Greening Big Data Networks: Velocity Impact

Iミ デエキゲ ;ヴデキIノWが ┘W キミ┗Wゲデキェ;デW デエW キマヮ;Iデ ラa Hキェ S;デ;げゲ ┗WノラIキデ┞ ラミ ェヴWWミキミェ IP ラ┗Wヴ WDM ミWデ┘ラヴニゲく WW Iノ;ゲゲキa┞ デエW processing velocity of big data into two modes: expedited-data processing mode and relaxed-data processing mode. Expedited-data demands higher amount of computational resources to reduce the execution time compared to the relaxedS;デ;く WW SW┗WノラヮWS ; Mキ┝WS IミデWェWヴ LキミW;ヴ Pヴラェヴ;ママキミェ ふMILPぶ マラSWノ デラ ヮヴラェヴWゲゲキ┗Wノ┞ ヮヴラIWゲゲ Hキェ S;デ;げゲ ヴ;┘ デヴ;aaキI ラa Hラデエ modes at strategic locations, dubbed processing nodes (PNs), built into the network along the path from the data source to the destination. During the processing of big data, the extracted information from the raw traffic is smaller in volume compared to the original big data traffic each time the data is processed, hence, reducing network power consumption. Our results showed that up to 60% network power saving is achieved when nearly 100% of the data in the network required relaxed-processing. In contrast, only 15% of network power saving is gained when nearly 100% of the data required expedited-processing. We obtained around 33% power saving in the mixed modes (i.e., when approximately 50% of the data is processed in the relaxed-mode and 50% of the data is processed in expedited-mode), compared to the classical approach where no PNs exist in the network and all the processing is achieved inside the centralized datacenters only.