A Fuzzy Predictable Load Balancing Approach in Cloud Computing

Cloud computing is a new paradigm for hosting and delivering services on demand over the internet where users access services. It is an example of an ultimately virtualized system, and a natural evolution for data centers that employ automated systems management, workload balancing, and virtualization technologies. Live virtual machine (VM) migration is a technique to achieve load balancing in cloud environment by transferring an active overload VM from one physical host to another one without disrupting the VM. In this study, to eliminate whole VM migration in load balancing process, we propose a Fuzzy Predictable Load Balancing (FPLB) approach which confronts with the problem of overload VM, by assigning the extra tasks from overloaded VM to another similar VM instead of whole VM migration. In addition, we propose a Fuzzy Prediction Method (FPM) to predict VMs migration time. This approach also contains a multi-objective optimization model to migrate these tasks to a new VM host. In proposed FPLB approach there is no need to pause VM during migration time. Furthermore, considering this fact that VM live migration contrast to tasks migration takes longer to complete and needs more idle capacity in host physical machine (PM), the proposed approach will significantly reduce time, idle memory and cost consumption

Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments

© 2015, Springer Science+Business Media New York. Optimizing task scheduling in a distributed heterogeneous computing environment, which is a nonlinear multi-objective NP-hard problem, plays a critical role in decreasing service response time and cost, and boosting Quality of Service (QoS). This paper, considers four conflicting objectives, namely minimizing task transfer time, task execution cost, power consumption, and task queue length, to develop a comprehensive multi-objective optimization model for task scheduling. This model reduces costs from both the customer and provider perspectives by considering execution and power cost. We evaluate our model by applying two multi-objective evolutionary algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Genetic Algorithm (MOGA). To implement the proposed model, we extend the Cloudsim toolkit by using MOPSO and MOGA as its task scheduling algorithms which determine the optimal task arrangement among VMs. The simulation results show that the proposed multi-objective model finds optimal trade-off solutions amongst the four conflicting objectives, which significantly reduces the job response time and makespan. This model not only increases QoS but also decreases the cost to providers. From our experimentation results, we find that MOPSO is a faster and more accurate evolutionary algorithm than MOGA for solving such problems

Optimal competitiveness for the Rectilinear Steiner Arborescence problem

We present optimal online algorithms for two related known problems involving Steiner Arborescence, improving both the lower and the upper bounds. One of them is the well studied continuous problem of the {\em Rectilinear Steiner Arborescence} ($RSA$). We improve the lower bound and the upper bound on the competitive ratio for $RSA$ from $O(\log N)$ and $\Omega(\sqrt{\log N})$ to $\Theta(\frac{\log N}{\log \log N})$, where $N$ is the number of Steiner points. This separates the competitive ratios of $RSA$ and the Symetric-$RSA$, two problems for which the bounds of Berman and Coulston is STOC 1997 were identical. The second problem is one of the Multimedia Content Distribution problems presented by Papadimitriou et al. in several papers and Charikar et al. SODA 1998. It can be viewed as the discrete counterparts (or a network counterpart) of $RSA$. For this second problem we present tight bounds also in terms of the network size, in addition to presenting tight bounds in terms of the number of Steiner points (the latter are similar to those we derived for $RSA$)

Semi-automatic tumor boundary detection in MR image sequences

The authors present a semi-automatic approach for the detection of tumor boundary in MR image sequences. An initial slice with an obvious tumor is selected from the image sequence. The tumor is roughly segmented using fuzzy c-means algorithm and its boundary can be further refined by region and contour deformation. For the rest of the slices, the initial plan applied for each slice is extracted from the resulting boundary of the previous slice. The tumor boundary is located using region and contour deformation. Performance of our approach is evaluated on the MR image sequence. Comparisons with manual tracing show the accuracy and effectiveness of our approach.published_or_final_versio

Tumor boundary extraction in multislice MR brain images using region and contour deformation

In this paper, we present a new approach for the extraction of brain tumor boundary in a series of 2D MR image slices. The shape and position of tumor in one slice could be assumed to be similar to that in its neighboring slices. Using this correlation between consecutive images, the initial plan applied for each slice is extracted from the resulting boundary of the previous slice. The tumor boundary is located using region and contour deformation, which tolerates a rough initial plan. Therefore, only one coarse manual initial plan is required for the whole series of MR image slices. Performance of our approach is evaluated on MR image set. Comparisons with manual tracing show the accuracy and effectiveness of our approach.published_or_final_versio

Adherence to the 2018 World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR) Cancer Prevention Recommendations and risk of 14 lifestyle-related cancers in the UK Biobank prospective cohort study

