Grid Resource Selection Research Articles

I-MaxStd: Toward Improving MaxStd Task Scheduling Technique for Cloud Computing Environment

Job scheduling process under the roof of Cloud is consisting of three phases: Resource Discovery, Resource Selection and Task Scheduling (Yousif, A., et al., 2011. A taxonomy of grid resource selection mechanisms. International Journal of Grid and Distributed Computing, 4(3), pp.107.117). Among them, task scheduling is always treated a cumbersome activity because it mapped task(s) to their assigned resource(s) based on various constraints and impositions according to requirements. Task scheduling in Cloud environment is broadly categorized into two streams that are: Heuristic and Meta-Heuristic (sometimes combination of both). Heuristic approach is further categorized into two streams: Immediate mode or online approach and Batch mode or offline scheduling technique. MaxStd, heuristic mapping, is one of the efficient batch modes scheduling technique for independent task(s) due to its inherent efficiency and performance. In this paper, we have proposed an improved version of MaxStd (I-MaxStd) that refines the mapping process of conventional MaxStd to yields an efficient output in the form of reduced makespan and better resource average utilization rate without compromising its legacy. The validation of proposed work has been done for heterogeneous types of ETC matrices being used as dataset.

Resource selection in computational grids based on learning automata

Grid computing, in simplest terms, is distributed computations that have reached higher evolution level. Grid scheduler is part of Grid. Scheduler generates an assignment of jobs to resources using the resource information provided by the Grid information service. Since the problems raised in the resource management system are NP-hard, classical methods, such as dynamic programming, are useful only for small-size problems. Being capable of producing efficient schedules at an acceptable time, even for large samples of the problem, heuristic algorithms are promising methods for solving the scheduling problem. The resource scheduling process in the Grid consists of three main phases: resource discovery, resource selection and job execution. In this paper, we propose an algorithm based on learning automata to resource selection in computational Grid. In this algorithm, decisions are made based on the list of resources that discovered at the resource discovery phase, and after being selected based on predicted time to execution or completion job, they would sent to the next phase namely the execution phase. The efficiency of the proposed algorithm is evaluated through conducting several simulation experiments under different Grid scenarios. The obtained results are compared with several existing methods in terms of the average turn-around time, average response time and throughput.

Characterization of grid computing resources using measurement-based evaluation

An important factor that needs to be considered by every Grid application end-user and systems (such as schedulers or mediators), during Grid resource selection and mapping to applications, is the performance capacity of hardware resources attached to the Grid, and made available through its Virtual Organizations. In this paper, we represent the performance of a computational Grid as a regression model that can be used to fine-tune the selection of suitable Grid resources. A study on the performance of distributed systems with respect to particular variations in parameters is presented. Our objective is to use a measurement-based evaluation technique to characterize the specific performance contribution of the individual Grid resource configurations. In the process, we identify the key primary parameters (or factors) that should be considered when selecting and allocating a computational node for user application execution.

A Novel Optimization Model Based on the Unification of Proximity and Semantic Similarity in Grid Computing

Resources in Grid computing are geographically distributed across the world through a wide area network under various virtual organizations. Due to the distributed nature of the Grid, the selection and allocation of the optimal resources from the available resource are challenging. However, the overall Grid performance depends on the selection of Grid resources for user jobs. A significant amount of effort has been made by proposing various resource discovery algorithms. Current Grid literature reveals that the semantic matching can provide more results compared to syntax matching on available resources, but the selection of poor resources for user jobs can affect the Grid performance. The reason for poor selection is because of the allocation of Grid resources based on First Come First Serve (FCFS) scheme, which reduces the utilization of a domain-based semantic ontology Grid system. To overcome the issue and enhance the Grid performance, we propose a novel optimization model based on Unification of Proximity and Semantic similarity in Grid Computing. The purpose of this optimization model is to get optimized resources for user jobs, so that Grid brokers could select optimum resources in terms of proximity with high semantic relevancy. The proposed model utilizes both semantic and proximity criteria and avoids the resources that are not suitable and faraway from the user locations. The model is designed using GridSim and FreePastry simulation & modeling toolkits. The experimental results have been compared with the (FCFS) allocation scheme that shows that the proposed optimization model statistically significantly outperforms the system with FCFS scheme.

