Dealing with a large amount of data in Data Grids makes the requirement for efficient data access more critical. In this paper, we proposed a new approach to replication problem by organizing the data into several data categories that it belongs to. This organizing will help improving placement strategy of data replication. We studied our approach in combination with scheduling issue and evalua...

Data grid is an infrastructure built based on internet which facilitates sharing and management of geographically distributed data resources. Data sharing in data grids is enhanced through dynamic data replication methodologies to reduce access latencies and bandwidth consumption. Replica placement is to create and place duplicate copies of the most needed file in beneficial locations in the da...

Data Grids seek to harness geographically distributed resources for large-scale data-intensive problems such as those encountered in high energy physics, bioinformatics, and other disciplines. These problems typically involve numerous, loosely coupled jobs that both access and generate large data sets. Effective scheduling in such environments is challenging, because of a need to address a vari...

In modern Data Grid infrastructures, we increasingly face the problem of providing the running applications with fast and reliable access to large data volumes, often geographically distributed across the network. As a direct consequence, the concept of replication has been adopted by the grid community to increase data availability and maximize job throughput. To be really effective, such proc...

In grid computing, resources are used outside the boundary of organizations and it becomes increasingly difficult to guarantee that resources being used are not malicious. Also, resources may enter and leave the grid at any time. So, fault tolerance is a crucial issue in grid computing. Fault tolerance can enhance grid throughput, utilization, response time and more economic profits. All mechan...

MapReduce clusters are usually multi-tenant (i.e., shared among multiple users and jobs) for improving cost and utilization. The performance of jobs in a multitenant MapReduce cluster is greatly impacted by the allMap-to-all-Reduce communication, or Shuffle, which saturates the cluster’s hard-to-scale network bisection bandwidth. Previous schedulers optimize Map input locality but do not consid...

BACKGROUND: When linking the employment rate, retention and cost of turnover, data suggest educators, rehabilitation counselors, providers need to teach people with disabilities job skills improve outcomes. OBJECTIVE: The purpose this narrative literature review was (a) identify evidence-based interventions for (b) specific which may be beneficial adults in postsecondary education, adult educat...

State machine replication is a technique used to guarantee the availability of a system even in the presence of faults. Agreement protocols are often used to implement state machine replication. However, the throughput of many agreement protocols, such as Paxos, does not scale with the number of machines available to the system. Systems whose throughput does scale often provide weaker consisten...

This paper presents a novel heuristic approach, named JDS-HNN, to simultaneously schedule jobs and replicate data files to different entities of a grid system so that the overall makespan of executing all jobs as well as the overall delivery time of all data files to their dependent jobs is concurrently minimized. JDS-HNN is inspired by a natural distribution of a variety of stones among differ...

This paper presents a novel Bee Colony based optimization algorithm, named Job Data Scheduling using Bee Colony (JDS-BC). JDS-BC consists of two collaborating mechanisms to efficiently schedule jobs onto computational nodes and replicate datafiles on storage nodes in a system so that the two independent, and in many cases conflicting, objectives (i.e., makespan and total datafile transfer time)...