Farah AIT SALAHT; Frédéric Desprez; Adrien Lebre
An Overview of Service Placement Problem in Fog and Edge Computing Article de journal
Dans: Acm Computing Surveys, vol. 53, no. 3, p. 1-35, 2020.
@article{ait_salaht_2680,
title = {An Overview of Service Placement Problem in Fog and Edge Computing},
author = {Farah AIT SALAHT and Frédéric Desprez and Adrien Lebre},
url = {https://doi.org/10.1145/3391196},
year = {2020},
date = {2020-06-01},
journal = {Acm Computing Surveys},
volume = {53},
number = {3},
pages = {1-35},
abstract = {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 complicate the deployment problem. This article 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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Farah AIT SALAHT; Maher REBAI; Nora IZRI
Optimizing Service Replication and Placement for IoT Applications in Fog Computing Systems Proceedings Article
Dans: European Conference on Parallel Processing, Madrid, Espagne, 2024.
@inproceedings{ait_salaht_3014,
title = {Optimizing Service Replication and Placement for IoT Applications in Fog Computing Systems},
author = {Farah AIT SALAHT and Maher REBAI and Nora IZRI},
url = {https://2024.euro-par.org/program/accepted-papers/},
year = {2024},
date = {2024-08-01},
booktitle = {European Conference on Parallel Processing},
address = {Madrid, Espagne},
abstract = {Fog Computing extends Cloud Computing to the network edge, enhancing distributed computing to meet the growing needs of Internet of Things (IoT) applications requiring real-time or near-real-time analysis. This research focuses on efficiently managing the vast amounts of data generated by IoT devices and the continuous data streams they produce, employing an advanced replication and placement strategy for application components across distributed Fog Computing nodes. This approach enables scalable and parallel data processing to adapt to demand fluctuations, prevent over-provisioning, and maintain low response times, making it particularly effective for the dynamic nature of data stream processing in IoT applications.
In this paper, we propose an Optimal IoT Service Replication and Placement (SRP) model, formulated as a constraint satisfaction problem, that considers the diverse requirements of IoT applications and the available infrastructure resources. Our model is designed to be adaptive and extensible, addressing the challenge of workload variability through real-time optimization.
Numerical evaluations confirm the superior performance and scalability of our model over existing methods, while maintaining quality of service constraints. This highlights the potential of our approach to improve efficiency and resource management in Fog Computing environments.},
note = {26-30/08/2024},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Farah AIT SALAHT; Mehdi Kandi; Hind Castel-Taleb; Emmanuel Hyon
Analysis of Performance and Energy Consumption in the Cloud Proceedings Article
Dans: Computer Performance Engineering, Berlin, Germany, 2017, ISBN: 978-3-319-66583-2.
@inproceedings{ait_salaht_2681,
title = {Analysis of Performance and Energy Consumption in the Cloud},
author = {Farah AIT SALAHT and Mehdi Kandi and Hind Castel-Taleb and Emmanuel Hyon},
url = {https://doi.org/10.1007/978-3-319-66583-2_13},
issn = {978-3-319-66583-2},
year = {2017},
date = {2017-08-01},
booktitle = {Computer Performance Engineering},
address = {Berlin, Germany},
abstract = {We analyze here a cloud system represented by hysteresis multi server queueing system. It is characterized by forward and backward thresholds for activation and deactivation of block of servers representing a set of VMs (Virtual Machines). The system is represented by a complex Markov Chain which is difficult to analyse when the size of the system is huge. We propose both analytical and numerical mathematical methods for deriving the steady-state probability distribution. We compute then performance and energy consumption measures and we define an overall cost taking into account both aspects. We compare the proposed methods with respect to the computation time and we analyse the impact of some parameters on the behaviour of the system.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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