2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC) 2021
DOI: 10.1109/icfec51620.2021.00010
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Multilayer Resource-aware Partitioning for Fog Application Placement

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Cited by 13 publications
(13 citation statements)
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“…Extensions. This paper extends our early work [18] on application placement in a static multilayer Fog in three areas. 1) We model the evolving Fog as a dynamic multilayer graph based on the incremental network changes and device availability; 2) We design a new dynamic placement algorithm based on an incremental multilayer resource partitioning method considering infrastructure changes; 3) We compare our results against three state-of-the-art methods using two applications running on a real testbed.…”
Section: Introductionsupporting
confidence: 53%
“…Extensions. This paper extends our early work [18] on application placement in a static multilayer Fog in three areas. 1) We model the evolving Fog as a dynamic multilayer graph based on the incremental network changes and device availability; 2) We design a new dynamic placement algorithm based on an incremental multilayer resource partitioning method considering infrastructure changes; 3) We compare our results against three state-of-the-art methods using two applications running on a real testbed.…”
Section: Introductionsupporting
confidence: 53%
“…Researchers studied various areas such as application placement, security, increasing response time, load balancing, request management, resource management, network optimization, etc. Spinnewynet al [7], Brogi et al [8], Cao et al [9], Mouradian et al [10], Kim et al [11], Mahmud et al [12][13], Baranwal et al [14], Kayal et.al [15], Xia et al [16],Mann [17] and Smani et al [18]consider application placement.…”
Section: Related Workmentioning
confidence: 99%
“…Mann [17] does application placement for individual Fog colonies and reduces the scalability problem. Smani et al [18] propose a resource-aware multilayer partitioning method to minimize resource wastage and maximize service placement and deadline satisfaction rate in a Fog environment. Sofiaet et al [19], in their paper, to effectively control energy consumption, use a Non-dominated Sorting Genetic Algorithm (NSGA-II) and artificial neural network (ANN) to predict virtual machines based on task characteristics and resource characteristics.…”
Section: Related Workmentioning
confidence: 99%
“…4) Data transfer reduction: Najafabadi Samani [11] proposed a multilayer partitioning method to minimize the resource wastage of Fog infrastructure that selects devices in resource partitions closest to the end-user. However, this work focuses on latency-sensitive workflows in Fog and Edge, isolated from the Cloud.…”
Section: Related Workmentioning
confidence: 99%