Description And Analysis Of The Run Time Pattern

However, this is inconsistent with what we have been taught and with what is written in the textbook. Since the textbook is a more reliable source of information, I must conclude that the reason for this difference in results may be attributed to error on the part of the students conducting the experiment.

This test was used in order to determine if there were too many or too few runs in a series of data. After conducting the runs test it produced a z-value of -5.9123, which indicates the amount of standard errors of the identified number of runs below the expected number of runs. The p-value indicates how extreme the z value is and with a p-value (0.0001) which is less than .05 or .1 the null hypothesis of randomness is rejected (Figure 1).

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.

This paper proposes backup task mechanism to improve the straggler tasks which are the final set of MapReduce tasks that take unusually longer time to complete. The simplified programming model proposed in this paper opened up the parallel computation field to general purpose programmers. This paper served as the foundation for the open source distributing computing software – Hadoop as well as tackles various common error scenarios that are encountered in a compute cluster and provides fault tolerance solution on a framework

Which of the following conclusions is most valid, given the results of experiment 1? 6-8.2

The experiment is repeated twice and an average is obtained. This ensures the consistency of the measurement as the reading is triplicated, thus reducing anomalies. It can be seen that the reading obtained were around the same value. This proves that the results are reliable.

Monitored the network congestion in order to achieve the optimal performance out of a network infrastructure.

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.

The Hadoop employs MapReduce paradigm of computing which targets batch-job processing. It does not directly support the real time query execution i.e OLTP. Hadoop can be integrated with Apache Hive that supports HiveQL query language which supports query firing, but still not provide OLTP tasks (such as updates and deletion at row level) and has late response time (in minutes) due to absence of pipeline

An important characteristic of Hadoop is the partitioning of data and computation across many (thousands) of hosts, and the execution of application computations in parallel close to their data. A Hadoop cluster scales computation capacity, storage capacity and I/O bandwidth by simply adding commodity servers. Hadoop clusters at Yahoo! span 40,000 servers, and store 40 petabytes of application data, with the largest cluster

The k value in run 2 is nearly double that of run 6. This is directly related to the ∆t found for each, with ∆t2 being half that of ∆t6. This is a result of the H2O being present in run 6 while (NH4)2SO4 being present in run 2.

This experiment shall be repeated twice or more to enhance accuracy of the results obtained. Besides detecting systematic errors, this experiment would aid on the technique and understandings to the correct use of these equipments.

Cost reduction: Big data technologies such as Hadoop and cloud based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify and implement more efficient ways of doing business.

Abstract - Hadoop Distributed File System, a Java based file system provides reliable and scalable storage for data. It is the key component to understand how a Hadoop cluster can be scaled over hundreds or thousands of nodes. The large amounts of data in Hadoop cluster is broken down to smaller blocks and distributed across small inexpensive servers using HDFS. Now, MapReduce functions are executed on these smaller blocks of data thus providing the scalability needed for big data processing. In this paper I will discuss in detail on Hadoop, the architecture of HDFS, how it functions and the advantages.

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.