Open AccessGreening big data networks: velocity impact
Author(s) -
AlSalim Ali M.,
ElGorashi Taisir E.H.,
Lawey Ahmed Q.,
Elmirghani Jaafar M.H.
Publication year - 2018
Publication title -
iet optoelectronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.379
H-Index - 42
eISSN - 1751-8776
pISSN - 1751-8768
DOI - 10.1049/iet-opt.2016.0165
Subject(s) - big data , computer science , data processing , volume (thermodynamics) , raw data , power (physics) , real time computing , path (computing) , process (computing) , data mining , database , computer network , physics , quantum mechanics , programming language , operating system
The authors investigate the impact of big data's velocity on greening IP over WDM networks. They classify the processing velocity of big data into two modes: expedited‐data and relaxed‐data modes. Expedited‐data demands higher amount of computational resources to reduce the execution time compared with the relaxed‐data. They developed a mixed integer linear programming model to progressively process big data at strategic locations, dubbed processing nodes (PNs), built into the network along the path from the source to the destination. The extracted information from the raw traffic is smaller in volume compared with the original traffic each time the data is processed, hence, reducing network power consumption. The results showed that up to 60% network power saving is achieved when nearly 100% of the data required relaxed processing. In contrast, only 15% of network power saving is gained when nearly 100% of the data required expedited processing. The authors obtained around 33% power saving in the mixed modes (i.e. when ∼50% of the data is processed in the relaxed mode and 50% of the data is processed in expedited mode), compared with the classical approach where all the processing is achieved inside the centralised data centres only.