A Comparative Analysis of Adaptive Solutions for Grid Environments

Grid computing environments are distributed systems composed by heterogeneous and geographically distributed resources. This type of systems mainly emerged to satisfy the increasing computing power demand within the scientific community. Despite the advantages of such paradigm, there are still several challenges related to the discovery, monitoring and selection of grid resources. Moreover, the dynamic nature and changing characteristics of such environments worsen the applications performance. Thus, improving their efficiency is a fundamental issue. The present contribution analyses two self-adaptive solutions focused on enhancing the grid resource selection process by using resources in an efficient way. On the one hand, the Efficient Resources Selection model which is defined from the user's point of view (it avoids controlling or modifying the infrastructure) and it is based on the Scatter Search method for achieving a suitable selection of resources. On the other hand, Montera2, a framework designed for addressing an efficient execution of distributed applications on the grid; it defines and employs a dynamic scheduling algorithm to determine the size and number of tasks to be executed. Both approaches have been tested on a real European infrastructure belonging to the well-known European Grid Infrastructure (EGI) project. The study also compares both solutions with the standard scheduling technique that governs this infrastructure, the gLiteWMS scheduler, showing a much better performance by reducing the final makespan by a factor of 20 if compared to the gLiteWMS scheduler. An analysis of task and time overheads for both approaches is also included. Furthermore, comparisons with many other solutions proposed in the literature are presented, showing the advantages of our approaches.

Intelligent self‐adaptive resources selection for grid applications

SummaryGrid computing is considered a promising trend, which enables the sharing of a wide variety of computational and storage resources geographically distributed. Despite the advantages of such paradigm, several problems have emerged during the last decade; most of them caused by an inefficient utilization of grid resources. The present contribution proposes an approach to improve the grid resources selection process. An optimization model for choosing grid resources in an intelligent way has been designed. A mathematical formulation to monitor the resources efficiency has also been established. Furthermore, the model provides a self‐adaptive capability to grid applications, enhancing them for dealing with the changing environmental conditions. The model applies an artificial intelligence algorithm for ensuring an efficient selection. In particular, three different versions have been implemented. Each of them uses a different algorithm. Finally, during the evaluation phase of the model, the experimental tests were performed in a real grid infrastructure. The results show that the model improves the infrastructure throughput, by increasing the finished tasks rate and by reducing the applications execution time. Copyright © 2014 John Wiley & Sons, Ltd.

Economic Trust Model in Grid Computing Systems for Resource Selection

Grid technology provides the sharing different types of resources to get huge computational power, immense data and better services. Since resources are heterogeneous and widely geographical distributed in grid environments, resource selection and economy are the important challenges. This paper presents an economic trust model which aims at achieving a unique model incorporating various criteria that are important for the calculation of trust values and the decision whether or not to trust an entity. Among the existent criteria, cost trade off, test result, self defence capability, direct trust and reputation are exploited. A fuzzy method using Multi Criteria Decision Making is applied. The obtained results show better performance of the presented method with respect to the number of failed requests and waiting time, but it needs more execution time. Keywords: trust, economy, cost trade off, reputation, multi criteria decision making

RESOURCE SELECTION IN GRID ENVIRONMENT BASED ON TRUST EVALUATION USING FEEDBACK AND PERFORMANCE

A grid computing is a collection of distributed computing resources owned by many organizations across boundaries and not dedicated for a single user available in the Virtual Organization. Grid computing environment provides more computational capabilities to increase the scalability and efficiency of the infra-structure. Resource selection in computational grid is the open issue for executing complex jobs. Trust is the one of the important factor for selecting the resources in computational grid. We introduced a new technique for finding the trusted resource. In our proposed method, we computed the trusted resource from user’s feedback and performance. While selecting the resource, overall trustworthiness is compared. The result of the resource selection algorithm is that most trust resource weight is above the threshold level. In our proposed algorithm job is submitted to the trust resource available in grid.

Developing a Novel Trust Model for the Best Resource Selection among the Resources in Grid Environment

Grid computing is a wide area distributed parallel computing environment where it focuses on communication among different heterogeneous devices and harnesses unused processor cycles of all computers in a network for solving critical problems too intensive for any stand-alone machine. In this scenario security is vital concern because of dynamic allocation of users and resources. Among all other security issues trust establishment is given first priority.In this paper, we propose a novel model for resource selection in Grid computing environment. This model is truly unique model where decision is based on computed total trust which includes transaction, feedbacks, reputation, similarity, activity, popularity and obviously the total trust of the resources. The final decision is depend on the total trust of each resource. So the main motto of our project is to retrieve the best resource among the resources in grid environment. This paper provides a model which will allow only dependable transactions in grid by using total trust as a measure for the resources.

