Harshadkumar B. Prajapati

Scheduling in Grid Computing Environment

Scheduling in Grid computing has been active area of research since its beginning. However, beginners find very difficult to understand related concepts due to a large learning curve of Grid computing. Thus, there is a need of concise understanding of scheduling in Grid computing area. This paper strives to present concise understanding of scheduling and related understanding of Grid computing system. The paper describes overall picture of Grid computing and discusses important sub-systems that enable Grid computing possible. Moreover, the paper also discusses concepts of resource scheduling and application scheduling and also presents classification of scheduling algorithms. Furthermore, the paper also presents methodology used for evaluating scheduling algorithms including both real system and simulation based approaches. The presented work on scheduling in Grid containing concise understandings of scheduling system, scheduling algorithm, and scheduling methodology would be very useful to users and researchers.

Classification of ECG signals using machine learning techniques: A survey

Classification of electrocardiogram (ECG) signals plays an important role in diagnoses of heart diseases. An accurate ECG classification is a challenging problem. This paper presents a survey of ECG classification into arrhythmia types. Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. Different classifiers are available for ECG classification. Amongst all classifiers, artificial neural networks (ANNs) have become very popular and most widely used for ECG classification. This paper discusses the issues involved in ECG classification and presents a detailed survey of preprocessing techniques, ECG databases, feature extraction techniques, ANN based classifiers, and performance measures to address the mentioned issues. Furthermore, for each surveyed paper, our paper also presents detailed analysis of input beat selection and output of the classifiers.

Survey of multi objective evolutionary algorithms

Multi-objective optimization aims at simultaneously optimizing two or more objectives of a problem. Multi-objective evolutionary algorithms (MOEAs) are widely accepted and useful for solving real world multi-objective problems. When we have two or more conflicting objectives of a problem then we can apply MOEA. MOEA generates a set of non-dominated solutions at the end of run, which is called Pareto set. The Pareto front contains set of Pareto solutions. Any MOEA aims to improve (i) convergence of population towards true Pareto front and (ii) diversity of solutions belonging to Pareto set. Generally, an external archive is used by MOEAs to maintain a set of non-dominated Pareto set solutions. Sometimes, Pareto set contains more number of solutions than the size of archive. This paper presents survey of various methods used by different MOEAs for reducing the size of Pareto set while maintaining solutions diversity. It presents comparison of these methods along with their advantages and disadvantages. The paper concludes by giving limitation of crowding distance based method in various scenarios.

A survey on semantic document clustering

Clustering is the process of partitioning a set of data objects into subsets. It is commonly used technique in data mining, information retrieval, and knowledge discovery for finding hidden patterns or objects from a data of different category. Text clustering process deals with grouping of an unstructured collection of documents into semantically related groups. A document is considered as a bag of words in traditional document clustering methods; however, semantic meaning of word is not considered. Thus, more informative features like concept weight are important to achieve accurate document clustering and this can be achieved through semantic document clustering because it takes meaningful relationship into account. This paper highlights major challenges in traditional document clustering and semantic document clustering along with brief discussion. This paper identifies five major areas under semantic clustering and presents a survey of 17 papers that has studied, covering major significant works. Moreover, this paper also provides a survey of tools, ontology databases, and algorithms, which help in applying and evaluating document clustering. The presented survey is used in preparing the proposed work in the same direction. This proposed work uses the concept weight for text clustering system which is to be developed based on a Hierarchical Agglomerative Clustering, Bisecting k-means algorithm, and Self Organized Map Neural Network in accordance with the principles of WordNet ontology as a background knowledge.

A survey on job scheduling algorithms in Big data processing

Scheduling problem has been an active area of research in computing systems since their inception. The Apache Hadoop framework has emerged as most widely adopted framework for distributed data processing because of open source and allowing use of commodity hardware. Job scheduling has become an important factor to achieve high performance in Hadoop cluster. Several scheduling algorithms have been developed for Hadoop-MapReduce model which vary widely in design and behavior, handling different issues such as locality of data, user share fairness and resource awareness. This paper highlights fundamental issues in job scheduling, presents classification of Hadoop schedulers, and discusses presented survey of existing scheduling algorithm. Moreover paper also discusses features, advantages, and limitations of the scheduling algorithms. This paper also discusses about how various resource monitoring tools or frameworks help in achieving better result from MapReduce. It also discusses customized MapReduce frameworks used for improving the performance. This paper would be useful to beginners and researchers for understanding the state-of-the-art on scheduling in Big data processing.

