Abstract. In this paper we propose a simulation framework that allows for the analysis of power and performance trade-offs for data centres that save energy via power management. The models are cooperating discrete-event and agent-based models, which enable a variety of data centre configurations, including various infrastructural choices, workload models, (heterogeneous) servers and power management strategies. The capabilities of our modelling and simulation approach is shown with an example of a 200-server cluster. A validation that compares our results, for a restricted model with a previously published numerical model is also provided.

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Data centres are playing a pivotal role in all cloud-based services (e-commerce, social networks, financial services, e-government, etc.). The performance of data centres is crucial for the acceptance of all these services by end-users. It is important to carefully design data centres with both performance and energy considerations in mind, as data centres are also known to use large amounts of electrical energy. For that purpose we have developed a modular simulation model (based on Anylogic) that can be used to study performance-energy trade-offs in data centre design. Key to such studies is the availability of a workload model. In this paper we present a workload characterisation model and algorithm using modern-day data science techniques, building on top of Jupyter Notebook and the ProFiDo platform. We present the method and show its versatility on a case study with real-world traces of 20 million entries, provided by the Dutch company better.be. CCS CONCEPTS• Theory of computation → Design and analysis of algorithms; • Computing methodologies → Modeling and simulation; Simulation evaluation;

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Abstract. Many new data centres have been built in recent years in order to keep up with the rising demand for server capacity. These data centres require a lot of electrical energy and cooling. Big data and video streaming are two heavily used applications in data centres. This paper experimentally investigates the possibilities and benefits of using cheap, low power and widely supported hardware in the form of a micro data centre with big data and video streaming as its main application area. For this purpose, multiple Raspberry Pi 2 Model B (RPi2)'s have been used in order to build a fully functional distributed Hadoop and video streaming setup that has acceptable performance and extends to new research opportunities. We experimentally validated the new setup to fit in a data centre environment by analysis of its performance, scalability, energy consumption, temperature and manageability. This paper proposes a high concurrency and low power setup in a small 1U form factor with an estimated number of 72 RPi2's as an interesting alternative to traditional rack servers.