C. R. Rao

Smart Replica Selection for Data Grids Using Rough Set Approximations (RSDG)

The best replica selection problem is one of the important aspects of data management strategy of data grid infrastructure. Recently, rough set theory has emerged as a powerful tool for problems that require making optimal choice amongst a large enumerated set of options. In this paper, we propose a new replica selection strategy using a grey-based rough set approach. Here first the rough set theory is used to nominate a number of replicas, (alternatives of ideal replicas) by lower approximation of rough set theory. Next, linguistic variables are used to represent the attributes values of the resources (files) in rough set decision table to get a precise selection cause, some attribute values like security and availability need to be decided by linguistic variables (grey numbers) since the replica mangers’ judgments on attribute often cannot be estimated by the exact numerical values (integer values). The best replica site is decided by grey relational analysis based on a grey number. Our results show an improved performance, compared to the previous work in this area.

Parameterless penalty function for solving constrained evolutionary optimization

A criticism of Evolutionary Algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods, because of their simplicity and ease of implementation. The penalty function approach is generic and applicable to any type of constraint (linear or nonlinear). Nonetheless, the most difficult aspect of the penalty function approach is to find an appropriate penalty parameters needed to guide the search towards the constrained optimum. In this paper, GAs population-based approach and Ranks are exploited to devise a penalty function approach that does not require any penalty parameter called Adaptive GA-RRWS. Adaptive penalty parameters assignment among feasible and infeasible solutions are made with a view to provide a search direction towards the feasible region. Rank-based Roulette Wheel selection operator (RRWS) is used. The new adaptive penalty and rank-based roulette wheel selection operator allow GAs to continuously find better feasible solutions, gradually leading the search near the true optimum solution. GAs with this constraint handling approach have been tested on five problems commonly used in the literature. In all cases, the proposed approach has been able to repeatedly find solutions closer to the true optimum solution than that reported earlier.

Privacy preserving data mining using particle swarm optimisation trained auto-associative neural network: an application to bankruptcy prediction in banks

While data mining made inroads into the diverse areas it also entails violation of individual privacy leading to legal complications in areas like medicine and finance as consequently, privacy preserving data mining (PPDM) emerged as a new area. To achieve an equitable solution to this problem, data owners must not only preserve privacy and but also guarantee valid data mining results. This paper proposes a novel particle swarm optimisation (PSO) trained auto associative neural network (PSOAANN) for privacy preservation. Then, decision tree and logistic regression are invoked for data mining purpose, leading to PSOAANN + DT and PSOAANN + LR hybrids. The efficacy of hybrids is tested on five benchmark and four bankruptcy datasets. The results are compared with those of Ramu and Ravi (2009) and others. It was observed that the proposed hybrids yielded better or comparable results. We conclude that PSOAANN can be used as viable approach for privacy preservation.

Replica Selection in Data Grids Using Preconditioning of Decision Attributes by K-means Clustering (K-RSDG)

This paper extends the applicability of the Rough Set Replica Selection Strategy in Data Grids, RSDG, proposed previously for such situations where the history of replica sites is unavailable. Grey based Rough Set Theory is applied using replicas information only as an input data. The Decision attributes are derived applying Grey based K-means clustering algorithm upon the input data. Each cluster label of K clusters represents a class of Decision attribute in the Decision Table of the rough set. Comparing to the previous work the synthetic data experimentation shows the improvement in the overall performance.

Rank-Based Genetic Algorithm with Limited Iteration for Grid Scheduling

In Grid Computing the number of resources and tasks is usually very large, which makes the scheduling task very complex optimization problem. Genetic algorithms (GAs) have been broadly used to solve these NP-complete problems efficiently. On the other hand, the Standard Genetic algorithm (SGA) is too slow when used in a realistic scheduling due to its time-consuming iteration. This paper proposes a new Rank-based Roulette Wheel Selection Genetic Algorithm (RRWSGA) for scheduling independent tasks in the grid environment, which increases the performance and the quality of schedule with a limited number of iterations, RRWSGA improves the reliability in the selection process while matching an acceptable output. A fast reduction of makespan making the RRWSGA of practical concern for grid environment. The results are encouraging, and can be used for real-world scheduling problems.