Grid Resource Selection Model based on Individual Preference and Trust

Grid Resource Selection Model based on Individual Preference and Trust

Research on Grid Scheduling and Resources Selection Algorithm

Grid, as a new tool for solving problems, is applied to science, engineering, industries and commerce. More and more application programs are making use of grid infrastructure to meet their needs of computing, storage and other aspects. The effectiveness of a grid environment depends on the effectiveness and efficiency of its scheduler. Grid scheduling is the process that maps grid jobs to the resource in multiple administrative domains. The article does research on the principles and methods of grid scheduling and gives a description about grid resource section by way of quantitative evaluation.

Agent Based Priority Heuristic for Job Scheduling on Computational Grids

A grid is an infrastructure for resource sharing. At present, many scientific applications require high computing power in processing, which can only be achieved by using the computational grid. For the selection and allocation of grid resources to current and future applications, grid job scheduling is playing a very vital role for computational grids. They constitute the building blocks for making grids available to the society. The efficient and effective scheduling policies, when assigning different jobs to specific resources, are very important for a grid to process high computing intensive applications. This paper presents an agent based job scheduling algorithm for efficient and effective execution of user jobs. This paper also includes the comparative performance analysis of our proposed job scheduling algorithm along with other well known job scheduling algorithms considering the quality of service parameters like waiting time, turnaround time, response time, total completion time, bounded slowdown time and stretch time. We also conducted the QoS based evaluation of the scheduling algorithms on an experimental computational grid using real workload traces. Experimental evaluation confirmed that the proposed grid scheduling algorithms posses a high degree of optimality in performance, efficiency and scalability. This paper also includes a statistical analysis of real workload traces to present the nature and behavior of jobs.

An efficient approach based on trust and reputation for secured selection of grid resources

Security is a principal concern in offering an infrastructure for the formation of general-purpose computational grids. A number of grid implementations have been devised to deal with the security concerns by authenticating the users, hosts and their interactions in an appropriate fashion. Resource management systems that are sophisticated and secured are inevitable for the efficient and beneficial deployment of grid computing services. The chief factors that can be problematic in the secured selection of grid resources are the wide range of selection and the high degree of strangeness. Moreover, the lack of a higher degree of confidence relationship is likely to prevent efficient resource allocation and utilisation. In this paper, we present an efficient approach for the secured selection of grid resources, so as to achieve secure execution of the jobs. This approach utilises trust and reputation for securely selecting the grid resources. To start with, the self-protection capability and reputation weightage of all the entities are computed, and based on those values, the trust factor (TF) of all the entities are determined. The reputation weightage of an entity is the measure of both the user's feedback and other entities' feedback. Those entities with higher TF values are selected for the secured execution of jobs. To make the proposed approach more comprehensive, a novel method is employed for evaluating the user's feedback on the basis of the existing feedbacks available regarding the entities. This approach is proved to be scalable for an increased number of user jobs and grid entities. The experimentation portrays that this approach offers desirable efficiency in the secured selection of grid resources.

Grid resources selection optimization with quality of service guarantee by a hybrid algorithm of genetic and particle swarm optimization

When a large number of tasks request different resources in a grid system, resources allocation should have been done with proper planning and scheduling to guarantee a good quality of service (QOS). There are different ways to provide these requests by choosing appropriate resources allocation to optimize the total operation of the system. In this study, first some parameters, such as priority, delay, reliability and cost are determined for each task to maximize system performance and appropriate resources distribution. Then, a hybrid optimization algorithm for choosing grid resource based on genetic algorithm and particle swarm optimization (PSO) is presented. Based on the experimental results, this method’s performance is 17.5% more than genetic and PSO algorithms in average. Key words: Grid system, resources selection, quality of service, genetic algorithm, particle swarm optimization (PSO).

