Enhancement of Map Function Image Processing System using DHRF Algorithm on Big Data in the Private Cloud Tool

Cloud computing is the concept of distributing a work and also processing the same work over the internet. Cloud computing is called as service on demand. It is always available on the internet in Pay and Use mode. Processing of the Big Data takes more time to compute MRI and DICOM data. The processing of hard tasks like this can be solved by using the concept of MapReduce. MapReduce function is a concept of Map and Reduce functions. Map is the process of splitting or dividing data. Reduce function is the process of integrating the output of the Map2019;s input to produce the result. The Map function does two various image processing techniques to process the input data. Java Advanced Imaging (JAI) is introduced in the map function in this proposed work. The processed intermediate data of the Map function is sent to the Reduce function for the further process. The Dynamic Handover Reduce Function (DHRF) algorithm is introduced in the reduce function in this work. This algorithm is implemented in the Reduce function to reduce the waiting time while processing the intermediate data. The DHRF algorithm gives the final output by processing the Reduce function. The enhanced MapReduce concept and proposed optimized algorithm is made to work on Euca2ool (a Cloud tool) to produce an effective and better output when compared with the previous work in the field of Cloud Computing and Big Data

Efficient handling of Big Data Analytics in Densely Distributed Sensor Networks

Abstract The elaboration of wireless sensor networks has reached a point where each specific node of a network may store and convey a massive amount of (sensor-based information at once or terminated time). Hence in the forthcoming future, densely linked, enormously dynamic distributed sensor networks such as vehicle-2-vehicle communication setups may hold even greater knowledge potency. This is often due to the increase in node complexity. Subsequently, data volumes will become a problem for traditional data aggregation strategies traffic-wise as well as with regard to energy efficiency. For that reason, in this paper we suggest to call such scenarios as big data scenarios, they pose similar questions and problems as traditional big data concepts and granting the major focus mostly on business intelligence difficulties. Consequently our scheme would be propose an aggregation strategy tied to technological prerequisites which enable the efficient use of energy and the handling of large data volumes in an open source Hadoop frameworks with single/multi clustered architectures. Together with, we demonstrate the energy conservation potential based on experiments with actual sensor platforms in a distributed context

An Architecture of Thin Client in Internet of Things and Efficient Resource Allocation in Cloud for Data Distribution

These days, Thin-client devices are continuously accessing the Internet to perform/receive diversity of services in the cloud. However these devices might either has lack in their capacity (e.g., processing, CPU, memory, storage, battery, resource allocation, etc) or in their network resources which is not sufficient to meet users satisfaction in using Thin-client services. Furthermore, transferring big size of Big Data over the network to centralized server might burden the network, cause poor quality of services, cause long respond delay, and inefficient use of network resources. To solve this issue, Thinclient devices such as smart mobile device should be connected to edge computing which is a localized near to user location and more powerful to perform computing or network resources. In this paper, we introduce a new method that constructs its architecture on Thin-client -edge computing collaboration. Furthermore, present our new strategy for optimizing big data distribution in cloud computing. Moreover, we propose algorithm to allocate resources to meet Service Level Agreement (SLA) and Quality of Service (QoS) requirements. Our simulation result shows that our proposed approach can improve resource allocation efficiently and shows better performance than other existing methods

Large Scale Data Analytics with Language Integrated Query

Databases can easily reach petabytes (1,048,576 gigabytes) in scale. A system to enable users to efficiently retrieve or query data from multiple databases simultaneously is needed. This research introduces a new, cloud-based query framework, designed and built using Language Integrated Query, to query existing data sources without the need to integrate or restructure existing databases. Protein data obtained through the query framework proves its feasibility and cost effectiveness