Optimizing the performance of optimization in the cloud environment–An intelligent auto-scaling approach

The cloud computing paradigm has gained wide acceptance in the scientific community, taking a significant share from fields previously reserved exclusively for High Performance Computing (HPC). On-demand access to a large amount of computing resources provided by Cloud makes it ideal for executing large-scale optimizations using evolutionary algorithms without the need for owning any computing infrastructure. In this regard, we extended WoBinGO, an existing parallel software framework for genetic algorithm based optimization, to be used in Cloud. With these extensions, the framework is capable of elastically and frugally utilizing the underlying cloud computing infrastructure for performing computationally expensive fitness evaluations. We studied two issues that are pertinent when dealing with large-scale optimization in the elastic cloud environment: the computing instance launching overhead and the price of engaging Cloud for solving optimization problems, in terms of the instances’ cumulative uptime. To explain the usability limits of WoBinGO framework running in the IaaS environment, a comprehensive analysis of the framework’s performance was given. Optimization of both total optimization time and total cumulative uptime, leads to minimizing the cost of cloud resources utilization. In this way, we are proposing an intelligent decision support engine based on artificial neural networks and metaheuristics to provide the user with an assessment of the framework’s behavior on the underlying infrastructure in terms of optimization duration and the cost of resource consumption. According to a given assessment, the user can decide upon faster delivery of results or lower infrastructure costs. The proposed software framework has been used to solve a complex real-world optimization problem of a subsurface rock mass model calibration. The results obtained from the private OpenStack deployment show that by using the proposed decision support engine, significant savings can be achieved in both optimization time and optimization cost

Design and Comparison of Two Web Service Based Frameworks for Parallel Evaluation of the Population in Genetic Algorithms

Genetic algorithms are powerful techniques for optimization of complexsystems. These methods require a large number of evaluations of candidate solutionswhich take huge CPU time. This paper introduces two web service based frameworksfor parallel evaluation of the population in genetic algorithm using the master-slavemodel. Developed frameworks can be easily incorporated into any genetic algorithm,giving a universal mechanism for distribution of individuals and collection of the eval-uation results. This concept provides parallelization of genetic algorithms on variousdistributed architectures, including multiprocessors and computing clusters. Performedtests have shown that proposed frameworks achieve signicant speedup, especially whenevaluating large-scale problems. In addition, a case study from the eld of hydrologyis presented

Elastic grid resource provisioning with WoBinGO: A parallel framework for genetic algorithm based optimization

In this paper, we present the WoBinGO (Work Binder Genetic algorithm based Optimization) framework for solving optimization problems over a Grid. It overcomes the shortcomings of earlier static pilot-job frameworks, by: (1) providing elastic resource provisioning thus avoiding unnecessary occupation of Grid resources; (2) providing friendliness towards other batching queue users thanks to adaptive allocation of jobs with limited lifetime. It hides the complexity of the underlying Grid environment, allowing the users to concentrate on the optimization problems. Theoretical analysis of possible speed-up is presented. An empirical study using an artificial problem, as well as a real-world calibration problem of a leakage model at the Visegrad power plant were performed. The obtained results show that despite WoBinGO’s adaptive and frugal allocation of computing resources, it provides significant speed-up when dealing with problems that have computationally expensive evaluations. Moreover, the benchmarks were performed in order to estimate the influence of the limited job lifetime feature on the queuing time of other batching jobs, compared to a static pilot-job infrastructure.Author's versio

Nebulin and Titin Modulate Cross-bridge Cycling and Length-dependent Calcium Sensitivity

Various mutations in the structural proteins nebulin and titin that are present in human disease are known to affect the contractility of striated muscle. Loss of nebulin is associated with reduced actin filament length and impairment of myosin binding to actin, whereas titin is thought to regulate muscle passive elasticity and is likely involved in length-dependent activation. Here, we sought to assess the modulation of muscle function by these sarcomeric proteins by using the computational platform muscle simulation code (MUSICO) to quantitatively separate the effects of structural changes, kinetics of cross-bridge cycling, and calcium sensitivity of the thin filaments. The simulations show that variation in thin filament length cannot by itself account for experimental observations of the contractility in nebulin-deficient muscle, but instead must be accompanied by a decreased myosin binding rate. Additionally, to match the observed calcium sensitivity, the rate of TnI detachment from actin needed to be increased. Simulations for cardiac muscle provided quantitative estimates of the effects of different titin-based passive elasticities on muscle force and activation in response to changes in sarcomere length and interfilament lattice spacing. Predicted force–pCa relations showed a decrease in both active tension and sensitivity to calcium with a decrease in passive tension and sarcomere length. We conclude that this behavior is caused by partial redistribution of the muscle load between active muscle force and titin-dependent passive force, and also by redistribution of stretch along the thin filament, which together modulate the release of TnI from actin. These data help advance understanding of how nebulin and titin mutations affect muscle function

