A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.     

Modelling microtubules in the brain as n-qudit quantum Hopfield network and beyond

The scientific approach to understand the nature of consciousness revolves around the study of the human brain. Neurobiological studies that compare the nervous system of different species have accorded the highest place to humans on account of various factors that include a highly developed cortical area comprising of approximately 100 billion neurons, that are intrinsically connected to form a highly complex network. Quantum theories of consciousness are based on mathematical abstraction and the Penrose–Hameroff Orch-OR theory is one of the most promising ones. Inspired by the Penrose–Hameroff Orch-OR theory, Behrman et al. have simulated a quantum Hopfield neural network with the structure of a microtubule. They have used an extremely simplified model of the tubulin dimers with each dimer represented simply as a qubit, a single quantum two-state system. The extension of this model to n-dimensional quantum states or n-qudits presented in this work holds considerable promise for even higher mathematical abstraction in modelling consciousness systems.     

A novel bacterial foraging technique for edge detection

A new approach for edge detection using a combination of bacterial foraging algorithm (BFA) and probabilistic derivative technique derived from Ant Colony Systems, is presented in this paper. The foraging behavior of some species of bacteria like Escherichia coli can be hypothetically modeled as an optimization process. A group of bacteria search for nutrients in a way that maximizes the energy obtained per unit time spent during the foraging. The proposed approach aims at driving the bacteria through the edge pixels. The direction of movement of the bacteria is found using a direction probability matrix, computed using derivatives along the possible directions. Rules defining the derivatives are devised to ensure that the variation of intensity due to noise is discarded. Quantitative analysis of the feasibility of the proposed approach and its comparison with other standard edge detection operators in terms of kappa and entropy are given. The effect of initial values of parameters of BFA on the edge detection is discussed.     

Distributed Symbolic Bounded Property Checking

In this paper we describe an algorithm for distributed, BDD-based bounded property checking and its implementation in the verification tool SymC. The distributed algorithm verifies larger models and returns results faster than the sequential version.The core algorithm distributes partitions of the state set to computation nodes after reaching a threshold size. The nodes proceed with image computation on the nodes asynchronously. The main scalability problem of this scheme is the overlap of state set partitions. We present static and dynamic overlap reduction techniques.     

Beta-hairpins with native-like and non-native hydrogen bonding patterns could form during the refolding of staphylococcal nuclease

Refolding of staphylococcal nuclease has been studied recently by hydrogen-deuterium exchange and NMR spectroscopy. These studies infer that beta-hairpin formed by strand 2 and strand 3 connected by reverse turn forms early during the refolding of nuclease. Typically, hydrogen-deuterium exchange NMR techniques are usually carried out on a time scale of milliseconds whereas beta-hairpins are known to fold on a much shorter time scale. It follows that in the experiments, the hydrogen-deuterium exchange protection patterns could be arising from a significant population of fully formed hairpins. In order to demonstrate it is the fully formed hairpins which gives rise to the hydrogen-deuterium exchange protection patterns, we have considered molecular dynamics simulation of the peptide (21)DTVKLMYKGQPMTFR(35) from staphylococcal nuclease corresponding to the beta-hairpin region, using GROMOS96 force field under NVT conditions. Starting from unfolded conformational states, the peptide folds into hairpin conformations with native-like and non-native hydrogen bonding patterns. Subsequent to folding, equilibrium conditions prevail. The computed protection factors and atom depth values, at equilibrium, of the various amide protons agree qualitatively with experimental observations. A collection of molecules following the trajectories observed in the simulations can account for experimental observations. These simulations provide a molecular picture of the formed hairpins and their conformational features during the refolding experiments on nuclease, monitored by hydrogen-deuterium exchange.     

Effect of impurities (graphite) on the high-temperature creep properties of NiAl

Previous high-temperature compression creep studies of NiAl have shown peculiar behaviour in the temperature range 700 to 900° C, which was perhaps due to precipitation of impurities in the matrix. To isolate the impurity effect, high-purity NiAl samples with 0.15 at% and 0.20 at% carbon (graphite) additions have been creep tested at four temperatures between 700 and 850° C. Addition of graphite has been shown to produce a significant reduction in the creep strength of the alloyS. However, alloys with higher graphite concentrations have shown better creep resistance than those with lower graphite concentrations. Transmission electron microscopy indicates the presence of competing softening and hardening mechanisms in the alloys. Softening is due to the graphite particles acting as a dislocation source. Hardening results from a grain-boundary hardening mechanism due to the graphite particles segregating at grain boundaries and a dislocation-impurity (fine graphite) interaction, developing a Cottrell-like atmosphere.     

A framework for joint community detection across multiple related networks

Community detection in networks is an active area of research with many practical applications. However, most of the early work in this area has focused on partitioning a single network or a bipartite graph into clusters/communities. With the rapid proliferation of online social media, it has become increasingly common for web users to have noticeable presence across multiple web sites. This raises the question whether it is possible to combine information from several networks to improve community detection. In this paper, we present a framework that identifies communities simultaneously across different networks and learns the correspondences between them. The framework is applicable to networks generated from multiple web sites as well as to those derived from heterogeneous nodes of the same web site. It also allows the incorporation of prior information about the potential relationships between the communities in different networks. Extensive experiments have been performed on both synthetic and real-life data sets to evaluate the effectiveness of our framework. Our results show superior performance of simultaneous community detection over three alternative methods, including normalized cut and matrix factorization on a single network or a bipartite graph.     

Evaluation of hot corrosion resistance of HVOF coatings on a Ni-based superalloy in molten salt environment

High-velocity oxy-fuel (HVOF) has the advantage of being a continuous and most convenient process for applying coatings to industrial installations at site. In this study, Cr₃C₂–NiCr, NiCrBSi, Stellite-6 and Ni–20Cr coatings were deposited on a Ni-based superalloy (19.5Cr–3Fe–0.3Ti–0.1C–balance Ni) using an HVOF process. Hot corrosion studies were performed on bare as well as coated superalloy specimens after exposure to a molten salt environment at 900 °C under cyclic conditions. The thermogravimetric technique was used to establish the kinetics of corrosion. XRD, SEM/EDAX and EPMA techniques were used to analyse the corrosion products. The hot corrosion resistance of all the coatings were superior to bare superalloy. Among the coatings studied, the Ni–20Cr coated superalloy imparted maximum hot corrosion resistance, whereas the Stellite-6 coated indicated minimum resistance. The hot corrosion resistance of all coatings may be attributed to the formation of oxides and spinels of nickel, chromium or cobalt.