Energy Saving in Data Processing and Communication Systems
نویسندگان
چکیده
منابع مشابه
Energy Saving in Data Processing and Communication Systems
The power management of ICT systems, that is, data processing (Dp) and telecommunication (Tlc) systems, is becoming a relevant problem in economical terms. Dp systems totalize millions of servers and associated subsystems (processors, monitors, storage devices, etc.) all over the world that need to be electrically powered. Dp systems are also used in the government of Tlc systems, which, beside...
متن کاملEnergy-saving method for technogenic waste processing
Dumps of a mining-metallurgical complex of post-Soviet Republics have accumulated a huge amount of technogenic waste products. Out of them, Kazakhstan alone has preserved about 20 billion tons. In the field of technogenic waste treatment, there is still no technical solution that leads it to be a profitable process. Recent global trends prompted scientists to focus on developing energy-saving a...
متن کاملEnergy Saving in Kiln Unit of of ABYEK CEMENT CO: Data Clustering Approach
Cost of cement producing all over the world depends on to the level of wages, energy cost and availability of raw materials. By investigating financial statements of various companies at the stock market, the share of electrical and fuel costs are nearly 27 percent of total costs and this plays the important role in right management of energy consumption. In this regard mathematics modeling and...
متن کاملElasticTree: Saving Energy in Data Center Networks
Networks are a shared resource connecting critical IT infrastructure, and the general practice is to always leave them on. Yet, meaningful energy savings can result from improving a network’s ability to scale up and down, as traffic demands ebb and flow. We present ElasticTree, a network-wide power manager, which dynamically adjusts the set of active network elements — links and switches — to s...
متن کاملHarnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication.
We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 ove...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Scientific World Journal
سال: 2014
ISSN: 2356-6140,1537-744X
DOI: 10.1155/2014/452863