Capacity Planning of Fog Computing Infrastructures for Smart Monitoring

Pinciroli, Riccardo; Gribaudo, Marco; Roveri, Manuel; Serazzi, Giuseppe

doi:10.1007/978-3-319-91632-3_6

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…The authors in [24] developed a real Wireless Sensor Network (WSN) with nodes capable of acquiring data at high frequencies and, at the same time, are equipped with solar panels. Similar works can be found in [25,26,27].…”

Section: Related Worksupporting

confidence: 76%

An energy harvesting solution for computation offloading in Fog Computing networks

Bozorgchenani

Disabato

Tarchi

et al. 2020

Computer Communications

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Section: Related Worksupporting

confidence: 76%

An energy harvesting solution for computation offloading in Fog Computing networks

Bozorgchenani

Disabato

Tarchi

et al. 2020

Computer Communications

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“…The implemented model is derived from a more complex version of the one in [32] that considers a completely different scenario: the smart monitoring of fog computing infrastructures. The key feature of these models is the dynamic management of the buffer of requests based on the intensity of arrivals and the expiration of a periodic trigger.…”

Section: Batching In Iot-based Healthcarementioning

confidence: 99%

Multi-formalism Models for Performance Engineering

2020

Self Cite

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Nowadays, the necessity to predict the performance of cloud and edge computing-based architectures has become paramount, in order to respond to the pressure of data growth and more aggressive level of service agreements. In this respect, the problem can be analyzed by creating a model of a given system and studying the performance indices values generated by the model’s simulation. This process requires considering a set of paradigms, carefully balancing the benefits and the disadvantages of each one. While queuing networks are particularly suited to modeling cloud and edge computing architectures, particular occurrences—such as autoscaling—require different techniques to be analyzed. This work presents a review of paradigms designed to model specific events in different scenarios, such as timeout with quorum-based join, approximate computing with finite capacity region, MapReduce with class switch, dynamic provisioning in hybrid clouds, and batching of requests in e-Health applications. The case studies are investigated by implementing models based on the above-mentioned paradigms and analyzed with discrete event simulation techniques.

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“…In [12] analytical model using continuous time Markov chain(CTMC) is proposed for evaluating the performance of fog node for heterogeneous fog nodes/containers. Capacity planning to determine the optimal amount of resources required at the fog, cloud-based on queueing network and Petri Nets given in [13] only initial stage of work is found, the details of work are missing. The work in [14] IoT is modeled as a closed system, experimental analysis of middleware API is done to predict the performance IoT system.…”

Section: Literature Surveymentioning

confidence: 99%

Scalability of M/M/c Queue based Cloud-Fog Distributed Internet of Things Middleware

Rathod¹,

Chowdhary²

2019

IJANA

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The Internet of Things (IoT) extends the Internet wherein real world things are part of a computing network. The IoT has seen exponential growth and according to Cisco prediction about 50 billion devices will be connected by 2020. Handling this massive scale is challenging research issue. In this paper, we use cloud-fog-edge based IoT middleware for distributed IoT service provisioning. We model IoT middleware using queueing network, perform analytical analysis of IoT middleware components. Followed by dynamic scaling algorithm which considers contention and coherency as limiting factors for scalability. It is used for quantitative analysis of IoT middleware with the increasing workload. The scalability function was evaluated using the simulation for important performance and scalability parameters namely throughput, CPU utilization and response time. It is observed that because of contention and coherency overhead, the proposed approach is able to scale sub-linearly which is practical compared ideal scalability of multi-server queueing network and not very restrictive as given universal scalability law(USL) applied for tightly coupled systems.

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Capacity Planning of Fog Computing Infrastructures for Smart Monitoring

Cited by 2 publications

References 10 publications

An energy harvesting solution for computation offloading in Fog Computing networks

An energy harvesting solution for computation offloading in Fog Computing networks

Multi-formalism Models for Performance Engineering

Scalability of M/M/c Queue based Cloud-Fog Distributed Internet of Things Middleware

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