A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment

A heterogeneous cloud system, for example, a Hadoop 2.6.0 platform, provides distributed but cohesive services with rich features on large-scale management, reliability, and error tolerance. As big data processing is concerned, newly built cloud clusters meet the challenges of performance optimization focusing on faster task execution and more efficient usage of computing resources. Presently proposed approaches concentrate on temporal improvement, that is, shortening MapReduce time, but seldom focus on storage occupation; however, unbalanced cloud storage strategies could exhaust those nodes with heavy MapReduce cycles and further challenge the security and stability of the entire cluster. In this paper, an adaptive method is presented aiming at spatial–temporal efficiency in a heterogeneous cloud environment. A prediction model based on an optimized Kernel-based Extreme Learning Machine algorithm is proposed for faster forecast of job execution duration and space occupation, which consequently facilitates the process of task scheduling through a multi-objective algorithm called time and space optimized NSGA-II (TS-NSGA-II). Experiment results have shown that compared with the original load-balancing scheme, our approach can save approximate 47–55 s averagely on each task execution. Simultaneously, 1.254‰ of differences on hard disk occupation were made among all scheduled reducers, which achieves 26.6% improvement over the original scheme

An Adaptively Speculative Execution Strategy Based on Real-Time Resource Awareness in a Multi-Job Heterogeneous Environment

MapReduce (MRV1), a popular programming model, proposed by Google, has been well used to process large datasets in Hadoop, an open source cloud platform. Its new version MapReduce 2.0 (MRV2) developed along with the emerging of Yarn has achieved obvious improvement over MRV1. However, MRV2 suffers from long finishing time on certain types of jobs. Speculative Execution (SE) has been presented as an approach to the problem above by backing up those delayed jobs from low-performance machines to higher ones. In this paper, an adaptive SE strategy (ASE) is presented in Hadoop-2.6.0. Experiment results have depicted that the ASE duplicates tasks according to real-time resources usage among work nodes in a cloud. In addition, the performance of MRV2 is largely improved using the ASE strategy on job execution time and resource consumption, whether in a multi-job environment

Estimation Accuracy on Execution Time of Run-Time Tasks in a Heterogeneous Distributed Environment

Distributed Computing has achieved tremendous development since cloud computing was proposed in 2006, and played a vital role promoting rapid growth of data collecting and analysis models, e.g., Internet of things, Cyber-Physical Systems, Big Data Analytics, etc. Hadoop has become a data convergence platform for sensor networks. As one of the core components, MapReduce facilitates allocating, processing and mining of collected large-scale data, where speculative execution strategies help solve straggler problems. However, there is still no efficient solution for accurate estimation on execution time of run-time tasks, which can affect task allocation and distribution in MapReduce. In this paper, task execution data have been collected and employed for the estimation. A two-phase regression (TPR) method is proposed to predict the finishing time of each task accurately. Detailed data of each task have drawn interests with detailed analysis report being made. According to the results, the prediction accuracy of concurrent tasks’ execution time can be improved, in particular for some regular jobs

The Influence of Assembly Unit of Fibers on the Mechanical and Long-Term Properties of Reactive Powder Concrete

The corrosion of concrete structures by chloride salt is very significant in coastal environments. In order to improve the durability of marine concrete structures, cement-based materials with high durability need to be developed. In this investigation, the influence of NaCl freeze–thaw cycles (FT-C) and NaCl dry-wet alternations (DW-A) on the flexural and compressive strengths of reactive powder concrete (RPC) with an assembly unit of basalt fibers and steel fibers is studied. Additionally, the mass loss rate, the relative dynamic modulus of elasticity (RDEM), the chloride ion migration coefficient (CMC) and the impact toughness are measured after the NaCl FT-C and DW-A action. Our findings show that the RDEM, mass loss, and mechanical strength loss of RPC are increased by the ascending NaCl FT-C and DW-A. Meanwhile, the RDEM and the impact toughness are decreased by the NaCl FT-C and DW-A. The RPC with 0.5% basalt fibers and 1.5% steel fibers by volume of RPC shows the optimum mechanical performance and resistance to NaCl FT-C and DW-A. However, RPC with 3% steel fibers shows the worst resistance to NaCl erosion. The maximum mass loss rates, RDEM, flexural strength loss rate, compressive strength loss rate, CMC and impact toughness of all specimens after 300 NaCl FT-C and 30 NaCl DW-A are 4.5%, 91.7%, 28.1%, 29.3%, 3.2 × 10−12 (m2/s) and 2471 J. Meanwhile, the corresponding minimum values are 1.62%, 83.2%, 20.4%, 15.7%, 1.1 × 10−12 (m2/s) and 625 J. The researching findings will provide an optimum mix ratio of RPC with an assembly unit of basalt fibers and steel fibers, which can be applied in the marine engineering environment

Influence of NaCl Freeze–Thaw Cycles on the Mechanical Strength of Reactive Powder Concrete with the Assembly Unit of Sulphoaluminate Cement and Ordinary Portland Cement

