Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing

Dolui, Koustabh; Datta, Soumya Kanti

doi:10.1109/giots.2017.8016213

Cited by 394 publications

(208 citation statements)

References 22 publications

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“…A resource management system is responsible for the discovery, monitoring, allocation, migration, and distribution of resources. Compared to conventional systems such as edge cloud or mobile ad hoc cloud, resource management in a private mobile edge cloud is more difficult due to heterogeneous computing and communication environments, multilayer network structure, node mobility, and battery powered nodes. Existing resource management systems are designed for homogeneous network environments or single communication technologies.…”

Section: Opportunities and Research Challengesmentioning

confidence: 99%

“…An edge‐computing layer in which computational and storage services are provided at the network edge has been suggested as a solution for the limitations of the cloud centered approach. Edge computing implementations can be divided into three categories: mobile edge computing, fog computing, and cloudlet computing. In mobile edge computing, nodes with processing and storage capabilities are deployed at the base stations of the cellular network; thus, cloud‐computing services are provided in the radio access network.…”

Section: Introductionmentioning

confidence: 99%

“…Mobile cloud computing systems cannot fulfill the requirements of resource‐intensive real‐time IoT and 5G applications due to high communication latency. Cloud computing also introduces unnecessary traffic to the core Internet network and consumes a significant amount of transmission energy when data are transmitted via cellular networks . Consequently, edge computing implementations require adding new infrastructure or updating existing infrastructure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Private mobile edge cloud for 5G network applications

Shah

2019

Internet Technology Letters

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Resource‐intensive real‐time IoT and 5G network applications require low latency, high data rates, and considerable computing and storage resources. Mobile cloud computing systems cannot fulfill the requirements of these applications due to high communication latency. Consequently, recently proposed edge computing implementations require adding new infrastructure or updating existing infrastructure. In addition, edge computing does not exploit the capabilities of end devices, which have become more powerful than supercomputers in the last decade. To overcome the drawbacks of mobile cloud and edge computing systems, we have proposed a private mobile edge cloud in which multiple mobile and stationary devices such as sensors, robots, home appliances, and smartphones, interconnected through infrastructure‐less and infrastructure‐based wireless local area networks are combined to create a small‐scale cloud data center at a local physical area such as a home. We have also discussed opportunities and research challenges associated with development of proposed system and a high‐level architecture based on a cross layer design for an efficient management of resources.

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Section: Opportunities and Research Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Private mobile edge cloud for 5G network applications

Shah

2019

Internet Technology Letters

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“…DVM consists of at least three components: Dew server (DS) : The DS acts such as the cloud service in the local PC. It interacts with the cloud service. Dew analytics server (DAS) : Dew analytics server collects the information about the using of dew server. Artificial intelligence of the dew (AID) : AID uses the data (the data which is sent by the DAS about using the patterns) to customize it; it connects the dew server to the users, to enhance their experience In this paper, we focus on the processing power and storage capabilities of the dew servers.…”

Section: Related Workmentioning

confidence: 99%

A proposed computation, which benefits from the cooperation of dew, edge, and cloud computations

Abdoos

Taheri

Taeezadeh

2019

Trans Emerging Tel Tech

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We propose a computation that compensates the weakness of the processing power and storage capacity of the dew and edge servers. In the proposed computation, the nodes near to each other, which can be the dew, edge, or cloud servers, form a computational grid. The dew servers can be mobile devices, laptops, or computers. Their processing power and storage capacity can be used when they are idle. Our goal is to achieve load balancing of different nodes (servers). A server of the grid with proper processing power, storage capacity, remained energy, and low task load is selected as the grid head (each server of grid shares its specification with the other servers of grid periodically). The grid head (server) is responsible for grid communications. The grid head shares the specification of the grid, with the neighbor nodes (we suppose that the end device is aware of the specification of the nearest dew, edge, and cloud grid heads to itself). The end device decides about sending its created tasks to the grid head of a dew or edge or cloud grid, with attention to the distance between itself and the grid head, and the free processing power and storage capacity of the grid. The scheduling algorithm of a grid is the combination of first‐come–first‐served and priority scheduling algorithms. The simulation results show that the proposed computation has less delay and energy consumption and more efficiency than the cloud and edge computations.

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“…Given that the tendency is shifting time-constrained services and applications towards the edge of the Cloud into the Fog, it is imperative to guarantee that the time restrictions are met [79]. The service orchestrator must incorporate novel mechanisms, different from those already available for other distributed systems such as Cloud, that are sensitive to time constraints, and that support other features such as mobility, dynamism, and geodistribution [17].…”

Section: Latencymentioning

confidence: 99%

Fog orchestration for the Internet of Everything: state-of-the-art and research challenges

Velásquez

Abreu

Assis

et al. 2018

J Internet Serv Appl

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Recent developments in telecommunications have allowed drawing new paradigms, including the Internet of Everything, to provide services by the interconnection of different physical devices enabling the exchange of data to enrich and automate people's daily activities; and Fog computing, which is an extension of the well-known Cloud computing, bringing tasks to the edge of the network exploiting characteristics such as lower latency, mobility support, and location awareness. Combining these paradigms opens a new set of possibilities for innovative services and applications; however, it also brings a new complex scenario that must be efficiently managed to properly fulfill the needs of the users. In this scenario, the Fog Orchestrator component is the key to coordinate the services in the middle of Cloud computing and Internet of Everything. In this paper, key challenges in the development of the Fog Orchestrator to support the Internet of Everything are identified, including how they affect the tasks that a Fog service Orchestrator should perform. Furthermore, different service Orchestrator architectures for the Fog are explored and analyzed in order to identify how the previously listed challenges are being tackled. Finally, a discussion about the open challenges, technological directions, and future of the research on this subject is presented.

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Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing

Cited by 394 publications

References 22 publications

Private mobile edge cloud for 5G network applications

Private mobile edge cloud for 5G network applications

A proposed computation, which benefits from the cooperation of dew, edge, and cloud computations

Fog orchestration for the Internet of Everything: state-of-the-art and research challenges

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