author	 = {Jakob Lüttgau and Julian Kunkel},
	title	 = {{Cost and Performance Modeling for Earth System Data Management and Beyond}},
	year	 = {2019},
	month	 = {01},
	booktitle	 = {{High Performance Computing: ISC High Performance 2018 International Workshops, Frankfurt/Main, Germany, June 28, 2018, Revised Selected Papers}},
	editor	 = {Rio Yokota and Michele Weiland and John Shalf and Sadaf Alam},
	publisher	 = {Springer},
	series	 = {Lecture Notes in Computer Science},
	number	 = {11203},
	pages	 = {23--35},
	conference	 = {HPC-IODC workshop, ISC HPC},
	organization	 = {ISC Team},
	location	 = {Frankfurt, Germany},
	isbn	 = {978-3-030-02465-9},
	issn	 = {1611-3349},
	doi	 = {},
	abstract	 = {Current and anticipated storage environments confront domain scientist and data center operators with usability, performance and cost challenges. The amount of data upcoming system will be required to handle is expected to grow exponentially, mainly due to increasing resolution and affordable compute power. Unfortunately, the relationship between cost and performance is not always well understood requiring considerable effort for educated procurement. Within the Centre of Excellence in Simulation of Weather and Climate in Europe (ESiWACE) models to better understand cost and performance of current and future systems are being explored. This paper presents models and methodology focusing on, but not limited to, data centers used in the context of climate and numerical weather prediction. The paper concludes with a case study of alternative deployment strategies and outlines the challenges anticipating their impact on cost and performance. By publishing these early results, we would like to make the case to work towards standard models and methodologies collaboratively as a community to create sufficient incentives for vendors to provide specifications in formats which are compatible to these modeling tools. In addition to that, we see application for such formalized models and information in I/O re lated middleware, which are expected to make automated but reasonable decisions in increasingly heterogeneous data centers.},