18 research outputs found
Infrared composition of the Large Magellanic Cloud
The evolution of galaxies and the history of star formation in the Universe
are among the most important topics in today's astrophysics. Especially, the
role of small, irregular galaxies in the star-formation history of the Universe
is not yet clear. Using the data from the AKARI IRC survey of the Large
Magellanic Cloud at 3.2, 7, 11, 15, and 24 {\mu}m wavelengths, i.e., at the
mid- and near-infrared, we have constructed a multiwavelength catalog
containing data from a cross-correlation with a number of other databases at
different wavelengths. We present the separation of different classes of stars
in the LMC in color-color, and color-magnitude, diagrams, and analyze their
contribution to the total LMC flux, related to point sources at different
infrared wavelengths
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Disaster-survivable cloud-network mapping
Cloud-computing services are provided to consumers through a network of servers and network equipment. Cloud-network (CN) providers virtualize resources [e.g., virtual machine (VM) and virtual network (VN)] for efficient and secure resource allocation. Disasters are one of the worst threats for CNs as they can causemassive disruptions andCN disconnection. A disaster may also induce post-disaster correlated, cascading failures which can disconnect more CNs. Survivable virtual-network embedding (SVNE) approaches have been studied to protect VNs against single physicallink/- node and dual physical-link failures in communication infrastructure, but massive disruptions due to a disaster and their consequences can make SVNE approaches insufficient to guarantee cloud-computing survivability. In this work, we study the problem of survivable CN mapping from disaster. We consider risk assessment, VM backup location, and post-disaster survivability to reduce the risk of failure and probability of CN disconnection and the penalty paid by operators due to loss of capacity.We formulate the proposed approach as an integer linear program and study two scenarios: a natural disaster, e.g., earthquake and a human-made disaster, e.g., weapons-of-mass-destruction attack. Our illustrative examples show that our approach reduces the risk of CN disconnection and penalty up to 90% compared with a baseline CNmapping approach and increases the CN survivability up to 100% in both scenarios. © 2014 Springer Science+Business Media New York