Satyabrata Das

An Efficient Intra-Server and Inter-Server Load Balancing Algorithm for Internet Distributed Systems

The growing popularity of Internet Distributed System has drawn enormous attention in business and research communities for handling large number of client requests. These requests are managed by a set of servers. However, the requests may not be equally distributed due to their random nature of arrivals. The optimal assignment of the requests to the servers is a well-known NP-hard problem. Therefore, many algorithms have been proposed to address this problem. However, these algorithms suffer from an excessive number of comparisons. In this paper, a Swapping-based Intraand interServer (SIS) load balancing with padding algorithm is proposed for its solution. The algorithm undergoes a three-phase process to balance the loads among the servers. The proposed algorithm is compared with a client-server load balancing algorithm and the performance is measured in terms of the number of load comparisons and load factor. The simulation outcomes show the efficacy of the proposed algorithm. KEywoRDS Client, Internet Distributed System, Load Balancing, Load Factor, Padding, Server, Swapping

Detection of Routing Misbehavior in MANETs with 2ACK scheme

The Routing misbehavior in MANETs (Mobile Ad Hoc Networks) is considered in this paper. Commonly routing protocols for MANETs [1] are designed based on the assumption that all participating nodes are fully cooperative. Routing protocols for MANETs are based on the assumption which are, all participating nodes are fully cooperative. Node misbehaviors may take place, due to the open structure and scarcely available battery-based energy. One such routing misbehavior is that some nodes will take part in the route discovery and maintenance processes but refuse to forward data packets. In this, we propose the 2ACK [2] scheme that serves as an add-on technique for routing schemes to detect routing misbehavior and to mitigate their effect. The basic idea of the 2ACK scheme is to send two-hop acknowledgment packets in the opposite direction of the routing path. To reduce extra routing overhead, only a few of the received data packets are acknowledged in the 2ACK scheme.

A Customer-Oriented Task Scheduling for Heterogeneous Multi-Cloud Environment

Task scheduling is widely studied in various environments such as cluster, grid and cloud computing systems. Moreover, it is NP-Complete as the optimization criteria is to minimize the overall processing time of all the tasks i.e., makespan. However, minimization of makespan does not equate to customer satisfaction. In this paper, the authors propose a customer-oriented task scheduling algorithm for heterogeneous multi-cloud environment. The basic idea of this algorithm is to assign a suitable task for each cloud which takes minimum execution time. Then it balances the makespan by inserting as much as tasks into the idle slots of each cloud. As a result, the customers will get better services in minimum time. They simulate the proposed algorithm in a virtualized environment and compare the simulation results with a well-known algorithm, called cloud min-min scheduling. The results show the superiority of the proposed algorithm in terms of customer satisfaction and surplus customer expectation. The authors validate the results using two statistical techniques, namely T-test and ANOVA.

A Seamless Vertical Handoff Algorithm in 4G Networks

The rapid improvement of the mobile generations was for the purpose of supporting as many mobile devices as possible that could benefit the users at anytime and anywhere in terms of common practical applications such as internet access, video-on-demand, video conferencing system and many more applications. The emergence of 4G wireless network technologies is intended to complement and replace the current generations. The keys features of 4G technologies include accessing information anywhere, anytime, with a seamless connection to a wide range of information and services, and receiving a large volume of information, data, pictures, video, and so on. Based on the developing trends of mobile communication, 4G will have broader bandwidth, higher data rate, and smoother and quicker handoff to provide seamless service across a multitude of wireless systems and networks. One of the major issues of seamless mobility is handoff management. To achieve seamless handoff between different wireless technologies, known as vertical handoff (VHO), it is a major challenge to design intelligent handoff management schemes for 4G-systems. In this paper we have presented the design of an adaptive multi-attribute vertical handoff decision algorithm based on genetic algorithm which is both cost effective and useful.

Clustering and classifying informative attributes using rough set theory

Clustering techniques are the unsupervised data mining applications and are important in data mining methods for exploring natural structure and identifying interesting patterns in original data, also it is proved to be helpful in finding coexpressed samples. In cluster analysis, generally the given dataset is partitioned into groups based on the given features such that the data objects in the same group are more similar to each other than the data objects in other groups. The objects are clustered or grouped based on the principle of maximizing intra-class similarity and minimizing interclass similarity. In this paper, the rough set theory (RST) has been used for attribute clustering. RST is a theory adopted to deal with rough and unsure knowledge, which analyzes the clusters and finds the data principles when previous knowledge is not available, providing a new method for data classification. With the continuous change in data objects we have to improve these relevant technologies over time, and we have to propose creative theory in response, meeting the demands of application, though there are many rough set methods. In this paper; after implementing the rough set based attribute clustering method on real life leukemia dataset, we classify them using some of the traditional classification techniques such as Multilayered Perceptron (MLP) based classifier, Naïve Bayesian (NB) classifier and Support Vector Machine (SVM). At the end, the same classification techniques are applied to classify the original leukemia dataset before application of rough set based attribute clustering. Finally the paper provides a comparative analysis among the traditional classifiers and the proposed corresponding rough set based classifiers. Among all, the proposed MLP classifier is found to be the better classifier than the others giving higher classification accuracy and it is proved to be efficient having lower error ratio.