The influence of sulphoaluminate cement and the dosage of polypropylene fibers on the basic mechanical strengths (compressive and flexural strengths) of reactive powder concrete (RPC) cured for 1 d, 3 d, 7 d, 14 d and 28 d is studied in this research. The content of sulphoaluminate cement ranges from 0% to 100% and the dosages of polypropylene fibers are 0%~3.5%, respectively. Moreover, the mechanical properties (compressive and flexural strengths), the relative dynamic elastic modulus (RDEM) and the chloride permeability of specimens with 50% sulphoaluminate cement and different dosages of polypropylene fibers are determined after the specimens are exposed to different NaCl freeze–thaw cycles. The water–binder ratio in this study is 0.25, and the sand-to-binder ratio is 1.25. Results show that the relationship between the mechanical strengths of RPC at early curing ages (lower than 7 d) and the sulphoaluminate cement content is a linear function with a positive correlation. However, when the curing age reaches 14 d, the compressive and flexural strengths decrease in the form of a linear function with the addition of sulphoaluminate cement. The correlation between the mechanical strengths and polypropylene fiber volume is a positive quadratic function. However, the mass loss rate and flexural strength loss rate increased in the form of a quadratic function, and RDEM shows a negative quadratic function with the freeze–thaw cycles. Moreover, the compressive strength loss rate increases linearly with the freeze–thaw cycle. The addition of polypropylene fibers can effectively improve the freeze–thaw resistance of cement mortar with an assembly unit of ordinary cement and sulphoaluminate cement

LETTER 1278 Gold(I)-Catalyzed Synthesis of Dihydrobenzofurans from Aryl Allyl Ethers

Abstract: Formation of dihydrobenzofurans from aryl allyl ethers was catalyzed by in situ generated PPh 3 AuOTf. This reaction appears to proceed by a Claisen rearrangement, followed by addition of the resulting phenol to the allyl group

Influence of CO₂ Curing on the Alkali-Activated Compound Mineral Admixtures’ Corrosion Resistance to NaCl Dry–Wet Alternations

In this study, the influence of CO2 curing on the corrosion resistance of reinforced alkali-activated compounds is investigated. Fly ash (FA) and blast furnace slag powder (BFS) are used as mineral admixtures. The specimens were subjected to dry–wet alternations with 3% NaCl, used to simulate a concrete structure under a corrosion environment. The ultrasonic velocity, mass loss rate, and electrical characteristics (such as electrical resistance, AC impedance spectra, and corrosion area rates determined by Tafel curves) are utilized to determine the degree of corrosion. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) are used to analyze the corrosion mechanism. Results show that the corrosion resistance is decreased by the addition of FA but improved by CO2 curing. When CO2 curing is provided, the addition of BFS shows a higher enhancing effect on the corrosion resistance than that of FA. The equivalent circuit diagram of reinforced alkali-activated compound mineral admixtures obtained by AC impedance spectra is composed of three electrical elements (electrical resistance and capacitance in parallel) in series. The X-ray diffraction results show that adding BFS and CO2 curing can decrease the rust’s iron oxides on the steel bars’ surface. Finally, as found in the SEM photos, BFS and CO2 curing can effectively improve the compactness of specimens. Meanwhile, the roughness of hydration is increased by CO2 curing

The Influence of Assembly Unit of Fibers on the Mechanical and Long-Term Properties of Reactive Powder Concrete

The corrosion of concrete structures by chloride salt is very significant in coastal environments. In order to improve the durability of marine concrete structures, cement-based materials with high durability need to be developed. In this investigation, the influence of NaCl freeze–thaw cycles (FT-C) and NaCl dry-wet alternations (DW-A) on the flexural and compressive strengths of reactive powder concrete (RPC) with an assembly unit of basalt fibers and steel fibers is studied. Additionally, the mass loss rate, the relative dynamic modulus of elasticity (RDEM), the chloride ion migration coefficient (CMC) and the impact toughness are measured after the NaCl FT-C and DW-A action. Our findings show that the RDEM, mass loss, and mechanical strength loss of RPC are increased by the ascending NaCl FT-C and DW-A. Meanwhile, the RDEM and the impact toughness are decreased by the NaCl FT-C and DW-A. The RPC with 0.5% basalt fibers and 1.5% steel fibers by volume of RPC shows the optimum mechanical performance and resistance to NaCl FT-C and DW-A. However, RPC with 3% steel fibers shows the worst resistance to NaCl erosion. The maximum mass loss rates, RDEM, flexural strength loss rate, compressive strength loss rate, CMC and impact toughness of all specimens after 300 NaCl FT-C and 30 NaCl DW-A are 4.5%, 91.7%, 28.1%, 29.3%, 3.2 × 10−12 (m2/s) and 2471 J. Meanwhile, the corresponding minimum values are 1.62%, 83.2%, 20.4%, 15.7%, 1.1 × 10−12 (m2/s) and 625 J. The researching findings will provide an optimum mix ratio of RPC with an assembly unit of basalt fibers and steel fibers, which can be applied in the marine engineering environment

Using a Backpropagation Artificial Neural Network to Predict Nutrient Removal in Tidal Flow Constructed Wetlands

Nutrient removal in tidal flow constructed wetlands (TF-CW) is a complex series of nonlinear multi-parameter interactions. We simulated three tidal flow systems and a continuous vertical flow system filled with synthetic wastewater and compared the influent and effluent concentrations to examine (1) nutrient removal in artificial TF-CWs, and (2) the ability of a backpropagation (BP) artificial neural network to predict nutrient removal. The nutrient removal rates were higher under tidal flow when the idle/reaction time was two, and reached 90 ± 3%, 99 ± 1%, and 58 ± 13% for total nitrogen (TN), ammonium nitrogen (NH4+-N), and total phosphorus (TP), respectively. The main influences on nutrient removal for each scenario were identified by redundancy analysis and were input into the model to train and verify the pollutant effluent concentrations. Comparison of the actual and model-predicted effluent concentrations showed that the model predictions were good. The predicted and actual values were correlated and the margin of error was small. The BP neural network fitted best to TP, with an R2 of 0.90. The R2 values of TN, NH4+-N, and nitrate nitrogen (NO3−-N) were 0.67, 0.73, and 0.69, respectively