Cyclic reservation schemes for efficient operation of multiple-queue single-server systems

We study two new cyclic reservation schemes for the efficient operation of systems consisting of a single server and multiple queues. The schemes are the Globally Gated regime and the Cyclic-Reservation Multiple-Access (CRMA). Both procedures possess mechanisms for prioritizing the queues and lend themselves to a closed-form analysis. The combination of these two properties allows for effective and efficient operation of the systems, for which we provide a thorough delay analysis and derive simple rules for optimal operation.     

Admit or preserve? Addressing server failures in cloud computing task management

Cloud computing task management has a critical role in the efficient operation of the cloud resources, i.e., the servers. The task management handles critical and complicated decisions, overcoming the inherent dynamic nature of cloud computing systems and the additional complexity due to the large magnitude of resources in such systems (tens of thousands of servers). Due to the fact that servers may fail, task management is required to conduct both task admissions and task preservation decisions. Moreover, both these decisions require considering future system trajectories and the interplay between preservation and admission. In this paper we study the combined problem of task admission and preservation in a dynamic environment of cloud computing systems through analysis of a queueing system based on a Markov decision process (MDP). We show that the optimal operational policy is of a double switching curve type. On face value, the extraction of the optimal policy is rather complicated, yet our analysis reveals that the optimal policy can be reduced to a single rule, since the rules can effectively be decoupled. Based on this result, we propose two heuristic approaches that approximate the optimal rule for the most relevant system settings in cloud computing systems. Our results provide a simple policy scheme for the combined admission and preservation problem that can be applied in a complex cloud computing environments, and eliminate the need for sophisticated real-time control mechanisms.

     

Ultra‐Tough Inverse Artificial Nacre Based on Epoxy‐Graphene by Freeze‐Casting

Epoxy nanocomposites combining high toughness with advantageous functional properties are needed in many fields. However, fabricating high‐performance homogeneous epoxy nanocomposites with traditional methods remains a great challenge. Nacre with outstanding fracture toughness presents an ideal blueprint for the development of future epoxy nanocomposites. Now, high‐performance epoxy‐graphene layered nanocomposites were demonstrated with ultrahigh toughness and temperature‐sensing properties. These nanocomposites are composed of ca. 99 wt % organic epoxy, which is in contrast to the composition of natural nacre (ca. 96 wt % inorganic aragonite). These nanocomposites are named an inverse artificial nacre. The fracture toughness reaches about 4.2 times higher than that of pure epoxy. The electrical resistance is temperature‐sensitive and stable under various humidity conditions. This strategy opens an avenue for fabricating high‐performance epoxy nanocomposites with functional properties.     

Sampling potential energy surface of glycyl glycine peptide: Comparison of Metropolis Monte Carlo and stochastic dynamics

A comparative study was carried out to test the efficiency with which Metropolis Monte Carlo (MC) and stochastic dynamics (SD) sample the potential energy surface of the N-acetyl glycyl glycine methylamide peptide as defined by the united atom AMBER* force field. Boltzmann-weighted ensembles were generated with variations of all internal degrees of freedom (i.e., stretch, bend, and torsion) for a single N-acetyl glycyl glycine methylamide molecule at 300 K by 10⁸-step MC and 100-ns SD simulations. As expected, both methods gave the same final energetic results. However, convergence was found to be ∼10 times faster with MC than with SD as measured by comparisons of the populations of all symmetrically equivalent conformers. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1294–1299, 1998     

Rescoring of docking poses under Occam’s Razor: are there simpler solutions?

