Thermal Fingerprinting—Multi-Dimensional Analysis of Computational Loads

Digital fingerprinting is used in several domains to identify and track variable activities and processes. In this paper, we propose a novel approach to categorize and recognize computational tasks based on thermal system information. The concept focuses on all kinds of data center environments to control required cooling capacity dynamically. The concept monitors basic thermal sensor data from each server and chassis entity. The respective, characteristic curves are merged with additional general system information, such as CPU load behavior, memory usage, and I/O characteristics. This results in two-dimensional thermal fingerprints, which are unique and achievable. The fingerprints are used as input for an adaptive, pre-active air-conditioning control system. This allows a precise estimation of the data center health status. First test cases and reference scenarios clarify a huge potential for energy savings without any negative aspects regarding health status or durability. In consequence, we provide a cost-efficient, light-weight, and flexible solution to optimize the energy-efficiency for a huge number of existing, conventional data center environments

Simplicity, reproducibility and scalability for huge wireless sensor network simulations.

Programa de P?s-Gradua??o em Ci?ncia da Computa??o. Departamento de Ci?ncia da Computa??o, Instituto de Ci?ncias Exatas e Biol?gicas, Universidade Federal de Ouro Preto.Neste trabalho apresentamos duas contribui??es para a literatura de redes de sensores sem fio (WSN). A primeira ? um modelo geral para alcan?ar a reprodutibilidade no n?vel do kernel em simuladores paralelos. Infelizmente, os usu?rios devem implementar do zero como suas simula??es se repetem em simuladores WSN, mas uma simula??o paralela ou distribu?da imp?e o princ?pio de concorr?ncia, n?o trivial de ser implementada por n?o especialistas. Testes usando o simulador chamado JSensor comprovaram que o modelo garante o n?vel mais restrito de reprodutibilidade, mesmo quando as simula??es adotam diferentes n?meros de threads ou diferentes m?quinas em m?ltiplas execu??es. A segunda contribui??o ? o simulador JSensor, um simulador paralelo de uso geral para aplica??es WSN de grande escala e algoritmos distribu?dos de alto n?vel. O JSensor introduz elementos de simula??o mais realistas, como o ambiente representado por c?lulas personaliz?veis e eventos de aplica??o que representam fen?menos naturais, como raios, vento, sol, chuva e muito mais. As c?lulas s?o colocadas em uma grade que representa o ambiente com caracter?sticas do espa?o definido pelos usu?rios, como temperatura, press?o e qualidade do ar. Avalia??es experimentais mostram que o JSensor tem boa escalabilidade em arquiteturas de computadores multi-core, alcan?ando um speedup de 7,45 em uma m?quina com 16 n?cleos com tecnologia Hyper-Threading, portanto 50% dos n?cleos s?o virtuais. O JSensor tamb?m provou ser 21% mais r?pido que o OMNeT++ ao simular um modelo do tipo flooding.In this work we present two contributions for the wireless sensor network (WSN) literature. The first one is a general model to achieve reproducibility in kernel level of parallel simulators. Unfortunately, users must implement how their simulations repeat from scratch in WSN simulators, but a parallel or distributed simulation imposes the concurrence principle, not trivial to be implemented by non-specialists. Tests using the simulator named JSensor proved that the model guarantees the most restrict level of reproducibility, even when simulations adopt different number of threads or different machines in multiple runs. The second contribution is the JSensor simulator, a parallel general purpose simulator for large scale WSN applications and high-level distributed algorithms. JSensor introduces more realistic simulation elements, such as the environment represented by customizable cells and application-events representing natural phenomena, such as lightning, wind, sun, rain and more. The cells are placed in a grid that represents the environment with characteristics of the space defined by the users, such as temperature, pressure and air quality. Experimental evaluations show that JSensor has good scalability in multi-core computer architectures, achieving a speedup of 7.45 in a machine with 16 cores with Hyper-Threading Technology, thus 50% of cores are virtual ones. JSensor also proved to be 21% faster than OMNeT++ while simulating a flooding model