582 research outputs found
Naval Reserve support to information Operations Warfighting
Since the mid-1990s, the Fleet Information Warfare Center (FIWC) has led the Navy's Information Operations (IO) support to the Fleet. Within the FIWC manning structure, there are in total 36 officer and 84 enlisted Naval Reserve billets that are manned to approximately 75 percent and located in Norfolk and San Diego Naval Reserve Centers. These Naval Reserve Force personnel could provide support to FIWC far and above what they are now contributing specifically in the areas of Computer Network Operations, Psychological Operations, Military Deception and Civil Affairs. Historically personnel conducting IO were primarily reservists and civilians in uniform with regular military officers being by far the minority. The Naval Reserve Force has the personnel to provide skilled IO operators but the lack of an effective manning document and training plans is hindering their opportunity to enhance FIWC's capabilities in lull spectrum IO. This research investigates the skill requirements of personnel in IO to verify that the Naval Reserve Force has the talent base for IO support and the feasibility of their expanded use in IO.http://archive.org/details/navalreservesupp109451098
Modelling Energy Consumption based on Resource Utilization
Power management is an expensive and important issue for large computational
infrastructures such as datacenters, large clusters, and computational grids.
However, measuring energy consumption of scalable systems may be impractical
due to both cost and complexity for deploying power metering devices on a large
number of machines. In this paper, we propose the use of information about
resource utilization (e.g. processor, memory, disk operations, and network
traffic) as proxies for estimating power consumption. We employ machine
learning techniques to estimate power consumption using such information which
are provided by common operating systems. Experiments with linear regression,
regression tree, and multilayer perceptron on data from different hardware
resulted into a model with 99.94\% of accuracy and 6.32 watts of error in the
best case.Comment: Submitted to Journal of Supercomputing on 14th June, 201
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