I IP WDM Wprocessing 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 relaxed-W M I L P MILP 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.
IntroductionVelocity is data in motion, which is the speed at which data is fluxing in and processed in the data centers [1]. The flux rate can grow larger for applications collecting information from wide spatial or temporal domains. For instance, the Square Kilometre Array [2] telescope combines signals with a flow speed of 700TB/second of data received from thousands of small antennas spread over a distance of more than 3000 km. In another example, five million trade events created each day are scrutinized in real time to identify potential fraud. Five hundred million daily call detail records are analysed in real-time to predict customer churn faster [3].High-speed processing of such immense data volumes as produced by plentiful data sources calls for new processing and communications methodologies in the big data era. In [4] the authors study the minimization of overall cost for Big Data placement, processing, and movement across geodistributed datacenters. In [5], the authors presented an optimization technique to execute a sequence of MapReduce jobs in Geo-distributed DCs to minimize the time and pecuniary cost. The authors in [6] introduced technique to execute MapReduce jobs on multiple IoT nodes to locally process as much data as possible the raw data. The authors in [7] aimed to minimize the communication cost by satisfying as many big data queries as possible over a number of time slots. In-network processing is proposed in [8] to achieve network-awareness to save more bandwidth using custom routing, redundancy elimination and on-path data reduction. In [9], the authors developed a Mixed Integer Linear programing models for energy efficient cloud computing services in IP over WDM core networks.We developed in [10] and [11] MILP models to investigate the impact of the big data's volume, variety, and veracity on gre...