Modeling Data Center Building Blocks for Energy-efficiency and Thermal Simulations

International audienceIn this paper we present a concept and specification of Data Center Efficiency Building Blocks (DEBBs), which represent hardware components of a data center complemented by descriptions of their energy efficiency. Proposed building blocks contain hardware and thermodynamic models that can be applied to simulate a data center and to evaluate its energy efficiency. DEBBs are available in an open repository being built by the CoolEmAll project. In the paper we illustrate the concept by an example of DEBB defined for the RECS multi-server system including models of its power usage and thermodynamic properties. We also show how these models are affected by specific architecture of modeled hardware and differences between various classes of applications. Proposed models are verified by a comparison to measurements on a real infrastructure. Finally, we demonstrate how DEBBs are used in data center simulations

CoolEmAll D2.4 First release of the simulation and visualisation toolkit

This deliverable describes the realisation of the first prototype of the simulation, visualisation and decision support toolkit and the interaction of its components. It further describes the usage and the tests of the components of the 1st Prototype of the SVD toolkit. Another focus of this deliverable is describing the heterogeneous deployment architecture of the SVD toolkit and the invoking of the different components for performing an automatic simulation.This deliverable is split into four major parts. Each part describes the different properties of the individual components. Special focus is put on the distributed deployment architecture, realization, usage and tests of this 1st prototype

CoolEmAll: Models and Tools for Planning and Operating Energy Efficient Data Centres

International audienceThe need to improve how efficiently data centre operate is increasing due to the continued high demand for new data centre capacity combined with other factors such as the increased competition for energy resources. The financial crisis may have dampened data centre demand temporarily, but current projections indicate strong growth ahead. By 2020, it is estimated that annual investment in the construction of new data centres will rise to \ 50bn in the US, and \ 220bn worldwid

Mastering system and power measures for servers in datacenter

International audienceUsing power meters and performance counters to get insight on system's behavior in terms of power consumption is common nowadays. The values coming from these external or internal meters are usually used directly by the research community, for instance to derive higher-level power models with learning techniques or to use them in decision tools such as schedulers in HPC and Cloud Computing. While it is reasonable when one wants only to have a broad view on the power consumption, they can not be used directly in most cases: We prove in this article that the problems of distributed measure and hardware limits are way more complex and create bias, and we give the keys to understand and chose the proper methodology to handle these bias to obtain relevant values for enhanced usage. A generic methodology is analyzed and its main lessons extracted for a direct usage by the research community to master system and power measures for servers in datacenter