\ua9 2023, The Author(s).Background: The World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR) Cancer Prevention Recommendations are lifestyle-based recommendations which aim to reduce cancer risk. This study investigated associations between adherence, assessed using a standardised scoring system, and the risk of all cancers combined and of 14 cancers for which there is strong evidence for links with aspects of lifestyle in the UK. Methods: We used data from 94,778 participants (53% female, mean age 56 years) from the UK Biobank. Total adherence scores (range 0–7 points) were derived from dietary, physical activity, and anthropometric data. Associations between total score and cancer risk (all cancers combined; and prostate, breast, colorectal, lung, uterine, liver, pancreatic, stomach, oesophageal, head and neck, ovarian, kidney, bladder, and gallbladder cancer) were investigated using Cox proportional hazard models, adjusting for age, sex, deprivation index, ethnicity, and smoking status. Results: Mean total score was 3.8 (SD 1.0) points. During a median follow-up of 8 years, 7296 individuals developed cancer. Total score was inversely associated with risk of all cancers combined (HR: 0.93; 95%CI: 0.90–0.95 per 1-point increment), as well as breast (HR: 0.90; 95%CI: 0.86–0.95), colorectal (HR: 0.90; 95%CI: 0.84–0.97), kidney (HR: 0.82; 95%CI: 0.72–0.94), oesophageal (HR: 0.84; 95%CI: 0.71–0.98), ovarian (HR: 0.76; 95%CI: 0.65–0.90), liver (HR: 0.78; 95%CI: 0.63–0.97), and gallbladder (HR: 0.70; 95%CI: 0.53–0.93) cancers. Conclusions: Greater adherence to lifestyle-based recommendations was associated with reduced risk of all cancers combined and of breast, colorectal, kidney, oesophageal, ovarian, liver, and gallbladder cancers. Our findings support compliance with the Cancer Prevention Recommendations for cancer prevention in the UK

Three-dimensional jamming and flows of soft glassy materials

Various disordered dense systems such as foams, gels, emulsions and colloidal suspensions, exhibit a jamming transition from a liquid state (they flow) to a solid state below a yield stress. Their structure, thoroughly studied with powerful means of 3D characterization, exhibits some analogy with that of glasses which led to call them soft glassy materials. However, despite its importance for geophysical and industrial applications, their rheological behavior, and its microscopic origin, is still poorly known, in particular because of its nonlinear nature. Here we show from two original experiments that a simple 3D continuum description of the behaviour of soft glassy materials can be built. We first show that when a flow is imposed in some direction there is no yield resistance to a secondary flow: these systems are always unjammed simultaneously in all directions of space. The 3D jamming criterion appears to be the plasticity criterion encountered in most solids. We also find that they behave as simple liquids in the direction orthogonal to that of the main flow; their viscosity is inversely proportional to the main flow shear rate, as a signature of shear-induced structural relaxation, in close similarity with the structural relaxations driven by temperature and density in other glassy systems.Comment: http://www.nature.com/nmat/journal/v9/n2/abs/nmat2615.htm

From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions

©2009 Gao, Skolnick. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.doi:10.1371/journal.pcbi.1000341DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Ca deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein

LES investigation into the generation of momentum deficits in the supersonic wake of a micro-ramp

Implicitly implemented large eddy simulation (LES) with a fifth-order WENO scheme was conducted in this study. Based on Navier-Stokes equations, this LES was carried out to explore the origin of momentum deficit caused by a supersonic micro-ramp at flow conditions of M a = 2.5 and Re θ = 5760. The numerical results were validated through qualitative and quantitative comparisons with existing experimental data. After describing the aerodynamic properties of the supersonic wake, such as the deficit and the streamwise vortices, the momentum deficit was later detected to originate from the lower portion of the upstream boundary layer, while the high momentum fluid originated from close to the wall at the upper portion. Position alternation trigged by the micro-ramp was finally proposed as a revised mechanism

Ge quantum dot arrays grown by ultrahigh vacuum molecular beam epitaxy on the Si(001) surface: nucleation, morphology and CMOS compatibility

Issues of morphology, nucleation and growth of Ge cluster arrays deposited by ultrahigh vacuum molecular beam epitaxy on the Si(001) surface are considered. Difference in nucleation of quantum dots during Ge deposition at low (<600 deg C) and high (>600 deg. C) temperatures is studied by high resolution scanning tunneling microscopy. The atomic models of growth of both species of Ge huts---pyramids and wedges---are proposed. The growth cycle of Ge QD arrays at low temperatures is explored. A problem of lowering of the array formation temperature is discussed with the focus on CMOS compatibility of the entire process; a special attention is paid upon approaches to reduction of treatment temperature during the Si(001) surface pre-growth cleaning, which is at once a key and the highest-temperature phase of the Ge/Si(001) quantum dot dense array formation process. The temperature of the Si clean surface preparation, the final high-temperature step of which is, as a rule, carried out directly in the MBE chamber just before the structure deposition, determines the compatibility of formation process of Ge-QD-array based devices with the CMOS manufacturing cycle. Silicon surface hydrogenation at the final stage of its wet chemical etching during the preliminary cleaning is proposed as a possible way of efficient reduction of the Si wafer pre-growth annealing temperature.Comment: 30 pages, 11 figure