As a departure from traditional situational perspectives, researchers have given increased attention to the dispositional basis of attitudes. Recently, Hepler and Albarracín (2013) introduced a construct that they called "dispositional attitude" and provided validity evidence for a new scale--the Dispositional Attitude Measure (DAM). Although the DAM was introduced as a "new" approach for asses...

Grid computing systems are increasingly growing importance in the present world with advances in the network technology. Grids are composed of many geographically disturbed resources, each having its own administration domain. Grid computing involves decentralized heterogeneous, geographically distributed resources that can work on a job together. Since the resource availability is dynamic in n...

Data Grid is a geographically distributed environment that deals with large-scale data-intensive applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Data replication is another key optimization technique for reducing access latency and managing large data by storing ...

This paper addresses the scheduling of user jobs in grids so that failures can be avoided in the presence of resources faults. The proposed architecture employs job replication as an effective approach for achieving efficient fault-tolerant and scheduling system. Most of work done using the replication-based approach assumes using a fixed number of replications for each job which consumes more ...

The uncertainties of grid sites security are main hurdle to make the job scheduling secure, reliable and fault-tolerant. Most existing scheduling algorithms use fixed-number job replications to provide fault tolerant ability and high scheduling success rate, which consume excessive resources or can not provide sufficient fault tolerant functions when grid security conditions change. In this pap...

Grid computing is emerging as the best solution to the problems posed by the massive computational and data handling requirements of many current international scientific projects, such as the Large Hadron Collider at CERN. Simulation of the Grid environment is important to evaluate the impact of potential data handling strategies before they are deployed on the Grid. This report looks at the e...

In computational Grid, fault tolerance is an imperative issue to be considered during job scheduling. Due to the widespread use of resources, systems are highly prone to errors and failures. Hence fault tolerance plays a key role in grid to avoid the problem of unreliability. The two main techniques for implementing fault tolerance in grid environment are check pointing and replication. Grid Co...

Computational grids have the potential for solving large-scale scientific applications using heterogeneous and geographically distributed resources. In addition to the challenges of managing and scheduling these applications, reliability challenges arise because of the unreliable nature of grid infrastructure. Two major problems that are critical to the effective utilization of computational re...

The execution of data intensive Grid applications raises several questions regarding job scheduling, data migration, and replication. This paper presents new scheduling algorithms using more sophisticated job behaviour descriptions that allow estimating job completion times more precisely thus improving scheduling decisions. Three approaches of providing input to the decision procedure are disc...

Most replication methods either monitor the popularity of files or use complicated functions to calculate the overall cost of whether or not a replication decision or a deletion decision should be issued. However, once the replication decision is issued, the popularity of the files is changed and may have already impacted access latency and resource usage. This article proposes a decision-tree-...