Ligand affinity prediction from docking simulations is usually performed by means of highly empirical and diverse protocols. These protocols often involve the re-scoring of poses generated by a force field (FF) based Hamiltonian to provide either estimated binding affinities—or alternatively, some empirical goodness score. Re-scoring is performed by so-called scoring functions—typically, a reweighted sum of FF terms augmented by additional terms (e.g., desolvation/entropic penalty, hydrophobicity, aromatic interactions etc.). Sometimes, the scoring function actually drives ligand positioning, but often it only operates on the best scoring poses ranked top by the initial ligand positioning tool. In either of these rather intricate scenarios, scoring functions are docking-specific models, and most require machine-learning-based calibration. Therefore, docking simulations are less straightforward when compared to “standard” molecular simulations in which the FF Hamiltonian defines the energy, and affinity emerges as an ensemble average property over pools of representative conformers (i.e., the trajectory). Paraphrasing on Occam’s Razor principle, additional model complexity is only acceptable if demonstrated to bring a significant improvement of prediction quality. In this work we therefore examined whether the complexity inherent to scoring functions is indeed justified. For this purpose we compared sampler for multiple protein–ligand entities, a general purpose conformation sampler based on the AMBER/GAFF FF, complemented with continuum solvation terms, with several state of the art docking tools that rely on calibrated scoring functions (Glide, Gold, Autodock-Vina) in terms of its ability to top-rank the actives from large and diverse ligand series associated with various proteins. There is no clear winner of this study, where each program performed well on most of the targets, but also failed with respect to at least one of them. Therefore, a well-parameterized force field with a simple, energy-based ligand ranking protocol appears to be an as effective docking protocol as intricate rescoring strategies based on scoring functions. A tool that can sample the conformational space of the free ligand, the bound ligand and the protein binding site using the same force field may avoid many of the approximations common to contemporary docking protocols and allow e.g., for docking into highly flexible active sites, when current scoring functions are not well suited to estimate receptor strain energies.     

Modified MM2 scheme for computation of O?C?N-containing systems

A modification of Allinger's MM2 force field for the anomeric effect in O? C? N systems is presented. For optimal consistency, it was parameterized by alternate use of ab initio (3-21G level) and X-ray results to account for the energetic and structural manifestations of the effect in the gas or condensed phase, respectively. The results obtained with the modified force field are in good agreement with those from both theoretical and experimental methods. The parameterization scheme explicitly treats all structural parameters of the C? O? C? N? C moiety as well as C? N bond lengths in tertiary amines contained within an anomeric unit. In addition, it includes directional H-bond type interactions. A limited number of parameters is put forward, in accord with the general MM2 force-field approach. © 1993 John Wiley & Sons, Inc.     

Promise as Practice Reason

Acta Analytica - To promise someone to do something is to commit oneself to that person to do that thing, but what does that commitment consist of? Some think a promissory commitment is an...     

A resource allocation queueing fairness measure: properties and bounds

Fairness is an inherent and fundamental factor of queue service disciplines in a large variety of queueing applications, ranging from airport waiting lines to computer queueing systems. We study a newly proposed measure, a Resource Allocation Queueing Fairness Measure (RAQFM), first introduced in Raz, Levy, and Avi-Itzhak (Perform. Eval. Rev. 32(1):130–141, 2004). We analyze the properties of RAQFM and tie them to intuition, provide bounds for its values, and discuss briefly how it yields to analysis. This work was supported in part by the Israeli Ministry of Science and Technology, grant number 380-801, and by EURO-NGI network of excellence.     

Modeling photothermal and acoustical induced microbubble generation and growth

Previous experimental studies showed that powerful heating of nanoparticles by a laser pulse using energy density greater than 100 mJ/cm², could induce vaporization and generate microbubbles. When ultrasound is introduced at the same time as the laser pulse, much less laser power is required. For therapeutic applications, generation of microbubbles on demand at target locations, e.g. cells or bacteria can be used to induce hyperthermia or to facilitate drug delivery. The objective of this work is to develop a method capable of predicting photothermal and acoustic parameters in terms of laser power and acoustic pressure amplitude that are needed to produce stable microbubbles; and investigate the influence of bubble coalescence on the thresholds when the microbubbles are generated around nanoparticles that appear in clusters.

We develop and solve here a combined problem of momentum, heat and mass transfer which is associated with generation and growth of a microbubble, filled with a mixture of non-vaporized gas (air) and water vapor. The microbubble’s size and gas content vary as a result of three mechanisms: gas expansion or compression, evaporation or condensation on the bubble boundary, and diffusion of dissolved air in the surrounding water. The simulations predict that when ultrasound is applied relatively low threshold values of laser and ultrasound power are required to obtain a stable microbubble from a single nanoparticle. Even lower power is required when microbubbles are formed by coalescence around a cluster of 10 nanoparticles. Laser pulse energy density of 21 mJ/cm² is predicted for instance together with acoustic pressure of 0.1 MPa for a cluster of 10 or 62 mJ/cm² for a single nanoparticle. Those values are well within the safety limits, and as such are most appealing for targeted therapeutic purposes.