Load Balancing in Fog Computing Using QoS

Harnal, Shilpi; Sharma, Gaurav; Seth, Nidhi; Mishra, Ravi Dutt

doi:10.1007/978-981-16-3448-2_8

Cited by 6 publications

(2 citation statements)

References 55 publications

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“…In our work, we design a load balancing algorithm that is QoS oriented, which targets the delay that users experience when using the deployed application. Similar works, like [6] propose the (OLBA) framework, which takes into account turn-around time and service delay and relies on Particle Swarm Optimization (PSO) for finding the best load balancing strategy but the approach is not fully decentralized, the same approach is followed by [7]. Then, Tripathy et al in [8] focus on the QoS parameters but in a smart city setting and a smart allocation scheme is performed through a genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Each node implements a Python web server based on the Flask 5 library, that once deployed with Docker, receives the traffic from a machine that acts as a traffic generator. The source of the application is published as open source 6 . The webserver implements the scheduling decision, indeed, when a new task arrives, it decides to execute it locally or forward it to another neighbour node according to the current configuration of migration ratios.…”

Section: Experimental Settingmentioning

confidence: 99%

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A Load Balancing Algorithm for Equalising Latency Across Fog or Edge Computing Nodes

Mattia

Pietrabissa

Beraldi

2023

IEEE Trans. Serv. Comput.

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When dealing with distributed applications in Edge or Fog computing environments, the service latency that the user experiences at a given node can be considered an indicator of how much the node itself is loaded with respect to the others. Indeed, only considering the average CPU time or the RAM utilisation, for example, does not give a clear depiction of the load situation because these parameters are application-and hardware-agnostic. They do not give any information about how the application is performing from the user's perspective, and they cannot be used for a QoS-oriented load balancing. In this paper, we propose a load balancing algorithm that is focused on the service latency with the objective of levelling it across all the nodes in a fully decentralised manner. In this way, no user will experience a worse QoS than the other. By providing a differential model of the system and an adaptive heuristic to find the solution to the problem in real settings, we show both in simulation and in a real-world deployment, based on a cluster of Raspberry Pi boards, that our approach is able to level the service latency among a set of heterogeneous nodes organised in different topology.

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Section: Related Workmentioning

confidence: 99%

Section: Experimental Settingmentioning

confidence: 99%