Scalable edge cloud platforms for IoT services

Sonkoly, Balázs; Haja, Dávid; Németh, Balázs; Szalay, Márk; Czentye, János; Szabó, Rita; Ullah, Rehmat; Kim, Byung-Seo; Toka, László

doi:10.1016/j.jnca.2020.102785

Cited by 32 publications

(24 citation statements)

References 21 publications

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“…On the other hand, there are multiple options of edge nodes in the collected papers, because we omit research results on binary offloading decisions. Usually the challenges addressed in these papers stem from the choice of edge nodes, the latency requirements, Online [52], [118]- [124] [59], [125]- [128] [129]- [140] [100], [141]- [146] [141], [143], [147]- [153] [109], [111], [154]- [160] [113] Online with migration [161]- [167] [168]- [170] Hybrid [171], [172] [173]- [179] [180]- [182] [183] and compute capabilities. 5) Cloud-edge-terminal: Finally, the last category might be considered as an extension of the previous one by adding remote clouds as options to the offloading decision.…”

Section: A Platform Componentsmentioning

confidence: 99%

“…2) Resource allocation in edge and fog computing systems: In this section we overview the research papers that focus on edge systems without considering the terminals' compute capabilities. In these papers the underlying infrastructure assumes a multi-cloud [49], a cloud-edge [54]- [57], [60], [63], [68], [73], [78], [84], [86], [88], [106], [111], [145], [153], [160], [163], [167], [175], [177] [54], [76], [124], [146] [59], [66], [67], [125] [74], [95], [97], [124], [170], [180] [69], [74], [98], [165], [166], [177], [184], or a multi-edge scenario [78], [82], [86], [87], [90], [93], [130], [168], [181]. These choices belong to the Platform components dimension, and highly d...…”

Section: A Single Componentmentioning

confidence: 99%

“…Following our Platform components dimension, we distinguish the cloud-edge and the multi-edge scenarios depending on the placement options. The authors of [58], [61], [62], [65]- [67], [73], [76], [125], [126], [128], [162]- [164], [167], [175], [179] consider the cloud-edge scenario where the service components can be run either in the available edge domains or in the central cloud. Other research papers [83], [88], [89], [91], [92], [96], [97], [134], [138] investigate the multi-edge option where the central cloud cannot be used as a runtime environment.…”

Section: Multiple Components With Connectionmentioning

confidence: 99%

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Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

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Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

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Section: A Platform Componentsmentioning

confidence: 99%

Section: A Single Componentmentioning

confidence: 99%

Section: Multiple Components With Connectionmentioning

confidence: 99%

See 1 more Smart Citation

Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

Self Cite

View full text Add to dashboard Cite

show abstract

“…We control population connections and coevolution through population migration. Therefore, the optimal individuals in a population are transferred to other populations through migration operations (lines [15][16]. At each iteration, the elite population is renewed and the elite individuals of each population are retained (line 17).…”

Section: Offloading Strategy Based On Mce-gamentioning

confidence: 99%

“…MEC is a promising enabler of future Internet such as the fifth generation (5G) [ 14 ], enabling multiple future 5G applications and network services such as IoT applications, haptic Internet, AR/VR use cases, or remote driving [ 15 ]. Computation-intensive applications, such as AR and VR, require a lot of computation in a very short period of time, which need very high computing power.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Computation Offloading Strategy with Mobile Edge Computing for IoT

Fang

Shi

et al. 2021

Micromachines

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With the rapidly development of mobile cloud computing (MCC), the Internet of Things (IoT), and artificial intelligence (AI), user equipment (UEs) are facing explosive growth. In order to effectively solve the problem that UEs may face with insufficient capacity when dealing with computationally intensive and delay sensitive applications, we take Mobile Edge Computing (MEC) of the IoT as the starting point and study the computation offloading strategy of UEs. First, we model the application generated by UEs as a directed acyclic graph (DAG) to achieve fine-grained task offloading scheduling, which makes the parallel processing of tasks possible and speeds up the execution efficiency. Then, we propose a multi-population cooperative elite algorithm (MCE-GA) based on the standard genetic algorithm, which can solve the offloading problem for tasks with dependency in MEC to minimize the execution delay and energy consumption of applications. Experimental results show that MCE-GA has better performance compared to the baseline algorithms. To be specific, the overhead reduction by MCE-GA can be up to 72.4%, 38.6%, and 19.3%, respectively, which proves the effectiveness and reliability of MCE-GA.

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Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

Gupta

2022

Int J Communication

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Summary A drone or unmanned aerial vehicles (UAVs) is becoming a trending area for researchers worldwide. UAV's contribution is increasing in day‐to‐day life, whether it is in a military zone, disaster management, healthcare sector, smart cities, Internet of Things (IoT), urban air mobility, and many more. In contrast, UAV's limited computational capability and low‐energy sources pose significant challenges for real‐time data processing, storage, networking, and security that are critical in emergencies such as floods, earthquakes, and cyclones. UAVs are rapidly used to satisfy user requirements as well as services. As the demand for UAVs aided heterogeneous wireless networks increases in critical emergencies, fog computing serves several benefits to fulfill users' demands in terms of low latency, support, data storage, mobility, availability, scalability, and so on. This study aims to present a comprehensive study with their technical aspects for understanding fog computing, security issues, privacy concerns, and risks, along with its solutions. This paper suggests the collaboration of UAV‐Fog architecture based on the four‐tier network consisting of smart things, local UAVs, UAV‐Fog, and cloud server, to control UAV's data and also described some of the security issues faced by this cloud infrastructure. Further, this research article also sheds new light on some scenarios of UAV‐Fog for such deployments, applications, opportunities, challenges, and their major security threats and their countermeasures. Afterward, we design taxonomy of the collaboration of UAV‐Fog with their respective approaches.

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Scalable edge cloud platforms for IoT services

Cited by 32 publications

References 21 publications

Survey on Placement Methods in the Edge and Beyond

Survey on Placement Methods in the Edge and Beyond

An Efficient Computation Offloading Strategy with Mobile Edge Computing for IoT

Flying through the secure fog: A complete study on UAV‐Fog in heterogeneous networks

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