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

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

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

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

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

A Scheduler based on Resource Competition for Parameter Sweep Workflow

Grid workflow scheduling has been a prevalent field of research in order to allocate scientific workflow tasks to grid resources. To actuate these grid workflow scheduling algorithms, schedulers need to be developed for grid workflow management systems. A scheduler is a component that gathers information, such as estimated execution times and lists of available grid resources, as inputs for scheduling algorithms. Once a grid schedule is generated, the scheduler uses it to allocate grid resources to the tasks in the workflow. This is even more complicated for parameter sweep workflow scheduling. As parameter sweep workflows are repeatedly executed a number of times with different inputs, to schedule them in parallel, the scheduler must be able to handle multiple workflow instances and multiple scheduling iterations. In this paper, we present a scheduling algorithm for parameter sweep workflows and suggest an implementation of a scheduler for parameter sweep workflows based on the algorithms. We highlight the implementation issues encountered in our experience of scheduler development