Extensions to IQuickReduct

IQuickReduct algorithm is an improvement over a poplar reduct computing algorithm known as QuickReduct algorithm. IQuickReduct algorithm uses variable precision rough set (VPRS) calculations as a heuristic for determining the attribute importance for selection into reduct set to resolve ambiguous situations in Quick Reduct algorithm. An apt heuristic for selecting an attribute helps in producing shorter non redundant reducts. This paper explores the selection of input attribute in ambiguous situations by adopting several heuristic approaches instead of VPRS heuristic. Extensive experimentation has been carried out on the standard datasets and the results are analyzed.

Design of a Structured Fine-Grained Access Control Mechanism for Authorizing Grid Resources

The heterogeneous and very dynamic nature of a grid environment demands a very scalable authorization system. This brings out the need for a fast fine-grained access control (FGAC) mechanism for grid resources to accomplish the required scalability. This paper is proposing a new method of storing and manipulating the resources security policy in a way that enables authorization systems to avoid redundancy in security policy checking and to more rapidly specify which group of resources the authenticated user is authorized to access. Comparative study is made in a simulated environment. The proposed method can be integrated in the present authorization systems for better performance.

A two phased service oriented Broker for replica selection in data grids

Replica selection is one of the fundamental problems in Data Grids environment. This works concern is designing a Two phasedService Oriented Broker (2SOB) for replica selection. It is focused on investigating, selecting, modifying, and experimenting with some non-conventional approaches to be applied on the relevant selection techniques. The motivation of this work is to introduce a novel Service OrientedBroker for Replica Selection in Data Grid. The main characteristics of this 2SOB are: Scalability, Reliability, Availability, Efficiency and Ease of deployment. 2SOB consists of two phases; the first is a Coarse-grainphase, basically used for sifting replica sites that have low latency (uncongested network links) and distinguishing them from other replicas having a high latency (congested network links). Procedurally, this has been done using the association rules concept of the Data Mining approach. The second is a Fine-grainphase,used for extracting the replicas admissible for user requirements through applying Modified Minimum Cost and Delay Policy (MMCD). Both phases have accordingly been designed, simulated, coded, and then validated using real data from EU Data Grid.The first phase has thereby been applied on the real network data of CERN (February 2011). Experimentations compared with some other contemporary selection methods of different Brokers showed appreciable results. Using this proposed Broker it is possible to achieve an enhancement in the speed of executing Data Grid jobs through reducing the transfer time.

Enhancing the hierarchical clustering mechanism of storing resources’ security policies in a grid authorization system

Many existing grid authorization systems adopt an inefficient structure of storing security policies for the available resources, which reduces the scalability and leads to huge repetitions in checking security rules. One of the efficient mechanisms that handles these repetitions and increases the scalability is the Hierarchical Clustering Mechanism (HCM) [1]. HCM outperforms the Brute Force Approach as well as the Primitive Clustering Mechanism (PCM). This paper enhances HCM to accommodate the dynamism of the grid and the same is demonstrated using new algorithms.

Solving Constrained Multi-objective Optimization Problems Using Non-dominated Ranked Genetic Algorithm

A criticism of Evolutionary Algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods, because of their simplicity and ease of implementation. Nonetheless, the most difficult aspect of the penalty function approach is to find an appropriate penalty parameters. In this paper, a method combining the new Non-dominated Ranked Genetic Algorithm (NRGA), with a parameterless penalty approach are exploited to devise the search to find Pareto optimal set of solutions. The new Parameterless Penalty and the Nondominated Ranked Genetic Algorithm (PP-NRGA) continuously find better Pareto optimal set of solutions. This new algorithm have been evaluated by solving four test problems, reported in the multi-objective evolutionary algorithm (MOEA) literature. Performance comparisons based on quantitative metrics for accuracy, coverage, and spread are presented.