Nowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A data-intensive scientific workflow is useful for modeling such process. Since the sequential execution of data-intensive scientific workflows may take much time, Scientific Workflow Management Systems (SWfMSs) should enable the parallel execution of data-intensive scientific workflows and exploit the resources distributed in different infrastructures such as grid and cloud. This paper provides a survey of data-intensive scientific workflow management in SWfMSs and their parallelization techniques. Based on a SWfMS functional architecture, we give a comparative analysis of the existing solutions. Finally, we identify research issues for improving the execution of data-intensive scientific workflows in a multisite cloud.

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A Survey of Data-Intensive Scientific Workflow Management

With recent technological progress in the field of electronics, sensors and communication systems, the production of small UAVs (Unmanned Air Vehicles) became possible, which can be used for several military, commercial and civilian applications However, the capability of a single and small UAV is inadequate Multiple-UAVs can make a system that is beyond the limitations of a single small UAV A Flying Ad hoc Networks (FANETs) is such kind of network that consists of a group of small UAVs connected in ad-hoc manner, which are integrated into a team to achieve high level goals Mobility, lack of central control, self-organizing and ad-hoc nature between the UAVs are the main features of FANETs, which could expand the connectivity and extend the communication range at infrastructure-less area On one hand, in case of catastrophic situations when ordinary communication infrastructure is not available, FANETs can be used to provide a rapidly deployable, flexible, self-configurable and relatively small operating expenses network; the other hand connecting multiple UAVs in ad-hoc network is a big challenge This level of coordination requires an appropriate communication architecture and routing protocols that can be set up on highly dynamic flying nodes in order to establish a reliable and robust communication The main contribution of this paper include the introduction of suitable communication architecture, and an overview of different routing protocols for FANETs The open research issues of existing routing protocols are also investigated in this paper

Flying ad-hoc networks (FANETs): A review of communication architectures, and routing protocols

In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4?% in the first scenario and 60.0, 38.9, 31.5 and 31.2?% in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment.

Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

The ever-increasing demand for flexible and portable communications has led to a rapid evolution in networking between unmanned aerial vehicles (UAVs) often referred to as flying ad-hoc networks (FANETs). However, due to the exclusive characteristics of UAVs such as high mobility, frequent topology change and 3D space movement, make routing a challenging task in FANETs. Due to these characteristics, designing new routing protocols for FANETs is quite difficult. In the literature study of FANETs, a variety of traditional ad-hoc networking protocols have been suggested and tested for FANETs to establish an efficient and robust communication among the UAVs. In this context, topology-based routing is considered the most significant approach for solving the routing issues in FANETs. Therefore, in this article we specifically focus on topology-based routing protocols with the aim of improving the efficiency of the network in terms of throughput, end-to-end delay, and network load. We present a brief review of the most important topology-based routing protocols in the context of FANETs. We provide them with their working features for exchanging information, along with the pros and cons of each protocol. Moreover, simulation analyses of some of the topology-based routing protocols are also evaluated in terms of end-to-end delay, throughput and network load the using optimized network engineering tools (OPNET) simulator. Furthermore, this work can be used as a source of reference for researchers and network engineers who seek literature that is relevant to routing in FANETs.

Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols

Over the the past decade, social networking services (SNS) have proliferated on the web. The nature of such sites makes identity deception easy, providing a fast means for creating and managing identities, and then connecting with and deceiving others. Fake users are those accounts specifically created for purposes such as stalking or abuse of another user, for slander, or for marketing. The current system for detecting deception depends on behavioral, non-behavioral and user-generated content (UGC) information gathered from users. Although these methods have high detection accuracy, they cannot be implemented in databases with massive volumes of data. To address this issue, this paper proposes an enhanced graph-based semi-supervised learning algorithm (EGSLA) to detect fake users from a large volume of Twitter data. The proposed method encompasses four modules: data collection, feature extraction, classification and decision making. Data collected from Twitter using Scrapy is utilized for the evaluation. The performance of the proposed algorithm is tested with existing game theory, k-nearest neighbor (KNN), support vector machine (SVM) and decision tree techniques. The results show that the proposed EGSLA algorithm achieves 90.3% accuracy in spotting fake users.

