arXiv subjects

Jaafar M. H. Elmirghani

Publications and source records attributed to Jaafar M. H. Elmirghani.

130 records · Page 8Linked to original sources

arXiv2018

On Energy Efficiency of Networks for Composable Datacentre Infrastructures

This paper evaluates the optimal scale of datacentre (DC) resource disaggregation for composable DC infrastructures and investigates the impact of present day silicon photonics technologies on the energy efficiency of different composable DC infrastructures. We formulated a mixed integer linear programming (MILP) model to this end. Our results show that present day silicon photonics technologies enable better network energy efficiency for rack-scale composable DCs compared to pod-scale composable DCs despite reported similarities in CPU and memory resource power consumption.

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arXiv2018

Impact of Link Failures on the Performance of MapReduce in Data Center Networks

In this paper, we utilize Mixed Integer Linear Programming (MILP) models to determine the impact of link failures on the performance of shuffling operations in MapReduce when different data center network (DCN) topologies are used. For a set of non-fatal single and multi-links failures, the results indicate that different DCNs experience different completion time degradations ranging between 5% and 40%. The best performance under links failures is achieved by a server-centric PON-based DCN.

cs.NI↗

arXiv2018

Energy Efficiency of Fog Computing Health Monitoring Applications

Fog computing offers a scalable and effective solution to overcome the increasing processing and networking demands of Internet of Thing (IoT) devices. In this paper, we investigate the use of fog computing for health monitoring applications. We consider a heart monitoring application where patients send their 30 minute recording of Electrocardiogram (ECG) signal for processing, analysis, and decision making at fog processing units within the time constraint recommended by the American Heart Association (AHA) to save heart patients when an abnormality in the ECG signal is detected. The locations of the processing servers are optimized so that the energy consumption of both the processing and networking equipment are minimised. The results show that processing the ECG signal at fog processing units yields a total energy consumption saving of up to 68% compared to processing the at the central cloud.

cs.NI↗

arXiv2018

Big Data Analytics for Wireless and Wired Network Design: A Survey

Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.

cs.NI↗