EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud

Cloud computing has become more popular in provision of computing resources under virtual machine (VM) abstraction for high performance computing (HPC) users to run their applications. A HPC cloud is such cloud computing environment. One of challenges of energy efficient resource allocation for VMs in HPC cloud is tradeoff between minimizing total energy consumption of physical machines (PMs) and satisfying Quality of Service (e.g. performance). On one hand, cloud providers want to maximize their profit by reducing the power cost (e.g. using the smallest number of running PMs). On the other hand, cloud customers (users) want highest performance for their applications. In this paper, we focus on the scenario that scheduler does not know global information about user jobs and user applications in the future. Users will request shortterm resources at fixed start times and non interrupted durations. We then propose a new allocation heuristic (named Energy-aware and Performance per watt oriented Bestfit (EPOBF)) that uses metric of performance per watt to choose which most energy-efficient PM for mapping each VM (e.g. maximum of MIPS per Watt). Using information from Feitelson's Parallel Workload Archive to model HPC jobs, we compare the proposed EPOBF to state of the art heuristics on heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF can reduce significant total energy consumption in comparison with state of the art allocation heuristics.Comment: 10 pages, in Procedings of International Conference on Advanced Computing and Applications, Journal of Science and Technology, Vietnamese Academy of Science and Technology, ISSN 0866-708X, Vol. 51, No. 4B, 201

Generating pointing motions for a humanoid robot by combining motor primitives

The human motor system is robust, adaptive and very flexible. The underlying principles of human motion provide inspiration for robotics. Pointing at different targets is a common robotics task, where insights about human motion can be applied. Traditionally in robotics, when a motion is generated it has to be validated so that the robot configurations involved are appropriate. The human brain, in contrast, uses the motor cortex to generate new motions reusing and combining existing knowledge before executing the motion. We propose a method to generate and control pointing motions for a robot using a biological inspired architecture implemented with spiking neural networks. We outline a simplified model of the human motor cortex that generates motions using motor primitives. The network learns a base motor primitive for pointing at a target in the center, and four correction primitives to point at targets up, down, left and right from the base primitive, respectively. The primitives are combined to reach different targets. We evaluate the performance of the network with a humanoid robot pointing at different targets marked on a plane. The network was able to combine one, two or three motor primitives at the same time to control the robot in real-time to reach a specific target. We work on extending this work from pointing to a given target to performing a grasping or tool manipulation task. This has many applications for engineering and industry involving real robots

Using the Rosat Catalogue to find Counterparts for Unidentified Objects in the 1st Fermi/LAT Catalogue

There are a total of 1451 gamma-ray emitting objects in the Fermi Large Area Telescope First Source Catalogue. The point source location accuracy of typically a few arcminutes has allowed the counterparts for many of these sources to be found at other wavelengths, but even so there are 630 which are described as having no plausible counterpart at 80% confidence. In order to help identify the unknown objects, we have cross-correlated the positions of these sources with the Rosat All Sky Survey Bright Source Catalogue. In this way, for Fermi sources which have a possible counterpart in soft X-rays, we can use the, much smaller, Rosat error box to search for identifications. We find a strong correlation between the two samples and calculate that there are about 60 sources with a Rosat counterpart. Using the Rosat error boxes we provide tentative associations for half of them, demonstrate that the majority of these are either blazars or blazar candidates and give evidence that most belong to the BL Lac class. Given that they are X-ray selected and most are high synchrotron peaked objects, which indicates the presence of high energy electrons, these sources are also good candidates for TeV emission, and therefore good probes of the extragalactic background light.Comment: 9 pages, 1 figure; Accepted for publication in MNRA

The MultiSite Spectroscopic Telescope campaign: 2m spectroscopy of the V361 Hya variable PG1605+072

We present results and analysis for the 2m spectroscopic part of the MultiSite Spectroscopic Telescope (MSST) campaign undertaken in May/June 2002. The goal of the project was to observe the pulsating subdwarf B star PG1605+072 simultaneously in velocity and photometry and to resolve as many of the >50 known modes as possible, which will allow a detailed asteroseismological analysis. We have obtained over 150 hours of spectroscopy, leading to an unprecedented noise level of only 207m/s. We report here the detection of 20 frequencies in velocity, with two more likely just below our detection threshold. In particular, we detect 6 linear combinations, making PG1605+072 only the second star known to show such frequencies in velocity. We investigate the phases of these combinations and their parent modes and find relationships between them that cannot be easily understood based on current theory. These observations, when combined with our simultaneous photometry, should allow asteroseismology of this most complicated of sdB pulsators.Comment: 9 pages, 5 figures, accepted for publication in A&A; Figure 1 at lower resolution than accepted versio