International Journal of Computer and Applications

With the increasing growth of large data storage and computational demand, Green Cloud Computing is known to be a broad area and hot field for research. To capitalize various IT resources, Cloud computing has produced an ultimate and impressing way to virtualize servers and data centers and to make energy efficient. The IT resources consume huge amounts of power and energy, which in turn produces shortage in energy and change in global climate. Therefore, there is a need of Green cloud computing which can produce solutions that can not only make the IT resources energy efficient but also minimize the operational costs. To solve environment related issues in the field of IT, Green IT is named to be an important step. It includes a huge number of focus areas for instance to provide proper management of power, virtualization of servers, design of data centers, recycling methods, eco-labeling, environment sustainability design and energy efficient resources etc. In this review firstly, a brief discussion on Cloud and Green computing is given then various application areas of Green IT are discussed and reviewed further. Based on the comparative analysis on Green-IT areas several research issues related to concerned Green IT areas, Objectives of such areas etc. are elaborated further.

Green cloud computing: A review on Green IT areas for cloud computing environment

Thank you very much for reading introduction to privacy preserving data publishing concepts and techniques. Maybe you have knowledge that, people have search numerous times for their favorite books like this introduction to privacy preserving data publishing concepts and techniques, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some malicious bugs inside their desktop computer.

Introduction To Privacy Preserving Data Publishing Concepts And Techniques

Taxi GPS traces, which contain a great deal of valuable information as regards to human mobility and city traffic, can be extracted to improve the quality of our lives. Since the method of visualized analysis is believed to be an effective way to present information vividly, we develop our analysis and visualization method based on a city’s short-dated taxi GPS traces, which can provide recommendation to help cruising taxi drivers to find potential passengers with optimal routes. With our approach, hot spots for loading and unloading passenger(s) are extracted using an improved DBSCAN algorithm after data preprocessing including cleaning and filtering. Then, this paper describes the start-end point-based similar trajectory method to get coarse-level trajectories clusters, together with the density-based e distance trajectory clustering algorithm to identify recommended potential routes. A weighted tree is defined including such factors as driving time, velocity, distance and endpoint attractiveness for optimal route evaluation from vacant to occupied hot spots. An example is presented to show the effectiveness of our visualization method.

https://www.mdpi.com/2078-2489/6/2/134/pdf

Analysis and Visualization for Hot Spot Based Route Recommendation Using Short-Dated Taxi GPS Traces

Software-defined Networking (SDN) has emerged as the dominant programmable network architecture that separates the control plane from the data plane. SDN allows the network administrators and data centers to efficiently and flexibly manage their networking equipment using software that runs on external servers. SDN adopts OpenFLow protocol to communicate between network switch and controller. In this paper, we first establish a SDN-based cloud computing environment via open source OpenFLow switch and controller packages. Thereafter, we extend the functionality of OpenFLow controller to provide load balancing, power-saving, and monitoring mechanisms. To verify the feasibility of our architecture, we also develop a queueing model for SDN and calculate its analytical solution for the steady-state probabilities. The experimental results show that SDN-based environment can provide QoS guaranteed cloud computing services.

An SDN-based cloud computing architecture and its mathematical model

Privacy preserving data publication is the main concern in present days, because the data being published through internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals with the privacy preservation in context of big data using a data warehousing solution called hive. We implemented nearest similarity based clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity which deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with big data. This framework also supports the execution of existing algorithms without any changes. The model in the article outperforms than existing models.

/pdf/privacy-preserving-data-publishing-based-on-sensitivity-in-2w3bbj1wpc.pdf

Privacy preserving data publishing based on sensitivity in context of Big Data using Hive

In this paper we introduce an M/M/1 interdependent queueing model with controllable arrival rates. These models are much useful in analysing the practical situations arising at places like, data voice transmission, computer communication systems and neuro-physiological problems. The steady state solutions of the model are derived. The system characteristics are obtained and analysed. It is observed that the mean dependence rate between the arrival and service processes can reduce the congestion in queues and delays in transmission. This model also includes the earlier models as particular cases for specific or limiting values of the parameters.

The M/M/1 Interdependent Queueing Model with Controllable Arrival Rates

The massive increases in data have paved a path for distributed computing, which in turn can reduce the data processing time. Though there are various approaches in distributed computing, Hadoop is one of the most efficient among the existing ones. Hadoop consists of different elements out of which Map Reduce is a scalable tool that enables to process a huge data in parallel. We proposed a Novel and Efficient User Profile Characterization under distributed environment. In this frame work the network anomalies are detected by using Hadoop Map Reduce technique. The experimental results clearly show that the proposed technique shows better performance.

/pdf/a-novel-and-efficient-method-for-protecting-internet-usage-57rydo43cu.pdf

A Novel and Efficient Method for Protecting Internet Usage from Unauthorized Access Using Map Reduce

Information retrieval is fast becoming the prevailing form of information access, surpassing traditional database style searching. Ontologies have become the tool of choice employed in many information retrieval systems and more prominently in semantic information retrieval. In order to overcome the disadvantages in key word based information retrieval systems, which transfer irrelevant information, ontology has been designed. A system with ontology mimics the real world, where every task is laced with certain meaning as this is basic idea behind knowledge processing. Hadoop, which is an open source frame work for storing and processing large datasets, is used for preprocessing the text documents. First, a set of text documents are considered. Preprocessing is performed on a large domain of data using Hadoop MapReduce. This includes the removal of the stop words along with stemming and excluding less frequency words. Despite this pre-processing, owing to the colossal number of index terms still floating in the considered domain data, the problem of high dimensionality is encountered. Therefore the dimensionality of such a group of terms is reduced by identifying it as a concept and those concepts can be viewed as a single dimension in a ontology based information retrieval system. Now ontology is constructed by assigning synonym set to each concept in this structure using tools like word net. Thus constructed ontology can be mapped on to the processed query which gives us the relevant information from the data pool considered.

http://www.sersc.org/journals/IJDTA/vol8_no6/5.pdf

An Efficient Semantic Ranked Keyword Search of Big Data Using Map Reduce

Data publishing plays a major role to establish a path between current world scenarios and next generation requirements and it is desirable to keep the individuals privacy on the released content without reducing the utility rate. Existing KC and KCi models concentrate on multiple categorical sensitive attributes. Both these models have their own merits and demerits. This paper proposes a new method named as novel KCi slice model, to enhance the existing KCi approach with better utility levels and required privacy levels. The proposed model uses two rounds to publish the data. Anatomization approach is used to separate the sensitive attributes and quasi attributes. The first round uses a novel approach called as enhanced semantic l-diversity technique to bucketize the tuples and also determine the correlation of the sensitive attributes to build different sensitive tables. The second round generates multiple quasi tables by performing slicing operation on concatenated correlated quasi attributes. It concatenate the attributes of the quasi tables with the ID's of the buckets from the different sensitive tables and perform random permutations on the buckets of quasi tables. Proposed model publishes the data with more privacy and high utility levels when compared to the existing models.

/pdf/a-novel-dynamic-kci-slice-publishing-prototype-for-retaining-1gkuwd5r5s.pdf

P. Srinivasa Rao

Papers

Privacy preserving data publishing based on sensitivity in context of Big Data using Hive

The M/M/1 Interdependent Queueing Model with Controllable Arrival Rates

A Novel and Efficient Method for Protecting Internet Usage from Unauthorized Access Using Map Reduce

An Efficient Semantic Ranked Keyword Search of Big Data Using Map Reduce

A Novel Dynamic KCi - Slice Publishing Prototype for Retaining Privacy and Utility of Multiple Sensitive Attributes