Big Data meet green challenges : VM Consolidation impact on QoS and sustainability in CloudSim
Niazi, Ijlal Ahmed (2019)
Niazi, Ijlal Ahmed
School of Engineering Science, Tietotekniikka
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The number of connected devices is increasing day by day and is projected to reach approximately 50 billion by 2020. This leads to data generation on a whole new scale and such complex, vast and varied data is called Big Data. Big Data processing will lead the way into the future of smart and sustainable communities. Data Centers are needed as entities to deal with Big Data, but a major challenge is the immense energy consumption and danger towards sustainability. To counter these challenges, Virtual Machine (VM) consolidation is a technique for reducing energy and resource consumption of a cloud computing infrastructure. Several of these techniques have been studied with promising results but no solid work is present which analyzes their effects on a big data workload. This work aims to analyze the impact of VM consolidation in terms of Quality of Service (QoS) and Quality in Sustainability (QiS) by comparing a number of techniques in response to big data workload. This is done by simulating a cloud computing environment using CloudSim. The effect of VM consolidation along with Big Data characteristics is observed and results are presented based on which a combination of the algorithms is recommended.