DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS

Grid, an infrastructure for resource sharing, currently has shown its importance in many scientific applications requiring tremendously high computational power. Grid computing enables sharing, selection and aggregation of resources for solving complex and large-scale scientific problems. Grids computing, whose resources are distributed, heterogeneous and dynamic in nature, introduces a number of fascinating issues in resource management. Grid scheduling is the key issue in grid environment in which its system must meet the functional requirements of heterogeneous domains, which are sometimes conflicting in nature also, like user, application, and network. Moreover, the system must satisfy non-functional requirements like reliability, efficiency, performance, effective resource utilization, and scalability. Thus, overall aim of this research is to introduce new grid scheduling algorithms for resource allocation as well as for job scheduling for enabling a highly efficient and effective utilization of the resources in executing various applications. The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. The issue is not only to develop new algorithms, but also to evaluate them on an experimental computational grid, using synthetic and real workload traces, along with the other existing job scheduling algorithms. Experimental evaluation confirmed that the proposed grid scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability

DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS

Grid, an infrastructure for resource sharing, currently has shown its importance in many scientific applications requiring tremendously high computational power. Grid computing enables sharing, selection and aggregation of resources for solving complex and large-scale scientific problems. Grids computing, whose resources are distributed, heterogeneous and dynamic in nature, introduces a number of fascinating issues in resource management. Grid scheduling is the key issue in grid environment in which its system must meet the functional requirements of heterogeneous domains, which are sometimes conflicting in nature also, like user, application, and network. Moreover, the system must satisfy non-functional requirements like reliability, efficiency, performance, effective resource utilization, and scalability. Thus, overall aim of this research is to introduce new grid scheduling algorithms for resource allocation as well as for job scheduling for enabling a highly efficient and effective utilization of the resources in executing various applications. The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical\ud analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. The issue is not only to develop new algorithms, but also to evaluate them on an experimental computational grid, using synthetic and real workload traces, along with the other existing job scheduling algorithms. Experimental evaluation confirmed that the proposed grid scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability

Can Common Stocks Provide Hedge against Inflation? Evidence from SAARC Countries

The theory says that if stocks provide an effective hedge against inflation then the effect of expected inflation should be compensated in the form of nominal stock return. As Fisher Hypothesis (1930) concluded that nominal expected return on a security is a function of expected inflation rate as well as expected real interest rate. Bodie (1976) worked on Fisher Hypothesis and found that actual nominal return depends on expected and unexpected inflation rates and also it depends on expected and unexpected nominal returns. According to Geske and Roll (1983) a positive relationship exists between stock returns and inflation, based on the assumption that securities represent claims on real assets. When there is an increase in rate of inflation, it is expected that prices of real assets will also rise, thereby improving the value of securities representing a claim on such real assets. We found that various studies in this area reported against the hypothesis, showing a negative relationship between the two. However, certain other studies support the theory asserting that the relationship existing between stock returns and inflation is positive. While the negative relationship between inflation and stock return is against the theory, negative results have led to formation of hypothesis such as tax augmented hypothesis. The tax augmented hypothesis states that when we deduct tax from the stock returns, their relationship with inflation tends to get negative as the quantum and rate of taxes also rise along with inflation. This hypothesis also opines that initial researcher did not consider the tax impact when they were empirically testing the relationship between stock returns and inflation

Post-focus compression in Brahvi and Balochi

Previous research has shown that post-focus compression (PFC) - the reduction of pitch range and intensity after a focused word in an utterance, is a robust means of marking focus, but it is present only in some languages. The presence of PFC appears to follow language family lines. The present study is a further exploration of the distribution of PFC by investigating Brahvi, a Dravidian language, and Balochi, an Indo-Iranian language. Balochi is predicted to show PFC given its presence in other Iranian languages. Dravidian languages have not been studied for prosodic focus before and they are not related to any languages with PFC. We recorded twenty native speakers from each language producing declarative sentences in different focus conditions. Acoustic analyses showed that, in both languages, post-focus f 0 and other correlates were significantly reduced relative to baseline neutral-focus sentences, but post-focus lowering of f 0, and intensity was greater in magnitude in Balochi than in Brahvi. The Balochi results confirm our prediction, while the Brahvi results offer the first evidence of PFC in a Dravidian language. The finding of PFC in a Dravidian language is relevant to a postulated origin of PFC, which is related to the controversial Nostratic Macrofamily hypothesis

A DFT computational design and exploration of novel direct band gap silver-thallium double perovskites

Researchers have addressed the non-traditional power generation schemes as alternatives to the traditional fossil-fuel methods enormously since the scientific community has serious concerns about shortages of energy on our planet for future generations. In this scenario, the innovative materials for photovoltaic and thermoelectric device applications are required by addressing current issues of instability and efficiency. Perovskites are very popular in this regard particularly having higher power conversion efficiency of 25.2% in the case of solar cells. In the current article, we investigated innovative small direct band gap double perovskites (elapsolite) Cs$_2$AgTlX$_6$ (X= Cl, Br) with a comprehensive discussion on structural, electronic, optical, and thermoelectric properties using a first-principles approach. The compounds under investigation are found stable, efficient, and economical with alluring optical and thermoelectric properties. The higher absorption peaks in the visible range, substantial optical conductivities (~10$^{16}$ sec$^{-1}$), and a lower percentage of reflection in the visible range make these compounds fascinating for solar cell applications. Whereas large values of Seebeck coefficients, electrical conductivities, the figure of merits (greater than unity), and small values of thermal conductivities suggest the applications of these compounds in thermoelectric generators.Comment: 24 pages, 8 figure