Rainfall forecasting using neural network: A survey

An accurate rainfall forecasting is very important for agriculture dependent countries like India. For analyzing the crop productivity, use of water resources and pre-planning of water resources, rainfall prediction is important. Statistical techniques for rainfall forecasting cannot perform well for long-term rainfall forecasting due to the dynamic nature of climate phenomena. Artificial Neural Networks (ANNs) have become very popular, and prediction using ANN is one of the most widely used techniques for rainfall forecasting. This paper provides a detailed survey and comparison of different neural network architectures used by researchers for rainfall forecasting. The paper also discusses the issues while applying different neural networks for yearly/monthly/daily rainfall forecasting. Moreover, the paper also presents different accuracy measures used by researchers for evaluating performance of ANN.

A survey on detection and classification of rice plant diseases

Identifying disease from the images of the plant is one of the interesting research areas in computer and agriculture field. This paper presents a survey of different image processing and machine-learning techniques used in the identification of rice plant diseases based on images of disease infected rice plants. This paper presents not only survey of various techniques but also concisely discusses important concepts of image processing and machine learning applied to plant disease detection and classification. We carry out detailed study of 19 papers, covering the work on rice plant diseases and other different plants and fruits, and present a survey of these papers based on important criteria. These criteria include size of image dataset, no. of classes(diseases), preprocessing, segmentation techniques, types of classifiers, accuracy of classifiers etc. We utilize our survey and study to propose and design our work on detection and classification of rice plant diseases.

Load balancing using process migration for linux based distributed system

This paper presents implementation of load balancing mechanism using master-slave model and Berkeley Lab Checkpoint/Restart (BLCR) toolkit. The overall goal is to create a Master-Slave model through which we can migrate processes from highly loaded nodes to some dedicated lightly loaded nodes. The agent running on master node divides total work into equal sub tasks and delegates these sub-tasks to several independent slave nodes. The master node computes final result by aggregating the partial results returned by slaves.

A survey on detection and classification of cotton leaf diseases

Cotton is the most important cash crop in India. It is also known as “White Gold” or “The King of fibers” among all cash crops in the country. About 80-90% of the diseases which occur on the leaves of cotton are Alternaria leaf spot, Cercospora leaf spot, Bacterial blight and Red spot. This paper presents a survey on detection and classification of cotton leaf diseases. It is difficult for human eyes to identify the exact type of leaf disease which occurs on the leaf of plant. Thus, in order to identify the cotton leaf diseases accurately, the use of image processing and machine learning techniques can be helpful. The images used for this work were acquired from the cotton field using digital camera. In pre-processing step, background removal technique is applied on the image in order to remove background from the image. Then, the background removed images are further processed for image segmentation using otsu thresholding technique. Different segmented images will be used for extracting the features such as color, shape and texture from the images. At last, these extracted features will be used as inputs of classifier.

Analysis Perspective Views of Grid Simulation Tools

Due to the high complexity of Grid computing systems, experimentation on a real Grid system is challenging and time consuming. Moreover, deploying a Grid system demands a lot of efforts, money, and skills. Therefore, a simulation based approach of experimentation and research is adopted by many researchers. Many simulation tools are available supporting diverse research studies in the Grid computing area. However, researchers need to choose the most appropriate one for the intended study, and taking that decision requires that the researchers understand all relevant details pertaining to the Grid simulation tools. Therefore, to guide a researcher in choosing a particular Grid simulation tool, we pose important questions that the researcher needs to consider. To answer the posed questions, we provide analysis perspective views of 12 important Grid simulation tools with emphasis on different users. Furthermore, we share our experience of working with SimGrid and GridSim. Our results with 31 comparison criteria on the selected Grid simulation tools would become very useful to users to get insights on the tools. Furthermore, we expect that the presented work will guide the authors of prospective simulation tools.