Mga 2110 Unit 2

M1- Assess the likely immediate effects of two different forms of abuse on the health and wellbeing of adults

The two bigger things I plan to continue working on with Aaron are his social skills and his anxiety. Together, him and I will continue working on positive social skills and how to interact with others. He is not one that enjoys role play. He would much rather just sit and talk. I would also like to keep working on art projects or any hands on project where he can write things down. This way, we are not just discussing out loud what to work on, but we are writing it down or drawing pictures which helps us to think about the things we are working on in other ways as well. It is one thing to talk about it, but it is another to draw pictures or write it down. His poor social skills and his grades create a lot of anxiety in his life, so I plan to keep working on different coping skills with him. He has informed me in the past that his favorite coping skills is counting to ten. We have been going over breathing and counting to ten when he is anxious. As of now, he needs a lot of reminders to breathe use a coping

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.

Different retailing businesses have very different distribution methods based on the types of product that they sell, some arguably more effectively than others.

Quantitative data can be described as measurable factual and accurate data, i.e. sales, volumes and costs. Can be developed with historic records. Quantative data is more objective rather than subjective focusing on key issues.

Over the past few years, the needs for special-purpose applications that could handle large amount of data have increased dramatically. However, these applications required complex concepts of computations such as parallelizing the tasks, distributing data, and taking care of failures. As a reaction to this problem, a new abstract layer that allows us to express the simple computations we were trying to perform but hides the complex details was designed, MapReduce. This paper is an influential paper in the field of large scale data processing. It simplifies the programming model for processing large data set. The paper describes a new programming model based on lisp’s map and reduces primitives for processing large data set. In addition, the paper also describes a framework to automatically parallelize the map tasks across various worker machines.

Figure 5.22: velocity, acceleration, and roof displacment of twenty-story regular RC building due to (a) 1957 San Francisco (Golden Gate Park) GGP010 component, andFigure 5.24: : velocity, acceleration, and roof displacment of twenty-story regular RC building due to (a)1979 Imperial Valley-06 (Holtville Post Office) H-HVP225 component, and (b) IS 1893 (Part1) : 2002 ground motion in z-direction (b) 1940 Imperial Valley (El Centro) ElcentroEW component ground motion in x-direction

MapReduce Parallel programming model if we ever get a chance. In Hadoop, there are two nodes in the cluster when using the algorithm, Master node and Slave node. Master node runs Namenode, Datanode, Jobtracker and Task tracker processes. Slave node runs the Datanode and Task tracker processes. Namenode manages partitioning of input dataset into blocks and on which node it has to store. Lastly, there are two core components of Hadoop: HDFS layer and MapReduce layer. The MapReduce layer read from and write into HDFS storage and processes data in parallel.

First off to start the assignment only requires writing about four different types of failures that can happen on a distributed system, however there are many more than just four types of failures that can happen and they are all important to learn about if you are going to work with a distributed system so that you know how to deal with and handle each one of them.

As we can see from the comparison tables, all computing tools provide these facilities. Hadoop distributes data as well as computing via transferring it to various storage nodes. Also, it linearly scales by adding a number of nodes to computing clusters but shows a single point failure. Cloudera Impala also quits execution of the entire query if a single part of it stops. IBM Netezza and Apache Giraph whereas does not have single point failure. In terms of parallel computation IBM Netezza is fastest due to hardware built parallelism.

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.

The aim of work is to provide service guarantees when multiple synchronous requests are present with high disk throughput. To address this problem we consider BFQ and modified versions of BFQ. It is found that MBFQV1 gives a better performance when compared with the BFQ. MBFQV2 is the suggested new disk scheduler which preserve both guarantees and a high throughput. In MBFQV2 we observed that the throughput, speed of transfer were better compared to the other schedulers for the normal size applications.

The human resources department needs to revisit some of their decisions to strength their portion of the structure and better the company for the future. The high turnover rate has caused lack of employee motivation, low morale and with pay levels below their competitors’standards; there is lack of structure in the performance review process within the entire company. These issues can be corrected by creating a coaching, feedback process, and

Grid computing consists of some disadvantages too. Some applications need to be modified in order to use this application in grid computing. The applications which are not designed to use an MPI (Message Passing Interface) will have to revert to an SMP (Symmetric Multi-Processing). Grid computing requires an advanced infrastructure like small servers, fast connections between the servers and the use of quality tools, software and skilled technicians to manage the grid.This grid computing is also costly and security issues also need to be

Map/Reduce is a structure for preparing parallelizable issues crosswise over gigantic datasets utilizing a substantial number of PCs (hubs), all things considered alluded to as a group i.e. if all hubs are on the same local network and use the same hardware) or a framework i.e. if the hubs are shared crosswise over geologically and authoritatively conveyed systems, and use a more heterogenous hardware. Handling can happen on data saved either in a file system - unstructured or in a database - organized. Map/Reduce can exploit domain of data, preparing it on or close to the storage resources with a specific end goal to reduce the length over which it must be transmitted. Jiaxing et al. proposes SUDO, an advancement system that reasons about data partition characteristics, working properties, and data shuffling. They contend that reasoning about data partition properties crosswise phases opens up chances to reduce extravagant data shuffling. For instance, in the event that we realize that data partitions from past computation phases as of now have alluring properties for the following phase, we have the method to stay away from superfluous data shuffling steps. The fundamental obstruction to reasoning about data partition properties crosswise over processing phases is the utilization of UDFs [4]. At the point when a UDF is viewed as a "black-box", which is typically the case, we must expect conservatively that all data partition characteristics are lost subsequent to applying