Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications

An Architecture for Simplified and Automated Machine Learning

learning has been adopted by businesses to analyze their vast data in order to make strategic decision. However, knowledge in machine learning and technical skill are usually required to prepare data and perform machine learning tasks. This obstacle prevents smaller businesses with no technical knowledge to utilize machine learning. In this paper, we propose an architecture for simplified and automated machine learning process currently supporting the data classification task. The architecture includes a method for characterizing datasets, which allows for simplifying and automating machine learning model and hyperparameter selection based on historical execution configurations. Users can simply upload their datasets via a web browser, and the system will determine the possible models and their hyperparameter configurations for the users to choose from. The prototype shows the feasibility of the proposed architecture. Although the accuracy is still limited by the small execution history and the cleanliness of the input datasets, the architecture can minimize user involvement in the machine learning process so that non-technical users can perform data classification through a web browser without installing any software

Finding the Optimal Value for Threshold Cryptography on Cloud Computing

The objective of using threshold cryptography on cloud environment is to protect the keys, which are the most important elements in cryptographic systems. Threshold cryptography works by dividing the private key to a number of shares, according to the number of virtual machines, then distributing them each share to each virtual machine. In order to generate the key back, not all the shares are needed. Howerver, the problem is that there has been no research attemping to find a suitable threshold value for key reconstruction. Therefore, this paper presented a guildline designed and implemented that can assist to choose such value. The experiment was setup using CloudSim to simulate cloud environment and collecting time taken in key distribution and key reconstruction process to achieve the optimal threshold value

Comparison of Second-Trimester Uterine Artery Doppler Indices between Pregnant Women with and without Prior Cesarean Delivery and Effect on Perinatal Outcomes

Objective: To evaluate the effects of prior cesarean delivery on second-trimester Uterine Artery (UtA) Doppler and its effect on perinatal outcomes. Materials and Methods: This study was conducted at the Maternal Fetal Medicine unit of Bhumibol Adulyadej Hospital, Thailand between June 2023 and January 2024. Healthy pregnant women aged between 18 and 45 years old were recruited and divided into prior (PCD) and non-prior cesarean delivery (non-PCD) groups. Both groups underwent UtA Doppler study from 18 to 24 weeks of gestation. UtA Doppler indices consisted of pulsatility index (UtA-PI), resistance index (UtA-RI) and systolic/diastolic ratio (S/D). Demographics, maternal and neonatal outcomes were collected. Results: Total of 416 participants were recruited and divided equally. The mean age of participants was 30.5 years old. Diabetes mellitus in pregnancy and hypertensive disorder were reported as 18.9 (79/416) and 5.5 (23/416) percent, respectively. From univariate analysis, subjects in the PCD group had higher UtA-PI, UtA-RI and UtA S/D than the non-PCD group with statistical significance. After multiple logistic regressions, only UtA-RI was greater in the PCD group with any statistical significance. The mean gestational age at delivery was 38.41 weeks. Prevalence of small for gestational age deliveries, preeclampsia, and preterm birth were 4.4 (17/376), 3.1 (12/376) and 10 percent (38/376), respectively. Maternal and neonatal outcomes of both groups were comparable. Number of cesarean deliveries, parity, miscarriage, and anterior placentation were not associated with UtA Doppler indices. Conclusion: UtA-RI of prior cesarean delivery participants were higher than non-prior cesarean delivery participants and not associated with maternal and neonatal adverse outcomes

Prevalence of Gestational Diabetes Mellitus and Pregnancy Outcomes in Women with Risk Factors Diagnosed by IADPSG Criteria at Bhumibol Adulyadej Hospital

Objective:To determine the prevalence and clinical outcomes of gestational diabetes mellitus (GDM), defined by IADPSG criteria, in pregnant women who are at risk of GDM.Study design: Descriptive study.Material and Method: We studied pregnant women who visited the antenatal clinics at Bhumibol Adulyadej Hospital between July 1, 2011 and December 31, 2012 and had risk factors of GDM. The diagnosis of GDM was defined using the IADPSG criteria. Primary outcome was the prevalence of GDM and the secondary outcomes were pregnancy related complications which included maternal and neonatal complications.Results: A total of 6,324 pregnancy women, 164 patients were diagnosed GDM. The prevalence of GDM was 2.6%. The most common clinical risk factor for GDM was age ≥30 years (75.4%). The most common maternal and neonatal complication were pregnancy induced hypertension (PIH) (12.7%) and hypoglycemia (47.6%). GDM women were significantly different from non-GDM women in PIH, primary cesarean section, hypoglycemia, Apgar <7, and NICU admission. Pregnancy outcomes between GDM A1 and A2 were significantly different. GDM A2 increased the rate of cesarean section, hypoglycemia, and NICU admission. Conclusion: Using the IADSP criteria, the prevalence of GDM was 2.6%. Compared to non-GDM regnant women, adversed pregnancy outcomes were significantly higher in GDM pregnant wome

Effects of N-Acetylcysteine Supplementation on Semen Analysis, Hormonal Profile and Spontaneous Pregnancy Rate in Idiopathic Infertile Men: Before and After Clinical Trial

