A graph partitioning-based heuristic for runtime IoT data placement strategies in a fog infrastructure

Naas, Mohammed; Lemarchand, Laurent; Boukhobza, Jalil; Raipin, Philippe

doi:10.1145/3167132.3167217

Cited by 32 publications

(29 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Proposes a balanced energy-delay solution for IoT applications placement and energy consumption problem in Fog Computing. [107,108] C R , C N Provides a framework called iFogStor for IoT data placement in a Fog infrastructure.…”

Section: Categorymentioning

confidence: 99%

See 1 more Smart Citation

An Overview of Service Placement Problem in Fog and Edge Computing

2020

View full text Add to dashboard Cite

To support the large and various applications generated by the Internet of Things (IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement in such infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new classification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.

show abstract

Section: Categorymentioning

confidence: 99%

“…The placement decision considers the FN with the highest centrality value. [107,108] Minimizes the overall latency of storing and retrieving data in a Fog. Provides a geographical partition to decreases the problemsolving time.…”

Section: Solutions Referencesmentioning

confidence: 99%

An Overview of Service Placement Problem in Fog and Edge Computing

2020

View full text Add to dashboard Cite

show abstract

“…Subsequently, unbalanced subproblems are found. The aim of IFogStorG [ 118 ] is to enhance runtime performance and minimize the complexity of the data-placement strategy. Thus, enhanced technology can handle dynamic changes in the network topology.…”

Section: Classifications Of Ecas-iotmentioning

confidence: 99%

“…To solve this issue, they proposed a greedy algorithm, called IFogStorM, to minimize overall latency. Results showed that overall latency was reduced by 10% more than in IFogStorG [ 118 ] and by 6% more than in IFogStorZ [ 117 ].…”

Section: Classifications Of Ecas-iotmentioning

confidence: 99%

“…IoT data may include personal data such as data generated from health sensors. Examples of ECAs-IoT that lack data privacy and confidentiality requirements are discussed here: IFogStor [ 117 ], iFogStorZ [ 117 ], and iFogStorG [ 118 ] architectures lack the ability to keep IoT data private because data are stored as plain text in data hosts. A privacy procedure must be performed to protect IoT data from breaches.…”

Section: Current Ecas-iot Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Edge-Computing Architectures for Internet of Things Applications: A Survey

Hamdan

Ayyash

Almajali

2020

Sensors

136

View full text Add to dashboard Cite

The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.

show abstract

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

Kashyap

Kumar

2022

Concurrency and Computation

View full text Add to dashboard Cite

SUMMARY Load balancing (LB) is nothing but the systematic distribution of load over different servers. The fog server is handling the maximum data of the cloud server to enhance the advancement of users' requests. The growth in data requests is escalating, and fog computing has intensified the accessibility of the data. Fog computing achieves many challenges according to the demands of the users, but even so, some challenges require more progress. The problem faced by fog computing is LB due to an increase in traffic on the network layer. Various LB techniques have already been proposed in the cloud layer, but until now they have only been in progress in the fog layer. Inefficient LB may cause a decrease in service quality, like delays in response time, processing time, security, and many more. In this survey, several algorithms have been discussed that are based on LB, which works out the issue of overloaded data on the network. Some parameters that authors have focused in LB are latency, bandwidth, deadlines, cost, security, execution time, and response time. Other parameters based on fault tolerance are also discussed with their quality parameter table and algorithm. In addition to this some of the limitations of the author's work, that is discussed in this article.

show abstract

A graph partitioning-based heuristic for runtime IoT data placement strategies in a fog infrastructure

Cited by 32 publications

References 9 publications

An Overview of Service Placement Problem in Fog and Edge Computing

An Overview of Service Placement Problem in Fog and Edge Computing

Edge-Computing Architectures for Internet of Things Applications: A Survey

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

Contact Info

Product

Resources

About