Arnab Mitra

Memory utilization in cloud computing using transparency

In this paper we are going to propose memory utilization method in cloud computing environment. End-user generates a service request for selecting memory of service based resources. The proposed mechanism enables users to access memories depending on the predefined criteria. Selection method for accessing the memory of a resource is introduced in this paper. Transparency is used as a virtualization, where multiple memories of several resources appear to the user as a single unit. The resources are virtualized and further delivered to the users as services. The aim of this paper is to select a computing memory of a resource by a service request. Service based memory utilization procedure is described in this paper. In particular, this paper seeks increasing the efficiency of using the memories in distributed manner. We introduce cloud service based memory utilization which is an effective mechanism for allocating memories in cloud computing environment. New types of memory architecture have been discussed for maintaining effectiveness in our approach.

Cost Optimized Approach to Random Numbers in Cellular Automata

In this research work, we are trying to put emphasis on on the cost effective generation approach of high quality random numbers using one dimensional cellular automaton. Maximum length cellular automata with higher cell number, has already been established for the generation of highest quality of pseudo random numbers. Sequence of randomness quality has been improved using DIEHARD tests reflecting the varying linear complexity compared to the maximum length cellular automata. The mathematical approach for proposed methodology over the existing maximum length cellular automata emphasizes on flexibility and cost efficient generation procedure of pseudo random pattern sets.

CA based cost optimized PRNG for Monte-Carlo simulation of distributed computation

Monte-Carlo (M-C) simulations are widely used today to evaluate the reliability of any distributed system. M-C simulations are generated using high quality pseudo-random numbers. Different pseudo-random number generators (PRNGs) are being used as a source of random patterns for M-C simulation. In this research, we have emphasized a cost effective design methodology for the pseudo-random numbers, using Cellular Automata (CA). We have introduced a behavioural discussion and examined the benefits of our proposed Equal Length Cellular Automata (ELCA) based PRNG. This research is expected to contribute to reducing the overhead use for fault coverage in generation of random integers using Cellular Automata. In the proposed design methodology, a high degree of randomness is maintained in generated patterns, while focusing on the reduction of various associated complexities, like design complexity, time complexity and searching complexity. The result achieved in the experiment reflects the high quality of randomness in the generated patterns for ELCA PRNG over the Maximum Length Cellular Automata (Max CA) PRNG and M-C PRNG.

A new approach in dynamic prediction for user based web page crawling

Maximum available Web prediction techniques typically follow Markov model for Web based prediction. Everybody knows that there are lots of Web links or URLs on any Web page. So, it is very hard to predict the next Web page from the huge number of Web links. Existing approaches predict successfully on the private (personal) computer using different Markov models. In case of public (like cyber cafe) computers, prediction can not be done at all, since many people use the same machine in this type of scenario. In this paper, we propose a new policy on Web prediction using the dynamic behavior of users. We demonstrate four procedures for Web based prediction to make it faster. Our technique does not require any Web-log or usage history at client machine. We are going to use the mouse movement and its direction for the prediction of next Web page. We track the mouse position and its respective direction instead of using Markov model. In this research work, we introduce a fully dynamic Web prediction scheme, since Web-log or any type of static or previous information has not been utilized in our approach. In this paper, we try to minimize the number of Web links to be considered of any Web page in runtime for achieving better accuracy in dynamic Web prediction. Our approach shows the step-wise build-up of a solid Web prediction program which is appropriate in both the private as well as public scenario. Overall, this method shows a new way for prediction using dynamic nature of the respective users.

Analysis of sequences generated by ELCA-type Cellular Automata targeting noise generation

The Equal Length Cellular Automata (ELCA) are suggested as a mean for generating pseudo-random sequences. For supporting this application, an analysis of the randomness of the generated sequences is performed for a variety of automata, using several analysis methods. The results of the analysis are presented and discussed. In addition, the complexity of the generation method is compared with other types of pseudo-chaotic generators. Potentiality of usage in noise generating application targeting security of data in microsystems is envisaged.