Objective: To compare sperm quality and quantity, hormonal profiles and spontaneous pregnancy rates before and after administering a 3 months course of N-Acetylcysteine (NAC) Material and Methods: This prospective clinical trial was conducted at the Infertility Unit of the Obstetrics and Gynecology Department at Bhumibol Adulyadej Hospital, Thailand. The study period was from June 1, 2023 to September 30, 2023. Subjects were idiopathic infertile males aged between 20 and 50 years old. All subjects received 600 mg of NAC orally per day. Semen analysis (SA) and male hormonal profiles (MHP; testosterone, LH, FSH and prolactin) were performed before and three months after NAC administration. Demographic, clinical characters and laboratory change were recorded. Results: The 92 participants were recruited. The mean age of couples was 34.5 years old. The average duration of infertility was 3.6 years. Increase of semen volume (2.1 vs 2.4 ml, p < 0.001), semen concentration (30.5 vs 43.1 x 106/mL, p < 0.001), total motility (59.57 vs 72.38 %, p < 0.001), progressive motility (59 vs 69.8 %, p < 0.001), normozoospermia (60 vs 83 %, p < 0.001), testosterone (452.8 vs 479.0 ng/dL, p = 0.038), LH (4.6 vs 5.3 mIU/mL, p = 0.004) and FSH (4.4 vs 4.6 mIU/mL, p = 0.009) were observed after three months of NAC administration. No changes in sperm morphology and prolactin level. One-third (27/92) of each participant’s spouse conceived spontaneously. Conclusion: NAC potentially enhances male hormonal profiles, sperm quality and quantity with an impressive spontaneous pregnancy rate

Prevalence of Abnormal Cerebroplacental Ratio in Uncomplicated Full-term Pregnancy and Correlation with Adverse Perinatal Outcomes

Objective: To investigate the prevalence of abnormal cerebroplacental ratio (CPR) and predicting values for adverse perinatal outcomes in uncomplicated full-term pregnancies. Materials and Methods: This prospective cross-sectional study was conducted at Bhumibol Adulyadej Hospital, Royal Thai Air Force, Thailand between July and December 2023. The study population comprised pregnant women between the ages 18 and 45 presenting uncomplicated full-term pregnancies. Transabdominal ultrasonography in Doppler color mode was performed on all participants. Umbilical artery pulsatility index (UAPI) and middle cerebral artery pulsatility index (MCAPI) were both measured. CPR was calculated by MCAPI divided by UAPI. A CPR value was considered low if it was less than 1.03. Obstetric and perinatal outcomes were recorded including route of delivery, gestational age (GA) at delivery, obstetric complications, Apgar score, neonatal birth weight, neonatal intensive care unit (NICU) admission, and fetal non-reassuring tracing (FNR). Results: A total of 250 pregnant women were recruited. The mean maternal age and GA was 27.7 years, 39.6 weeks, respectively. Low CPR prevalence was recorded at 16.4 percent. There were 41 and 209 cases in low (<1.03) and normal (≥1.03) CPR groups, respectively. UAPI and MCAPI of the normal/low CPR group were 0.8/1.3 and 1.1/0.9 with statistical significance. CPR for predicted value of FNR (1.03) gave sensitivity, specificity, PPV, and NPV at 95.5, 91.2, 51.2 and 99.5 percent, respectively. This study presented no adverse perinatal outcomes. Conclusion: Low CPR prevalence was 16.4 percent. Normal CPR values measured within a week before birth was a good indicator of normal perinatal outcomes.

Scheduling parameter sweep workflow in the grid

Workflow technology has been adopted in scientific domains to orchestrate and automate scientific processes in order to facilitate experimentation. Such scientific workflows often involve large data sets and intensive computation that necessitate the use of the Grid, which offers supercomputing power through shared distributed resources. To execute a scientific workflow in the Grid, tasks within the workflow that represent steps in the scientific process are assigned to Grid resources for execution. To ensure efficient execution of the workflow, Grid workflow scheduling is required to manage the allocation of Grid resources. Although many Grid workflow scheduling techniques exist, they are mainly designed for the execution of a single workflow. This is not the case with parameter sweep workflows which focus on parametric study and parameter optimisation. A parameter sweep workflow is executed numerous times with different input parameters in order to determine the effect of each parameter combination on the experiment. While executing multiple instances of a parameter sweep workflow in parallel can reduce the time required for the overall execution, this execution introduces new challenges to Grid workflow scheduling. Not only is a scheduling algorithm that is able to manage multiple workflow instances required, but this algorithm also needs the ability to schedule tasks across multiple workflow instances judiciously, as tasks may require the same set of Grid resources. Without appropriate resource allocation, resource competition problem could arise. In the thesis, we propose a new Grid workflow scheduling technique for parameter sweep workflow called the Besom scheduling algorithm. The scheduling decision of our algorithm is based on the resource dependencies of tasks in the workflow, as well as conventional Grid resource-performance metrics. In addition, the proposed technique is extended to handle loop structures in scientific workflows without using existing loop-unrolling techniques. We evaluate the Besom algorithm under a variety of conditions. A comparison between the simulation results of the Besom algorithm and of the three existing Grid workflow scheduling algorithms shows that the Besom algorithm is able to perform better than the existing algorithms for workflows that have complex structures and that involve overlapping resource dependencies of tasks. The Besom scheduling algorithm advances the ability to schedule parallel execution of parameter sweep workflows in the Grid. The outcomes of this thesis justify continuing research in this area to increase our understanding of scheduling multiple Grid workflow instances and to provide support to those involved in parametric study and scientific workflow management