Advantages And Disadvantages Of Distributed File System

What is capacity utilization at every step in the process? What is the direct resource utilization?

Evaluation of the queues at each station revealed that Station 3 (Average Queue: 556.6, St Dev: 453.3) was more strained than Station 1 (Average Queue: 386.1, St Dev: 363.3). The firm understands that as queues Figure 2: Machine Utilization and Queues at Littlefield Technologies

Hadoop \cite{white2012hadoop} is an open-source framework for distributed storage and data-intensive processing, first developed by Yahoo!. It has two core projects: Hadoop Distributed File System (HDFS) and MapReduce programming model \cite{dean2008mapreduce}. HDFS is a distributed file system that splits and stores data on nodes throughout a cluster, with a number of replicas. It provides an extremely reliable, fault-tolerant, consistent, efficient and cost-effective way to store a large amount of data. The MapReduce model consists of two key functions: Mapper and Reducer. The Mapper processes input data splits in parallel through different map tasks and sends sorted, shuffled outputs to the Reducers that in turn groups and processes them using a reduce task for each group.

When a queue monitor begins working a ticket, he/she assumes ownership of that ticket until it is routed to the proper support group outside the ITHD. Queue monitors will not route tickets to other agents unless directed to do so by management. All actions that the queue

Group policy can be simply explained as the norms that are followed to assign a job to user.

HDFS is Hadoop’s distributed file system that provides high throughput access to data, high-availability and fault tolerance. Data are saved as large blocks making it suitable for applications

The responsibility assignment matrix (RAM) is a matrix that maps the work of the project as described in the Work Breakdown Structure (WBS)

Hadoop1 provides a distributed filesystem and a framework for the analysis and transformation of very large data sets using the MapReduce [DG04] paradigm. While the interface to HDFS is patterned after the Unix filesystem, faithfulness to standards was sacrificed in favor of improved performance for the applications at hand.

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a Parallel and distributed computing environment. It makes Use of the commodity hardware Hadoop is Highly Scalable and Fault Tolerant. Hadoop runs in cluster and eliminates the use of a Super computer. Hadoop is the widely used big data processing engine with a simple master slave setup. Big Data in most companies are processed by Hadoop by submitting the jobs to Master. The Master distributes the job to its cluster and process map and reduce tasks sequencially.But nowdays the growing data need and the and competition between Service Providers leads to the increased submission of jobs to the Master. This Concurrent job submission on Hadoop forces us to do Scheduling on Hadoop Cluster so that the response time will be acceptable for each job.

Two approaches are used for loading work centres: infinite loading and finite loading. With infinite loading jobs are assigned to work centres without regard for capacity of the work centre. Priority rules are appropriate for use under the infinite loading

A queue has two major operations dequeue and enqueue. An enqueue operations adds an object at the back of the queue while a dequeue operation

Resource allocation is a process that involves the company strategically deciding where resources shoyld be used in the production of goods or services. Allocations will be used to make workflow processes more effective and efficient. This will also require allocations for training. Management must be able to use allocations for resources wisely in order to save money, while

Roles are normally created for the varying business roles or functions. Performance of certain activities is limited to certain job roles or functions. Staff members given the task to undertake such activities are given user accounts unique to them to undertake these roles (Ferraiolo, Kuhn & Chandramouli, 2003). This is normally under the discretion of the immediate supervisor through dialogue with the overall supervisor of a particular division or department.

Over the years it has become very essential to process large amounts of data with high precision and speed. This large amounts of data that can no more be processed using the Traditional Systems is called Big Data. Hadoop, a Linux based tools framework addresses three main problems faced when processing Big Data which the Traditional Systems cannot. The first problem is the speed of the data flow, the second is the size of the data and the last one is the format of data. Hadoop divides the data and computation into smaller pieces, sends it to different computers, then gathers the results to combine them and sends it to the application. This is done using Map Reduce and HDFS i.e., Hadoop Distributed File System. The data node and the name node part of the architecture fall under HDFS.

Then the characteristic of resource pooling. This allows a provider a plethora of computing resources such as data storage & processing. Rapid elasticity is the next and is best described as a network that expands or decreases, in some cases, automatically to meet the needs of a customer. Finally, measured service is cloud computing resource usage can be measured, described as providing transparency for the provider and consumer. Cloud computing services use a metering capability which permits it to control and enhance resource use. The more you utilize the higher the bill.