V. Rhymend Uthariaraj

Object-oriented software fault prediction using neural networks

This paper introduces two neural network based software fault prediction models using Object-Oriented metrics. They are empirically validated using a data set collected from the software modules developed by the graduate students of our academic institution. The results are compared with two statistical models using five quality attributes and found that neural networks do better. Among the two neural networks, Probabilistic Neural Networks outperform in predicting the fault proneness of the Object-Oriented modules developed.

Object oriented software quality prediction using general regression neural networks

This paper discusses the application of General Regression Neural Network (GRNN) for predicting the software quality attribute -- fault ratio. This study is carried out using static Object-Oriented (OO) measures (64 in total) as the independent variables and fault ratio as the dependent variable. Software metrics used include those concerning inheritance, size, cohesion and coupling. Prediction models are designed using 15 possible combinations of the four categories of the measures. We also tested the goodness of fit of the neural network model with the standard parameters. Our study is conducted in an academic institution with the software developed by students of Undergraduate/Graduate courses.

Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks

Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VMs multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods.

Hierarchical load balancing scheme for computational intensive jobs in Grid computing environment

Decrease in hardware costs and advances in computer networking technologies have led to increased interest in the use of grid computing systems. One of the biggest issues in such systems is the development of effective techniques/algorithms for the distribution of the jobs/load of a grid application on multiple resources to achieve goals such as minimizing execution time, minimizing communication delays, maximizing resource utilization and maximizing throughput. The Grid computing infrastructure load balancing issues are concerned with the traditional distribution of workload among the resources in a Grid environment. To improve the global throughput of these environments, effective and efficient Load Balancing algorithms are fundamentally important. The emergence of computational grids extends this challenge to deal with more serious problems such as scalability, heterogeneity of computing resources and considerable transfer delay. By considering all these issues we proposed a Hierarchical Based Load Balancing algorithm. The main benefit of this algorithm was to reduce the Average Response Time (ART) for the grid application. The algorithm was evaluated on a Java based discrete event grid simulation toolkit called GridSim. The proposed load-balancing algorithm has been compared with other load balancing schemes such as Perfect Information on Arrival (PIA) and Minimum Completion Time (MCT). The results of simulative experiments show that our proposed algorithm is effective. We have realized a significant improvement in average response time. It means that the proposed model can lead to the better load balancing between resources without high overhead.

Efficient algorithms to solve Broadcast Scheduling problem in WiMAX mesh networks

WiMAX is a last mile technology which provides efficient broadband access and coverage like MAN networks. The WiMAX standard IEEE 802.16d also called as WiMAX Mesh network can operate in multihop mode, in which the subscriber stations can communicate with the base station without having direct link between them. In such type of networks, the allocation of channel for the Subscriber Stations is an open issue. Also, if spatial reuse is enabled in such networks then many links can be activated simultaneously, thus increasing the effective usage of the available resources and providing better service to the available nodes. This paper deals with providing an efficient algorithm for spatial reuse. First, a dynamic programming (DP) algorithm to find the conflict-free set of nodes that can be activated to achieve optimality in throughput is proposed. Next, a genetic algorithm is provided, which is more scalable than the DP approach, but does not guarantee optimality. The results indicate that the algorithms proposed give better performance than the existing algorithms that address the same issue.

Sender initiated decentralized dynamic load balancing for multi cluster computational grid environment

Load balancing is essential for efficient utilization of resources and enhancing the performance of computational grid. Job migration is an effective way to dynamically balance the load among multiple clusters in the grid environment. Due to limited capacity of single cluster, it is necessary to share the underutilized resources of other clusters. Each cluster saves information about its neighbors including the transfer delay available between the local resource and its neighbor and the load value of its neighbor. This paper addresses the issues in multi cluster load balancing based on job migration across separate clusters. A decentralized grid model, as a collection of clusters for computational grid environment is proposed. A Sender Initiated Decentralized Dynamic Load Balancing (SI-DDLB) algorithm is introduced. The algorithm estimates system parameters such as the resource processing rate, load on each resource and then balance the load by migrating jobs to the least loaded neighbor resource by taking into account of transfer delay. The main goal of the proposed algorithm is to improve the average response times of jobs. The proposed algorithm has been verified through the GridSim simulation toolkit and compared with the Non Migration (NM) algorithm. Simulation results show that the proposed algorithm yields better performance when compared with the NM algorithm.

A Minimum Time to Release Job Scheduling Algorithm in Computational Grid Environment

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this potential can be realized. One problem that is critical to effective utilization of computational grids is the efficient scheduling of jobs. This work addresses this problem by describing and evaluating a grid scheduling architecture and a job-scheduling algorithm. The architecture is scalable and does not assume control of local site resources. In our algorithm Grid Resource Manager or Grid Scheduler performs resource brokering and job scheduling. The scheduler selects computational resources based on job requirements, job characteristics and information provided by the resources. The main aim of these schedulers is to minimize the total time to release for the individual application. The Time To Release (TTR) includes the processing time of the program, waiting time in the queue, transfer of input and output data to and from the resource. Since grid resources are heterogeneous and distributed over many areas the transmission time is very important criteria. In this paper, an algorithm for minimum time to release is proposed. The proposed scheduling algorithm has been compared with other scheduling schemes such as First Come First Served (FCFS) and Min-Min. These existing algorithms does not consider the transmission time (in time and out time) when scheduling jobs to resources. The proposed algorithm has been verified through the GridSim simulation toolkit and the simulation results confirm that the proposed algorithm produce schedules where the execution time of the application is minimized. The average weighted response times of all submitted jobs decrease up to about 19.79%. The results have been verified using different workloads and Grid configurations.

Evaluation and analysis of computational complexity for secure multicast models

Multicast is an internetwork service that provides efficient delivery of packets from a single source to multiple recipients. When there are large number of members in the group, security and scalability problems arise and an attempt to solve this, gives rise to additional computational complexities at the server. A model is said to be highly efficient if only it has less computational complexity at the server for all membership events and highly secure only when it requires large number of computations to successfully break the multicast model. In this paper, the computational complexities are determined and analyzed for different multicast models. Theoretical evaluation and experimental results prove that for all the membership events, the recently proposed multicast model named LeaSel [3] has computational complexity of O(NSG) when compared to other models which has computational complexity of O(N), where N ≫ NSG. It is also shown that to successfully break LeaSel, the computational complexity is O(SaN) when compared to other models whose computational complexity is O(Sn).

Common Fixed Point Theorem for Non-Self Mappings Satisfying Generalized Ćirić Type Contraction Condition in Cone Metric Space

We prove common fixed point theorem for coincidentally commuting nonself mappings satisfying generalized contraction condition of Ćirić type in cone metric space. Our results generalize and extend all the recent results related to non-self mappings in the setting of cone metric space.

An approach to find redundant objective function(s) and redundant constraint(s) in multi-objective nonlinear stochastic fractional programming problems

Structural redundancies in mathematical programming models are nothing uncommon and nonlinear programming problems are no exception. Over the past few decades numerous papers have been written on redundancy. Redundancy in constraints and variables are usually studied in a class of mathematical programming problems. However, main emphasis has so far been given only to linear programming problems. In this paper, an algorithm that identifies redundant objective function(s) and redundant constraint(s) simultaneously in multi-objective nonlinear stochastic fractional programming problems is provided. A solution procedure is also illustrated with numerical examples. The proposed algorithm reduces the number of nonlinear fractional objective functions and constraints in cases where redundancy exists.