Study Of Key Technologies For Energy-saving In Cloud Computing Centers Based On WMSNs

Energy consumption in cooling has been one of the main factors restricting the development of cloud computing centers due to their increasing, both quantity and scale, and the increasing in heat dissipated per unit surface area. In the U.S., the power consumption of data centers is 1.5% of total national electrical power consumption of the full year in 2006. In China, the power consumption of data centers account for about 1% of total national electrical power consumption of the full year in 2009. However,40% to 50% of the power consumption is cost to cool data centers. Therefore, a means of energy saving and emission reduction is to improve cooling efficiency of data centers, that is the current research of IBM, HP and other R&D Institutions of data centers.This dissertation focus on the imbalanced distribution of heat inside the data center, using the Wireless Multimedia Sensor Networks (WMSNs) to test the local hot spots instantly and cool the hot spots by job migration etc. to balance the distribution of heat in data center. This dissertation addresses the cloud computing center's local temperature management system based on WMSNs, which performs the real-time monitoring heat distribution to optimize datacenter performance in terms of energy consumption and throughput. The main tasks of the system are to detect and localize hotspots, and extract their characteristics for remove them. The specific contributions are as follows:The first is the dynamic spectrum allocation technology based on WMSNs. The thermal camera network in the local temperature management system uses the general wireless multimedia sensor platforms. It need real-time transmit characteristics series of hotspots in a hardware constrained environment. Thus this dissertation proposes the spectrum sensing and management method to prevent interference between WMSNs and other wireless devices.The second is cooperative localizing hotspots using the thermal camera network. This dissertation proposes the method to detect the real hotspot in the thermal using one thermal camera node, and the method to guide other thermal camera nodes find the hotspot by its information. Also this dissertation proposes the algorithm to cooperatively local the hotspot by multiple thermal camera nodes. The simulations prove that the location accuracy meets application requirements, and the algorithms can be used in hardware constrained environments. Last but not least, the characteristics of hotspots are extracted and compressed by nodes. The characteristics can indicate state, change and cause of the hotspot. It is conducive to selective effective way to remove hotspot. In addition, an asymmetrical compression method is proposed to extract two dimensional morphological characteristics of hotspots and to reconstruct it.Experiments prove that the local thermal management system proposed in this dissertation can remove hotspots and imbalance the distribution of heat, so that the servers in the cloud computing center can run in low temperature environment with low airflow from CRAC, and some energy can be saved.