Problem In Grid Computing Research Articles

Immune-Inspired Self-Protection Model for Securing Grid

The application of human immunology in solving security problems in Grid Computing seems to be a thought-provoking research area. Grid involves large number of dynamic heterogeneous resources. Manually managing the security for such dynamic system is always fault prone. This paper presents the simple immune based model for self-protection (SIMS) of grid environment from various attacks like DoS, DDoS, Probing, etc. Like human body helps to identify and respond to harmful pathogens that it doesn't recognize as self, in the same manner SIMS incorporates the immunological concepts and principles for safeguarding the grid from various security breaches.

Hybrid Ant Colony System and Genetic Algorithm Approach for Scheduling of Jobs in Computational Grid

Metaheuristic algorithms have been used to solve scheduling problems in grid computing. However, stand-alone metaheuristic algorithms do not always show good performance in every problem instance. This study proposes a high level hybrid approach between ant colony system and genetic algorithm for job scheduling in grid computing. The proposed approach is based on a high level hybridization. The proposed hybrid approach is evaluated using the static benchmark problems known as ETC matrix. Experimental results show that the proposed hybridization between the two algorithms outperforms the stand-alone algorithms in terms of best and average makespan values.

An improved approach for load balancing among heterogeneous resources in computational grids

In recent days, due to the rapid technological advancements, the grid computing has become an important area of research in distributed systems. The load balancing is a very important and complex problem in grid computing. In this paper, we propose a dynamic-distributed load-balancing technique called the improved load balancing on enhanced GridSim with deadline control (IEGDC) for computational grids. Here, we provide a new mechanism of scheduling to enhance the utilization of the resources and to prevent the resource overloading. A selection method for scheduling by considering the state of resource bandwidth and capacity of various resources is presented. We simulate the proposed load-balancing strategy on the GridSim platform. The proposed mechanism on comparison is found to outperform the existing schemes in terms of response time, resubmitted time, finished and unfinished Gridlets. The simulation results are presented.

Hybrid Discrete Particle Swarm Optimization for Task Scheduling in Grid Computing

Computational Grid is a high performance computing environment that participating machines resources are used through software layer as transparent and reliable. Task assignment problem in Grid Computing is a NP-Complete problem that has been studied by several researchers. The most common objective functions of task scheduling problems are Makespan and Flowtime. This paper gives a classification of meta-heuristic scheduling algorithms in distributed computing that are applicable to grid environment and addresses scheduling problem of independent tasks on Computational Grids. A Hybrid Discrete Particle Swarm Optimization and Min-min algorithm (HDPSO) is presented to reduce overall Completion Time of task.

Efficient Workflow Scheduling for Grid Computing Using a Leveled Multi-objective Genetic Algorithm

Task scheduling and resource allocation are important problems in grid computing. The workflow management system receives inter-dependent tasks from the users and allocates each task to an appropriate resource based on user requirements and constraints such as budget and deadline. Thus this system has a significant effect on performance and the efficient use of resources. In general, mapping tasks to distributed resources is an NP-hard problem. Hence, heuristic and meta-heuristic methods are typically employed. Moreover, since tasks can enter the system at any time, the task scheduling runtime is an important parameter for workflow management systems. This paper presents a fast method for scheduling workflows in a grid environment based on a multi-objective Genetic Algorithm (GA). In the proposed method, the workflows and chromosomes in the GA are assigned to levels to reduce the scheduling time. In addition, the proposed method prevents infeasible solutions being produced in new generations, so task dependencies do not need to be checked. New crossover and mutation operators are proposed to improve convergence and maintain solution diversity. Experimental results are presented and evaluated using several well-known metrics as well as a new metric. This shows the effectiveness of the proposed method compared to other approaches.

Job Scheduling in Grid Computing with Fast Artificial Fish Swarm Algorithm

of the problems in grid computing is job scheduling. It is known that the job scheduling is NP-complete, and thus the use of heuristics is the de facto approach to deal with this practice in its difficulty. The proposed is an apply FAFSA in job scheduling and comparison between FASFA and normal AFSA KeywordLevy, Fast Artificial Fish Swarm Algorithm, FAFSA

Visualization of Job Scheduling in Grid Computers

One of the hot problems in grid computing is job scheduling. It is known that the job scheduling is NP-complete, and thus the use of heuristics is the de facto approach to deal with this practice in its difficulty. The proposed is an imagination to fish swarm, job dispatcher and Visualization gridsim to execute some jobs.

An efficient meta-heuristic algorithm for grid computing

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm optimization (PSO) are needed to solve the problem. PSO is a simple parallel algorithm that can be applied in different ways to resolve optimization problems. PSO searches the problem space globally and needs to be combined with other methods to search locally as well. In this paper, we propose a hybrid-scheduling algorithm to solve the independent task-scheduling problem in grid computing. We have combined PSO with the gravitational emulation local search (GELS) algorithm to form a new method, PSO---GELS. Our experimental results demonstrate the effectiveness of PSO---GELS compared to other algorithms.