Phytochemical and biological screening of Berberis aristata

Background: Berberis aristata occupies significant position as a medicinal plant. Given its clinical applications and the grave concern of weed based crop damage in Pakistan, the plant was investigated for its antimicrobial and allelopathic activities.Methods: Fresh Berberis aristata plant was obtained from Rawalakot and Hajeera (District Poonch) Azad Kashmir. Methanolic extract preparation and phytochemical analysis was done using standard procedures. Antibacterial and antifungal activities of the root, stem and leaf extracts of the plant were assayed against the bacterial strains E. coli, S. typhi, S. aureus, Shigella, Citrobacter, P. vulgaris, Enterobacter, S. pyrogenes, V. cholera and Klebsiella spp. and fungal strains A. niger,Cladosporium, Rhizoctonia, Alternaria, Trichoderma, Penicillium, Curvularia, Paecilomyces andRhizopus using disc diffusion method. Also, the phytoxicity of the extracts was evaluated againstLemna minor and the data was recorded after seven days.Results: Phytochemical screening of the three extracts identified the presence of alkaloids, reducing sugars, steroids, flavonoids, terpenoids, glycosides and saponins while tannins were found to be absent. The leaf extract also showed negative tests for alkaloids and steroids. The extracts significantly inhibited the growth of the employed microbial isolates. The leaf extract, however, was not active against A. niger, Curvularia, Paecilomyces and Rhizopus. For most of the tested strains, the effectiveness of the extracts was much higher than that of Amoxicillin and Fluconazole; the positive controls used for bacterial and fungal cultures, respectively. All the extracts demonstrated 100% phytotoxicity against Lemna minor at 1000 μg/mL while low activity (10-20%) was observed at 10 μg/mL and 100 μg/mL, respectively.Conclusion: The results strongly support the profound ethnobotanical applications of this plant and also demonstrate its potential for use in weed control strategies

Characterization of sorghum germplasm for various morphological and fodder yield parameters

This study was performed to evaluate and characterize 24 sorghum accessions for various morphological and fodder yield parameters. The germplasm displayed considerable variability for leaf color, midrib color, panicle shape, days to 50% flowering, leaf area, flag leaf area, plant height and green fodder yield, while differences of smaller magnitude were observed for number of leaves and tillers plant-1. Genotype Fsd -sorghum was mature early with minimum days to maturity (63) while maximum plant height (232 cm) was observed for Acc.1692. Moreover, Acc.1827 exhibited maximum leaf area (447 cm2) and the highest green fodder yield at 50% maturity (58 t ha-1) was recorded for Acc. 1763. The results of this study indicate that significant genetic diversity exists among the sorghum accessions. The genetic potential of Fsd-sorghum, accessions 1692, 1827 and 1763 can be exploited in future sorghum breeding programs. Further, these genotypes are recommended for commercial cultivation to meet the fodder needs of the country.Keywords: Fodder, Sorghum bicolor, accession

Patterns of coronary artery vessel disease on diagnostic angiography in a south asian population

Objective: The objective was to establish patterns of diseased vessels amongst the study population.Methodology: This retrospective descriptive study analyzed the data of 396 patients who underwent diagnostic angiographies at a large tertiary care public hospital in Islamabad, from January-2018 till October 2018. All data was coded and recorded in SPSS and was quantitatively run to find percentages and tests of significance were done.Results: We found that single vessel disease was the most common at 31.6%, and that the left anterior descending was the most commonly involved vessel with the most significant coronary artery disease, 86.6% and 71.4% respectively. Conclusion: Hypertension and diabetes has a great burden on our South Asian population and contribution to the development of coronary artery disease. Severe disease present in one vessel should alert physicians to the possibility of multi-vessel involvement and multi vessel progression in the disease progress.  Delineation of the vessel involvement pattern in South Asians forms the basis for formulating local guidelines and strategies for tackling coronary artery disease.Keywords: Angiography, CAD, patterns

Analysis of Machine Learning Based Imputation of Missing Data

Data analysis and classification can be affected by the availability of missing data in datasets. To deal with missing data, either deletion-based or imputation-based methods are used that results in the reduction of data records or wrong predicted value imputed by means/median respectively. A significant improvement can be done if missing values are imputed more accurately with less computation cost. In this work, a flow for analysis of machine learning-based algorithms for missing data imputation is proposed. The K-nearest neighbors (KNN) and Sequential KNN (SKNN) algorithms are used to impute missing values in datasets using machine learning. Missing values handled using statistical deletion approach (List-wise Deletion) and ML-based imputation methods (KNN and SKNN) is then tested and compared using different ML classifiers (Support Vector Machine and Decision Tree) to evaluate effectiveness of imputed data. The used algorithms are compared in terms of accuracy, and results yielded that the ML-based imputation method (SKNN) outperforms LD-based approach and KNN method in terms of effectiveness of handling missing data in almost every dataset with both classification algorithms (SVM and DT)