Performance Analysis in Secured Grid Resource Selection based on Trust and Reputation

Grid computing is the robust technology which is used to utilize the idle or available resources very effectively is solving the large scale or scientific problems. The security is a major concern in selecting a high degree of strange resources available. In this paper we have compared our proposed approach with the previous approach and the experimental analysis have proved that the proposed approach is best in selecting the secured resource based on trust and reputation. This also ensures that the proposed approach provides better results for large number of malicious nodes compared with the previous approach.

A Self-Adaptive Grid Resource Selection Algorithm

In order to provide high Quality of Service (QoS), rational scheduling and resource allocation are needed when a great deal of tasks requesting grid resources. By quantifying some important properties of Grid QoS and maximizing cost-performance ratio of Grid system, a QoS-guaranteed self-adaptive grid resource selection algorithm based on SAGA which is named QoS-SGRSA is proposed, and the flow of the algorithm is introduced. In the low load condition, traditional resource selection algorithm is adopted, so an allocation policy is found rapidly. While in the high load condition, by means of using complementary advantages of GA and SA, it can guarantee that the search is on the optimization direction. Finally, we evaluated the effectiveness of QoS-SGRSA by emulation experiments.

GEVP: Grid Enabled Visualization Pipeline

The current grid computing practices and techniques built on static resource management technique associate with several drawbacks. These drawbacks forced the current grid systems of taking the direction of remote viewing and zooming techniques of already processed images. There is an urgent need for algorithms and techniques to support automatic resources discovery and selection of grid resources. This research investigates how the grid services could be used to support remote visualization. The implemented grid enabled visualization architecture is supported with an automatic resources selection mechanism to enable the visualization pipeline components to be automatically orchestrated without human interference. The presented findings are supported with practical implementation of grid enabled visualization prototype.

A Bidding-based Grid Resource Selection Algorithm using Single Reservation Mechanism

selection of resources that best fitting tasks in grid environments is an essential and critical factor to system performance. Current resource selection methods apply traditional mechanisms for the process of task allocation to resources, which cause performance problems. This paper proposes a resource selection algorithm in bidding based grid environment to minimize the total time for task completion in effective and efficient way. To avoid unexpected completion time arises when using non-reserved bidding process due to the competition between grid clients on resources; our algorithm proposes the single reservation mechanism to reserve the best resource for the task as a commitment and hence guarantees the task completion time will be as expected. This algorithm uses single reservation mechanism to reserve the best resource for the task. Unlike traditional reserved algorithms this algorithm reserves only the best resource and hence it allows other resources to participate in other bidding processes. We believe the proposed algorithm can select the most fitting resources for tasks execution and achieve a good performance in terms of effectiveness and efficiency.

A Comprehensive Optimization Model Based on Time and Cost Constraints for Resource Selection in Data Grid

Parallel data transmission based on multi-copy can enhance transmission speed and ensure the QoS of data grid greatly. The status of network and replica node, the distance of replica node, the time and bandwidth of service requester will directly affect service cost. How to take the above factors into account, so as to provide basis for node selection and bandwidth allocation, guarantee the time constraint of service requestor and optimize service cost is a key problem need to be solved urgently. Based on ‘0-1’ integer programming and linear programming methods, respectively, a minimum transfer time model, minimum cost model and comprehensive optimization model are proposed to solve the above problem. Simulative experiments show that the models are correct and effective.

An adaptive fault tolerance approach to enhance the execution of applications on multi-cluster grid configurations from mobile grid interfaces in wireless networks

Mobile grid environments can be broadly divided into two different types of configurations. In the first case, devices are used as grid interfaces. In the second configuration, mobile devices are considered to be grid resources. However, the interaction between mobile devices and wired networks still has to overcome several difficulties before it can produce an operational mobile grid infrastructure, in either type of configuration. Examples of these challenges include the submission of distributed applications to execute tasks in a coordinated manner, frequent disconnections, restrictions on power consumption and the selection of suitable grid resources for application executions. In this paper, a research study is conducted involving an adaptive fault tolerance approach for grid interfaces in wireless networks to enhance the submission and execution of distributed applications. The approach examines submission, monitoring and management functions through the use of fault tolerance and workflow mechanisms, especially when disconnections occur and the mobile devices are executing an application. The empirical results show that the proposed approach can achieve a satisfactory level to support grid interfaces in a wireless network, and provide a more robust environment for application executions with less power.