Fog Computing Advancement: Concept, Architecture, Applications, Advantages, and Open Issues

Fog computing has emerged as a computing paradigm for resource-restricted Internet of things (IoT) devices to support time-sensitive and computationally intensive applications. Offloading can be utilized to transfer resource-intensive tasks from resource-limited end devices to a resource-rich fog or cloud layer to reduce end-to-end latency and enhance the performance of the system. However, this advantage is still challenging to achieve in systems with a high request rate because it leads to long queues of tasks in fog nodes and reveals inefficiencies in terms of delays. In this regard, reinforcement learning (RL) is a well-known method for addressing such decision-making issues. However, in large-scale wireless networks, both action and state spaces are complex and extremely extensive. Consequently, reinforcement learning techniques may not be able to identify an efficient strategy within an acceptable time frame. Hence, deep reinforcement learning (DRL) was developed to integrate RL and deep learning (DL) to address this problem. This paper presents a systematic analysis of using RL or DRL algorithms to address offloadingrelated issues in fog computing. First, the taxonomy of fog computing offloading mechanisms based on RL and DRL algorithms was divided into three major categories: value-based, policy-based, and hybridbased algorithms. These categories were then compared based on important features, including offloading problem formulation, utilized techniques, performance metrics, evaluation tools, case studies, their strengths and drawbacks, offloading directions, offloading mode, SDN-based architecture, and offloading decisions. Finally, the future research directions and open issues are discussed thoroughly.INDEX TERMS Fog computing, Internet of Things (IoT), offloading, reinforcement learning, deep reinforcement learning.

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“…According to previous studies [35], [36], and [37], the hierarchical architecture of fog computing comprises the following three main layers:…”

Section: B Fog Computing Architecturementioning

confidence: 99%

Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

Abdulazeez

Askar

2023

IEEE Access

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“…So that, Fog cannot be taken in consideration as an alternate of typical Cloud infrastructure, instead, a fresh concept which brings IoT, Edge, and Cloud together. [20] 4…”

Section: Fog Computingmentioning

confidence: 99%

“…In accordance to this, reliability was observed as a primary development slab of architecture instead of enhanced as well as over-looked attribute to add on a head of it. As a part of this concern, the OpenFog Consortium [20] was very active with the concept of reference architecture of Fog that was having reliability and security as the first pillar.…”

Section: 1 Benefits Of Fog Computingmentioning

confidence: 99%

A Comparative Study on Cloud Computing, Edge Computing and Fog Computing

Tank

Gandhi

2023

Advances in Transdisciplinary Engineering

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In the recent era, Cloud computing has emerged as a high demanding technology. Apart from the cloud computing, edge and fog computing also becoming popular. Cloud computing was developed to provide steady and expansible services to end-users as well as industries. The merger of cloud computing with smart devices brings us into a new version of computing. Presently, Edge computing and fog computing techniques are flattering the world after cloud computing, which has all matured with the limitations in cloud. The objective of this study is to examine the current trends and concepts in detail, also, to provide a comparative study for the fog computing, edge computing, and cloud computing. This study includes few features, application, advantages as well as disadvantages, and the contrast between these technologies and how every one of them is effective for different applications.

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“…We note that the different forms of software entities can be physically executed in any computing location in communication networks [76], such as in-network computing [77]- [79] also referred to as computing in the network (COIN) [80], fog computing [81], [82], and multi-access edge computing (MEC) [83]- [89].…”

Section: E Summary Of Nf Softwarizationmentioning

confidence: 99%

Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research Studies

et al. 2022

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Scalable and flexible communication networks increasingly conduct the packet processing for Network Functions (NFs) in General Purpose Computing (GPC) platforms. The input/output (I/O)-intensive and latency-sensitive packet processing is challenging for the operating systems and hypervisors running on GPC platforms. This article surveys the existing enabling technologies and research studies on operating system and hypervisor aspects that directly influence the packet processing for NFs on GPC platforms. We organize this survey according to the main categories abstraction approach, memory access, and I/O strategy. We further categorize abstraction approach technologies and research studies into the categories operation systems, hypervisors, and containers. We partition the memory access category into the two subcategories of memory allocation and memory access, while we partition the I/O strategy category into the sub-categories I/O device virtualization and I/O device access. Our survey gives a comprehensive summary of the capabilities and limitations of the existing enabling technologies and researched approaches for abstraction, memory access, and I/O for NF packet processing. We outline critical future research directions for advancing NF packet processing on GPC platforms.

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Fog Computing Advancement: Concept, Architecture, Applications, Advantages, and Open Issues

Cited by 47 publications

References 109 publications

Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

A Comparative Study on Cloud Computing, Edge Computing and Fog Computing

Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research Studies

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