Three-dimensional stochastic model of actin–myosin binding in the sarcomere lattice

The effect of molecule tethering in three-dimensional (3-D) space on bimolecular binding kinetics is rarely addressed and only occasionally incorporated into models of cell motility. The simplest system that can quantitatively determine this effect is the 3-D sarcomere lattice of the striated muscle, where tethered myosin in thick filaments can only bind to a relatively small number of available sites on the actin filament, positioned within a limited range of thermal movement of the myosin head. Here we implement spatially explicit actomyosin interactions into the multiscale Monte Carlo platform MUSICO, specifically defining how geometrical constraints on tethered myosins can modulate state transition rates in the actomyosin cycle. The simulations provide the distribution of myosin bound to sites on actin, ensure conservation of the number of interacting myosins and actin monomers, and most importantly, the departure in behavior of tethered myosin molecules from unconstrained myosin interactions with actin. In addition, MUSICO determines the number of cross-bridges in each actomyosin cycle state, the force and number of attached cross-bridges per myosin filament, the range of cross-bridge forces and accounts for energy consumption. At the macroscopic scale, MUSICO simulations show large differences in predicted force-velocity curves and in the response during early force recovery phase after a step change in length comparing to the two simplest mass action kinetic models. The origin of these differences is rooted in the different fluxes of myosin binding and corresponding instantaneous cross-bridge distributions and quantitatively reflects a major flaw of the mathematical description in all mass action kinetic models. Consequently, this new approach shows that accurate recapitulation of experimental data requires significantly different binding rates, number of actomyosin states, and cross-bridge elasticity than typically used in mass action kinetic models to correctly describe the biochemical reactions of tethered molecules and their interaction energetics

Modeling the Actin.myosin ATPase cross-bridge cycle for skeletal and cardiac muscle myosin isoforms

Modeling the complete actin.myosin ATPase cycle has always been limited by the lack of experimental data concerning key steps of the cycle, because these steps can only be defined at very low ionic strength. Here, using human ?-cardiac myosin-S1, we combine published data from transient and steady-state kinetics to model a minimal eight-state ATPase cycle. The model illustrates the occupancy of each intermediate around the cycle and how the occupancy is altered by changes in actin concentration for [actin] = 1–20Km. The cycle can be used to predict the maximal velocity of contraction (by motility assay or sarcomeric shortening) at different actin concentrations (which is consistent with experimental velocity data) and predict the effect of a 5 pN load on a single motor. The same exercise was repeated for human ?-cardiac myosin S1 and rabbit fast skeletal muscle S1. The data illustrates how the motor domain properties can alter the ATPase cycle and hence the occupancy of the key states in the cycle. These in turn alter the predicted mechanical response of the myosin independent of other factors present in a sarcomere, such as filament stiffness and regulatory proteins. We also explore the potential of this modeling approach for the study of mutations in human ?-cardiac myosin using the hypertrophic myopathy mutation R453C. Our modeling, using the transient kinetic data, predicts mechanical properties of the motor that are compatible with the single-molecule study. The modeling approach may therefore be of wide use for predicting the properties of myosin mutations

State, Institutional Infrastructure and Transition Engineering in Republic of Serbia

Radical changes in the transition should enable the restructuring of the economy and financial markets, involvement in European and worldwide integration streams, influx of capital and creation of a favorable business environment. The creation of market infrastructure demands time and cannot, except in specific segments, be created by decrees. The implementation of changes in the economical and institutional environment has to be a sensibly channeled process. Apart from the reconstruction of the existing institutions, new institutions have to be created and substantial changes of their functioning have to be implemented. The underlying assumptions in the process of establishment of the goals and means of transforming the Serbian economy are the norms of a liberal market economy and the demands for an integral market and the universal action of market mechanisms. Thereby, a critical standpoint and selectiveness in the choice of methods for the creation of the transition, especially in the case of an installation of liberalism norms, must exist. The worldwide financial crisis points out the significant limitations of neoliberalism. The role of interventionism through state is more significant

Competition policy and Economic Efficiency

The prime purpose of competition policy is to promote and maintain a process of effective competition so as to achieve a more efficient allocation of resources. The improvement of competition regime is necessary for ensuring competitiveness of goods and services. The purpose and aims of competition policy (giving in the competition law) are general protection of competition, anti-collusion, anti-monopoly and merger control, conduct of public agents (including firms enjoying exclusive rights). Competition policy also depends on the pro-competitive nature of other parts of economic policy (for example, trade policy)