A New Hybrid Algorithm to Solve the Task Scheduling Problem in Grid Computing

The new generation of networks, distributed systems, grid computing, which allows users to share files and Users need to use different sources to provide. Grid computing system as one of the competing technologies for cloud computing can be considered to have many advantages for users. One goal of grid computing systems, the management of computing resources for processing user applications or clients So that the resulting high quality of service, lower costs and greater flexibility is. In this paper, to solve the scheduling problem in grid computing system combining genetic algorithms and algorithms of gravity is used.

Task Scheduling Heuristic in Grid Computing

Task scheduling is heart of any grid application which guides resource allocation in grid. Heuristic task scheduling strategies have been used for optimal task scheduling. Heuristic techniques have been widely used by the researchers to solve resource allocation problem in grid computing. In this paper, we classify heuristic task scheduling strategies in grid on the basis of their characteristics. We identified different types of heuristics such as population based heuristic, economic heuristic, meta heuristic, simple heuristic, and hybrid heuristic.

Grid Scheduling using PSO with Naive Crossover

Grid computing can be defined as applying the resources of many computers in a network to a problem which requires a great number of computer processing cycles or access to large amounts of data. Thetask scheduling problem is the problem of assigning the tasks in the system in a manner that will optimize the overall performance of the application, while assuring the correctness of the result. In this paper we use the technique of PSO with Naive crossover to solve the taskscheduling problem in grid computing. The aim of using thistechnique is use the given resources optimally and assign the task to the resources efficiently. The simulated results show that PSO with Naive Crossover proves to be a better algorithm when applied to resource allocation anddisk scheduling in grid computing.

A proposal to apply inductive logic programming to self‐healing problem in grid computing: How will it work?

SUMMARYAs computation systems get extremely large and complex, failure diagnosis becomes even more complex. To cope with this ever increasing complexity of managing heterogeneous systems—such as grids and nowadays clouds—systems should manage their own behavior themselves. This vision of self‐managing systems also referred to as autonomic computing (AC) aims to allow systems to recover themselves from various failures or malfunctions. This is known as self‐healing (SH) and is one of the requirements of AC. However, dealing with these complex failure scenarios is always an open challenge. Dealing with this challenge requires prediction and control through a number of automated learning and proactive actions. In this work, we present the usage of a relational learning method known as inductive logic programming, for prediction and root casual analysis, and the development of an SH component. Copyright © 2011 John Wiley & Sons, Ltd.

Project Scheduling Heuristics‐Based Standard PSO for Task‐Resource Assignment in Heterogeneous Grid

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP‐complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task‐resource assignment problems in grid computing and similar scheduling problems.

A mean field approach for optimization in discrete time

This paper investigates the limit behavior of Markov decision processes made of independent objects evolving in a common environment, when the number of objects (N) goes to infinity. In the finite horizon case, we show that when the number of objects becomes large, the optimal cost of the system converges to the optimal cost of a discrete time system that is deterministic. Convergence also holds for optimal policies. We further provide bounds on the speed of convergence by proving second order results that resemble central limits theorems for the cost and the state of the Markov decision process, with explicit formulas for the limit. These bounds (of order $1/\sqrt{N}$ ) are proven to be tight in a numerical example. One can even go further and get convergence of order $\sqrt{\log N}/N$ to a stochastic system made of the mean field limit and a Gaussian term. Our framework is applied to a brokering problem in grid computing. Several simulations with growing numbers of processors are reported. They compare the performance of the optimal policy of the limit system used in the finite case with classical policies by measuring its asymptotic gain. Several extensions are also discussed. In particular, for infinite horizon cases with discounted costs, we show that first order limits hold and that second order results also hold as long as the discount factor is small enough. As for infinite horizon cases with non-discounted costs, examples show that even the first order limits may not hold.

Min-Min Chromosome Genetic Algorithm for Load Balancing in Grid Computing

Load balancing is a very important and complex problem in grid computing. This paper studies Min-min chromosome genetic algorithm (MCGA) to gauge their suitability for solving grid load balancing problems. The effectiveness of the MCGA algorithm is shown for a number of test problems. Performance comparisons with Min-min, and genetic algorithm are also discussed.

The extended RBAC model based on grid computing

This article proposes the extended role-based access control (RBAC) model for solving dynamic and multidomain problems in grid computing, The formulated description of the model has been provided. The introduction of context and the mapping relations of context-to-role and context-to-permission help the model adapt to dynamic property in grid environment. The multidomain role inheritance relation by the authorization agent service realizes the multidomain authorization amongst the autonomy domain. A function has been proposed for solving the role inheritance conflict during the establishment of the multidomain role inheritance relation.

A parallel genetic simulated annealing hybrid algorithm for task scheduling

In this paper combiend with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm (PGASHA) and applied to solve task scheduling problem in grid computing. It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.

On Building Parallel & Grid Applications: Component Technology and Distributed Services

Software Component Frameworks are well known in the commercial business application world and now this technology is being explored with great interest as a way to build large-scale scientific applications on parallel computers. In the case of Grid systems, the current architectural model is based on the emerging web services framework. In this paper we describe progress that has been made on the Common Component Architecture model (CCA) and discuss its success and limitations when applied to problems in Grid computing. Our primary conclusion is that a component model fits very well with a services-oriented Grid, but the model of composition must allow for a very dynamic (both in space and in time) control of composition. We note that this adds a new dimension to conventional service workflow and it extends the "Inversion of Control" aspects of most component systems.