Feature reduction using principal component analysis for agricultural data set

Many applications like video surveillance, telecommunication, weather forecasting and sensor networks uses high volume of data of different types. The effective and efficient analysis of data in such different forms becomes a challenging task. Analysis of such large expression data gives rise to a number of new computational challenges not only due to the increase in number of data objects but also due to the increase in number of attributes. Hence, to improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by an efficient dimensionality reduction method. In this paper, we have proposed to use the method of k-means clustering and principal component analysis (PCA) approach for attribute reduction, which initially apply PCA to obtain reduced uncorrelated attributes specifying maximal eigenvalues in the dataset with minimum loss of information. Then again we proposed to use k-means on the PCA reduced dataset to discover discriminative features that will be the most adequate ones for classification. This is a combination of clustering approach with feature reduction to obtain a minimal set attributes retaining a suitably high accuracy in representing the original features. We have used the Greengram agricultural data set. Finally, we found that the result of clustering is same after reducing the attributes using PCA.

Randomized Algorithmic Approach for Biclustering of Gene Expression Data

Microarray data processing revolves around the pivotal issue of locating genes altering their expression in response to pathogens, other organisms or other multiple environmental conditions resulted out of a comparison between infected and uninfected cells or tissues. To have a comprehensive analysis of the corollaries of certain treatments, deseases and developmental stages embodied as a data matrix on gene expression data is possible through simultaneous observation and monitoring of the expression levels of multiple genes. Clustering is the mechanism of grouping genes into clusters based on different parameters. Clustering is the process of grouping genes into clusters either considering row at a time(row clustering) or considering column at a time(column clustering). The application of clustering approach is crippled by conditions which are unrelated to genes. To get better of these problems a unique form of clustering technique has evolved which offers simultaneous clustering (both rows and columns) which is known as biclustering. A bicluster is deemed to be a sub matrix consisting data values. A bicluster is resulted out of the removal of some of the rows as well as some of the columns of given data matrix in such a fashion that each row of what is left reads the same string. A fast, simple and efficient randomized algorithm is explored in this paper, which discovers the largest bicluster by random projections.

Survey of Energy-Efficient Techniques for the Cloud-Integrated Sensor Network

The sensor cloud is a combination of cloud computing with a wireless sensor network (WSN) which provides an easy to scale and efficient computing infrastructure for real-time application. A sensor cloud should be energy efficient as the life of the battery in the sensor is limited and there is a huge consumption of energy in the data centre in running the servers to provide storage. In this paper, we have classified energy-efficient techniques for sensor cloud into different categories and analyzed each technology by using various parameters. Usage percentage of each parameter for every technology is calculated and for all technologies on average is also calculated. From our analysis, we found that most of the energy-efficient techniques ignore quality of service (QoS) parameters, scalability, and network lifetime. Multiparameter optimization including other QoS parameters along with energy may be the future direction of research. Our study will be helpful for researchers to get information regarding current methods used for an energy-efficient sensor cloud and also to build advanced systems in the future.

Design and Development of a Parallel Lexical Analyzer for C Language

Futureof computing is rapidlymoving towardsmassivelymulti-core architecture becauseof its powerandcostadvantages.AlmosteverywhereMulti-coreprocessorsarebeingusednow-a-days andnumberofcoresperchipisalsorelativelyincreasing.Toexploitfullpotentialofferedbymulticorearchitecture,thesystemsoftwarelikecompilersshouldbedesignedforparallelizedexecution. Inthepast,varioussignificantworkshavebeenmadetochangethedesignoftraditionalcompiler totakeadvantagesofthefuturemulti-coreplatform.Thispaperfocusesonadaptingparallelismin thelexicalanalysisphaseofthecompilationprocess.Themainobjectiveofourproposalistodothe lexicalanalysisi.e.,findingthetokensinaninputstreaminparallel.Weusetheparallelconstructs availableinOpenMPtoachieveparallelisminthelexicalanalysisprocessformulti-coremachines. Theexperimentalresultofourproposalshowsasignificantperformanceimprovementintheparallel lexicalanalysisphaseascomparedtosequentialversionintermsoftimeofexecution. KeywORDS Compiler, Lexical Analysis, Multi-Core, OpenMP, Threads, Tokenizer

A novel rough set based affinity propagation method for cholesterol prediction from ABC transporter

In biological science, all functions of membrane protein are being modulated by membrane cholesterol. Cholesterol plays a prime role in plasma membrane. The present research emphasized on design of a hybrid rough set based affinity propagation model for prediction of cholesterol sequence from Adenosine Triphosphate Binding Cassette transporters (ABC transporters). Each and every time trans-membrane helix sequences of ABC are evaluated and targeted with cholesterol to find out new valid motif signature. Cholesterol sequences are generated using the formula like Cholesterol Recognition Aminoacid Consensus (CRAC) for forward pattern and reverse of CRAC is CARC for backward pattern. The hypothesis of cholesterol binding motif modulates the signalling pathway of helical membrane protein. Many types of membrane proteins are reported till now, among them G-Protein Coupled Receptors (GPCR) and ABC are important as they are needed for drug discovery. ABC transporter super family is aimed in this work. In experimental study, we explored our hybrid method to find out motif consensus. Finally, from the results we report that our novel technique work efficiently identifying valid motif consensus from ABC protein sequences and is helpful for clinical drug target for human diseases.