Energy Efficient Task-Pull Scheduling Using Equal Length Cellular Automata in Distributed Computing

Energy efficient task-pull scheduling for distributed computing has been reported in this research work. Cellular Automata (CA) based scheduling algorithm has been projected in this report. We have emphasized on the usage of Equal Length Cellular Automata (ELCA) for the allocation of equal length task-pulls to maximize the employment of available processing units. We have introduced a behavioral discussion and examined the benefits of our proposed ELCA based taskpull scheduling methodology. High degree of randomness in generated equal length task-pulls and equal load distribution to available processing units have been established by experimental results. Low energy consumption for any distributed computing environment using ELCA scheduler has been successfully achieved. Efficient utilization of maximum number of available central processing units (CPUs) is achieved using proposed ELCA based scheduling.

Cost Optimized Random Sampling in Cellular Automata for Digital Forensic Investigations

In today’s world, advancement of Information Technology has been simultaneously followed by cyber crimes resulting in offensive and distressful digital contents. Threat to the digital content has initiated the need for application of forensic activities in digital field seeking evidence against any type of cyber crimes for the sake of reinforcement of the law and order. Digital Forensics is an interdisciplinary branch of computer science and forensic sciences, rapidly utilizing the recovery and/or investigation works on digital data explored in electronic memory based devices with reference to any cyber based unethical, illegal, and unauthorized activities. A typical digital forensic investigation work follows three steps to collect evidence(s): content acquisition, content analysis and report generation. In digital content analysis higher amount of data volumes and human resource(s) exposure to distressing and offensive materials are of major concerns. Lack of technological support for processing large amount of offensive data makes the analytical procedure quite time consuming and expensive. Thus, it results in a degradation of mental health of concerned investigators. Backlog in processing time by law enforcement department and financial limitations initiate huge demand for digital forensic investigators turning out trustworthy results within reasonable time. Forensic analysis is performed on randomly populated sample, instead of entire population size, for faster and reliable analysis procedure of digital contents. Present work reports about an efficient design methodology to facilitate random sampling procedure to be used in digital forensic investigations. Cellular Automata (CA) based approach has been used in our random sampling method. Equal Length Cellular Automata (ELCA) based pseudo-random pattern generator (PRPG) has been proposed in a cost effective manner utilizing the concept of random pattern generator. Exhibition of high degree randomness has been demonstrated in the field of randomness quality testing. Concerned cost effectiveness refers to time complexity, space complexity, design complexity and searching complexity. This research includes the comparative study for some well known random number generators, e.g., recursive pseudo-random number generator (RPRNG), atmospheric noise based true-random number generator (TRNG), Monte-Carlo (M-C) pseudo-random number generator, Maximum Length Cellular Automata (MaxCA) random number generator and proposed Equal Length Cellular Automata (ELCA) random number generator.Resulting sequences for all those above mentioned pattern generatorshave significant improvement in terms of randomness quality. Associated fault coverage is being improved using iterative methods. Emphasis on cost effectiveness has been initiated for proposed random sampling in forensic analysis.

Random number generators: performance comparison of ELCA and MaxCA

In this paper, we have compared the performances of different cellular automata based random number generators to emphasize on the quality of randomness with a focus on cost effectiveness for concerned fault coverage. This research includes the study of maximum length cellular automata random number generator and proposed equal length cellular automata random number generator. It is found from the experimental results that resulting sequences have significant improvement in terms of randomness quality and associated fault coverage in their generation procedures. The different complexities associated considered here for generation of random numbers, are: space complexity, time complexity, design complexity and searching complexity.

Cost effective PRNG using ELCA: A BIST application

Generation of pseudo-random numbers using Cellular Automata (CA) for Built-In-Self-Test (BIST) applications has been emphasized in this work. Enhanced cost effectiveness is achieved for the generation procedure of pseudorandom patterns with proposed Equal Length CA (ELCA) with reference to Maximum-length CA (MaxCA). Proposed ELCA scheme generates patterns for BIST and achieves higher quality of fault coverage compared to MaxCA and Linear Feedback Shift Register (LFSR) based BIST pattern generator. Proposed ELCA based pattern generator is significantly contributing a role in overhead reduction of fault coverage for BIST pattern generator. A high degree of randomness is maintained in generated patterns in proposed design focusing on the reduction of some major associated complexities such as design complexity, time complexity and searching complexity. Experimental results have ensured that high degree of randomness and comparative high degree of fault coverage are achieved for proposed ELCA based BIST application.