Fog Computing is a promising networking paradigm enabling the nodes at the edge to share computational and storage resources. Being pervasively distributed, Fog Nodes are often battery powered and, for this reason, an efficient energy management should be considered to prolong network lifetime. In this paper, we introduce a smart energy management solution able to exploit information about the predicted harvested and consumed energy by Fog Nodes, equipped with small solar panels. The smart energy management is applied on a cluster based Fog Computing environment where computation offloading operations are performed. In the experimental section the effect of the smart energy management is explored in terms of network lifetime by considering variable battery size and Fog Nodes density in a realistic solar-panel harvesting-model and Fog Nodes setting.

An energy harvesting solution for computation offloading in Fog Computing networks / Bozorgchenani, Arash; Disabato, Simone; Tarchi, Daniele; Roveri, Manuel. - In: COMPUTER COMMUNICATIONS. - ISSN 0140-3664. - ELETTRONICO. - 160:(2020), pp. 577-587. [10.1016/j.comcom.2020.06.032]

An energy harvesting solution for computation offloading in Fog Computing networks

Tarchi, Daniele;
2020

Abstract

Fog Computing is a promising networking paradigm enabling the nodes at the edge to share computational and storage resources. Being pervasively distributed, Fog Nodes are often battery powered and, for this reason, an efficient energy management should be considered to prolong network lifetime. In this paper, we introduce a smart energy management solution able to exploit information about the predicted harvested and consumed energy by Fog Nodes, equipped with small solar panels. The smart energy management is applied on a cluster based Fog Computing environment where computation offloading operations are performed. In the experimental section the effect of the smart energy management is explored in terms of network lifetime by considering variable battery size and Fog Nodes density in a realistic solar-panel harvesting-model and Fog Nodes setting.
2020
160
577
587
Bozorgchenani, Arash; Disabato, Simone; Tarchi, Daniele; Roveri, Manuel
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1381028
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