An Overview of Service Placement Problem in Fog and Edge Computing

Salaht, Farah Aït; Desprez, Frédéric; Lèbre, Adrien

doi:10.1145/3391196

Cited by 181 publications

(106 citation statements)

References 139 publications

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“…Shakarami et al [30] reviewed the computation offloading approaches based on game-theoretic for mobile edge computing. Salaht et al [31] surveyed works addressing the service placement problem in edge computing. Yang and Rahmani [32] reviewed 15 articles designing heuristic or meta-heuristic task scheduling mechanisms in fog computing.…”

Section: C: Case 3: User 3 Moving Outside Edgesmentioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Wang

Huang

et al. 2020

IEEE Access

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Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.

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Section: C: Case 3: User 3 Moving Outside Edgesmentioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Wang

Huang

et al. 2020

IEEE Access

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“…The approach to be tackle was the ordinary idea of cloud computing facilities and infrastructure. Therefore, we present some recent efforts found in the literature, examples are [14,15,16] which are related to the fog-cloud environment cooperation. This cooperation is a vital element inside an architecture to collect and gather large amount of digital data.…”

Section: Related Work and Proposed Architecturementioning

confidence: 99%

“…In [14] it is stated that to support the large and various applications generated by the Internet of Things (IoT), fog computing was introduced to complement the cloud facilities. Thus, it offers cloud-like services at the edge of the network with low latency and real-time responses.…”

Section: Related Work and Proposed Architecturementioning

confidence: 99%

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Covid-19: A Digital Transformation Approach to a Public Primary Healthcare Environment

Nascimento

Iorio

Thomé

et al. 2020

2020 IEEE Symposium on Computers and Communications (ISCC)

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Digital transformation in e-health is a well-known challenge problem reported from several studies and from several dimensions. In addition, it has been verified a gap in the utilization of new technologies as differential tool in the war against the Covid-19 pandemic. In this paper, we present an ongoing research effort which is characterized for supporting a digital transformation gap found in a public primary healthcare system. Therefore, it can be seen as an interesting case study approach to tackle some challenges found in Covid-19. Utilizing smart bands by groups of different type of voluntaries, where vital signals were collected in a digital data fashion and then evaluated in public health unit. A recommendation system (RS) algorithm was also developed to understand users´ behaviors, based upon their vital signals. In addition, we utilized a simulator software to highlight people movement and predictable scenarios of Covid-19 contamination. This last effort provides a visualization on how the proposal could also help in a real ordinary monitoring scenario. Initial results from this research work indicates a differentiated approach to tackle challenges in digital transformation in a public health scenario, especially in a pandemic. In addition, our experiments illustrate that the adoption of some computational technologies require mainly changes on the present behavior, from governments and people, to be successful approaches to individual protection inside public environments.

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“…However, due to the limited capacity (CPU, storage, etc.) of FENs, only a subset of service images can be placed on each FEN [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

Choi

Ahn

2021

Sensors

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In recent years, we observed the proliferation of cloud data centers (CDCs) and the Internet of Things (IoT). Cloud computing based on CDCs has the drawback of unpredictable response times due to variant delays between service requestors (IoT devices and end devices) and CDCs. This deficiency of cloud computing is especially problematic in providing IoT services with strict timing requirements and as a result, gives birth to fog/edge computing (FEC) whose responsiveness is achieved by placing service images near service requestors. In FEC, the computing nodes located close to service requestors are called fog/edge nodes (FENs). In addition, for an FEN to execute a specific service, it has to be provisioned with the corresponding service image. Most of the previous work on the service provisioning in the FEC environment deals with determining an appropriate FEN satisfying the requirements like delay, CPU and storage from the perspective of one or more service requests. In this paper, we determined how to optimally place service images in consideration of the pre-obtained service demands which may be collected during the prior time interval. The proposed FEC environment is scalable in the sense that the resources of FENs are effectively utilized thanks to the optimal provisioning of services on FENs. We propose two approaches to provision service images on FENs. In order to validate the performance of the proposed mechanisms, intensive simulations were carried out for various service demand scenarios.

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An Overview of Service Placement Problem in Fog and Edge Computing

Cited by 181 publications

References 139 publications

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Covid-19: A Digital Transformation Approach to a Public Primary Healthcare Environment

Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

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