An enhanced graph-based semi-supervised learning algorithm to detect fake users on Twitter

Predicting and Identifying behavioral analysis in social media using big data analytics is exceptionally repetitive. Since data in motion are difficult to capture and process with existing innovation. In spite of the fact that there are numerous frameworks that have executed for user behavior analysis, it’s as yet an upcoming and unexplored market that has more prominent potential for better advancements. So with the help of Hadoop framework, a new approach of designing a communal framework is proposed and it’s used for predicting and identifying user’s behavioral analysis in a community. The proposed work can be applied in the community-based environment where the prediction and identification of user behavior analysis has to be made with the semantic web approach. In addition to that, a suitable model for the communally accountable software objects where these objects would observe online communal network information is designed. Further, these objects in online communal network, assess them from the viewpoint of communally accountable performance based on the relation modeling conceptions. The location of communally accountable mediators is grounded in diverse methods. Diverse illustrations are grabbed from the analysis with microblog assessments, community semantic web, higher relations for communal web and social network sourced big information assessments. In the proposed framework, the analysis is based on the assessment/observation of the communally accountable performance of the communal media big data and design of higher-level relations as the model for the above mentioned assessments.

Designing a Framework for Communal Software: Based on the Assessment Using Relation Modelling

The social media offers the firms the capability to assess the feelings towards the contexts and the actions related to them in the real time. Additionally, the initial demarche of the sentiment assessment is the pre-processing of information gathered from the social media. Several prevailing analysis is focused on dealing with the social media by mining fresh characteristics in relation to the sentiment. The objective is to offer the employment of Twitter in a number of designed topics which is the immense social networking sites where the Twitter information is constantly escalating at immense ratio every day which regards it as Big Data source. Followed by which the mechanism is Big Data scheme like infosphere big vision permits the processing of information which is initially gathered from the social networks by Apache Flume and hoarded in Hadoop storage. Moreover, the intention is to combine the perceptions of these assessment outcomes employing big sheets. The assessment based on these investigations approves the Big Data platform creates improved outcomes in terms of assessing the social media.

Envisioning Social Media Information for Big Data Using Big Vision Schemes in Wireless Environment

Health services research provides a multi-disciplinary area of scientific exploration in relation to financial systems, social factors, organizational processes, and health technologies. With the help of big data, the huge amount of data can well be stored and handled effectively for diagnosis and also proper treatment of diseases can be monitored with these emerging technologies. In recent years, Diabetes Mellitus is non-transmittable illnesses that are a matter of concern in most of the developing countries. This paper proposes a model of a statistical assessment, healthcare information system for Diabetes Analysis employing big data. The performance metric such as accuracy and F-measure for the proposed statistical assessment model is evaluated by Hadoop framework, the results are comparatively higher than existing methods.

Designing statistical assessment healthcare information system for diabetics analysis using big data

Fault Tolerance is a configuration that prevents a computer or network device from failing in the event of unexpected problem or error such as hardware failure, link failure, unauthorized access, variations in the configuration of different systems and system running out of memory or disk space. The integration of fault tolerance measures with scheduling gains much importance. Scientific workflows use distributed heterogeneous resources in cloud interface are often hard to program. This paper explains task replication technique and simulation of cloud computing systems.

S. Karthik

Papers

An enhanced graph-based semi-supervised learning algorithm to detect fake users on Twitter

Designing a Framework for Communal Software: Based on the Assessment Using Relation Modelling

Envisioning Social Media Information for Big Data Using Big Vision Schemes in Wireless Environment

Designing statistical assessment healthcare information system for diabetics analysis using big data

A fault tolerent approach in scientific workflow systems based on cloud computing