an application has been developed for storing and processing data to meet the real-time constraints is still a big study.…”

Section: Related Workmentioning

confidence: 99%

Efficient data transfer in edge envisioned environment using artificial intelligence based edge node algorithm

Kumar

Batth

et al. 2020

Trans Emerging Tel Tech

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Edge computing technology has drawn the keystone of future intelligent transportation systems, especially in smart cities, because of processing data that are near to the user location present at the edge of the cloud server. Generally, in smart cities where distributed things have access to computational resources, data transfer becomes inevitable because of high latency, thereby resulting in crucial situations. Even though numerous technologies have emerged for improving the data communication among geo-distributed devices received from the cloud server but still, it lags in low learning performance. To address these challenges, an innovative artificial intelligence (AI) based edge node (E-Node) algorithm is implemented to optimize the edge to edge learning for well-organized data migration. To attain high reliability, AI-K-means neural network (KNN) and convolution neural network (CNN) is used initially for pre-processing and filtering the edge node. Further, the proposed E-Node algorithm outperforms the optimization technique effectively through the edge to edge computing The reliability performance is increased by reducing the node optimization time from 132 to 98 ms for 25 kb data and data transmission time is reduced from 93 to 45 ms for 80 kb data, thus reducing the latency in an edge envisioned environment. INTRODUCTIONArtificial intelligent (AI) frameworks help to solve problems involving the learning abilities of computers, language processing, and identifying various speech in the environment in a highly reliable and accurate manner. 1 Smart cities concept endures the cities with intelligence and other concepts involving smart vehicles, smart grid, and smart healthcare using the concept of deep learning. Internet of Vehicles (IoV) 2,3 lays the foundation for several new transportation systems . 4 IoV helps to improve the efficiency of transportation, reducing the accident rate and reducing the energy consumption in vehicles being reduced. 5,6 The data generated from these smart vehicles are stored in the cloud. However, it is difficult to handle several request messages from the client and process it, during a particular grave learning task. The proposed system uses a new model to foil centralized cloud computing, which reduces the latency during communication and improves the quality through the provision of various resources 7 that are near to terminal devices for latency-sensitive tasks. The use of edge to edge cooperative AI increases the learning efficiency, quality of service. It also improves variousAbbreviations: CNN, convolution neural network; E-Node, edge node; KNN, K-means neural network.

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A matching game for tasks offloading in integrated edge‐fog computing systems

Cited by 20 publications

References 43 publications

Federated learning for IoE environments: A service provider revenue maximization framework

Federated learning for IoE environments: A service provider revenue maximization framework

Analysis on homomorphic technique for data security in fog computing

Efficient data transfer in edge envisioned environment using artificial intelligence based edge node algorithm

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