Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm

By Mohit Taneja, Alan Davy. IFIP/IEEE Symposium on Integrated Network Management, 2017

321 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d54202b49ec753c3974748ead34db6ba281b35bf

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In Computer Networks and Communications

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Citations in the corpus

The Internet of Things, Fog and Cloud Continuum: Integration and Challenges (2018, background): “Due to their limited range, they can be used in fog nodes for small buildings or campus [23, 24].”
A Review and State of Art of Internet of Things (IoT) (2021, background): “The network connectivity is to attach with IoT devices, extra fog nodes, cloud, routers, surrounded servers, alterations, and video shadowing cameras, etc. [70].”
Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions (2018, methodology): “However, availability and In order to ensure efﬁcient use of resources and network infrastructure in the Fog and cloud environment, Taneja and Davy [107] proposed a Module Mapping Algorithm, which efﬁciently deploys IoT Application Modules in the composite Fog-Cloud Infrastructure.”
Resource Management Approaches in Fog Computing: a Comprehensive Review (2019, methodology): “…identifying a best candidate solution using a fitness function with the aim of maximizing/minimizing its fitness value while the search-based solutions [26, 35, 44] used heuristic/ meta-heuristic techniques such as evolutionary algorithms, backtracking search, or firs/best-fit strategy.”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, methodology): “[140] Optimizes utilization of network resources.”
Resource Management in Fog/Edge Computing (2018, methodology): “constraints such as latency or deadline requirements during placement. In the FSPP, a fog node is characterized by three attributes, the CPU, memory, and storage, similar to the work of Taneja et al. [178]. The FSPP suggests a proactive approach where the placement is performed periodically to meet ”