V. DATA ANALYSIS IN THE CLOUD
In this section we descus the expected properties of a system designed for performing data analysis at the cloud environment and how parallel database systems and MapReduce-based systems achieve these properties.
Expected properties of a system designed for performing data analysis at cloud:
• Performance
Performance is the primary characteristic of database systems that can use to select best solution for the system.High performance relate with quality, amount and depth of analysis. High performance helps to reduce cost.Upgrading to a quicker software package will permit an organization avoid adding further nodes to application continues to scale.
• Fault Tolerance.
In transactional workloads fault tolerant means that DBMS can recover from a failure without losing any data. In the distributed databases fault tolerances means that successfully commit transactions and make progress even in the worker node failures. For read-only queries in analytical workloads, query doesn’t have to be restarted if a case of one node’s query fails.In cloud there is a high failure rate. It can happen in single node failure during long query processing. • Ability to run in a heterogeneous environment
Due to hardware failures in the system nodes in cloud not act as homogeneous. When the work is equally divided among all nodes, time takes to complete the task should be equal to time that needed slowest node needed to complete its portion of work. Because its
There are a lot of system requirements and assumptions made in this paper. The query model is assumed to have simple read and write operations to data nodes that are identified uniquely by a key. This assumption is made based on the fact that most of the amazon applications does not require a relational schema and can work with simple queries.
This paper discussed the Distributed database systems which are systems that have their data distributed and replicated over several locations; unlike the centralized data base system, where one copy of the data is stored in one location.
Byzantine failure is very common fault in cloud servers, in which a storage server can fail in arbitrary ways. On occurrence of a byzantine failure system responds in an unpredictable way. At the point when
Hadoop is an open source framework that could be very resourceful in data processing of the complex data systems, and has been reverently used in the recent past for query processing in the complex databases that contains millions of records. The major advantage of Hadoop is that it clusters the entire records to few blocks and the query is run on each cluster and the compiled information is displayed in effective terms.
Data acquisition is the step where data from diverse sources enters the Big Data system. The performance of this component directly impacts how much data a Big Data system can receive at any given point of time. Some of the logical steps involved in
Overview: This section describes the purpose of this research, the rationales for undertaking it and the background knowledge that is relevant to this research. It provides the research background that describes the polemic in the Database Management Systems (DBMS); research question in regards of performance of MySQL (non cluster) and Hadoop; the research aim; the research objectives; and the research outline.
these metrics are geared toward a large SQL server, with considerable resources needed to facilitate a high performance database. Due to the disk utilization, they also recommend primarily a RAID 1+0 configuration or other high performance storage configurations for both the database files and subsystems alike (SolarWinds, n.d.). in summary, databases require considerable resources among all but network throughput to provide timely querying to many users.
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.
It is essential for database to perform as maximized as possible to enable the largest possibilities to process workloads. However, performance bottlenecks would be in a range of common problems as a virtue towards several factors. Major influences to performance in databases are workload, throughput and resources. Workload defines how heavy system commands are in a given time which would largely endure poor performance that also added factor to consider of the overall capabilities of the computer to process all data, thus speed and efficiency define a huge role of a throughput. Further, factor of resources, which
As a process, performance measurement is not just collecting data associated with a predefined performance goal or standard. Performance measurement is an overall management system involving prevention and detection aimed at achieving conformance of the database management process to an established target. Additionally, it is concerned with process optimization through increased efficiency and effectiveness of the product, solution or service. These actions occur in a continuous cycle, allowing options for expansion and improvement of the process as better techniques are discovered and implemented.
In analysis, the data will be studied with the aim of trying to understand the risks in order to mitigate the risks or to minimize the existing risks in the cloud computing system. The objective is to extract the useful information from the analysis of the data collected during the course of the study, which will help in creating resolutions to the issues that surface regarding cloud computing security.
Todays appications are evolved from standalone to client-server models and ultimatetly to the cloud based elastic applications. Performance can directly affects the business and revenue. Its always been dificult to see whats going on inside the system
Abstract—Parallel databases are the high performance databases in RDBMS world that can used for setting up data intensive enterprise data warehouse but they lack scalability whereas, MapReduce paradigm highly supports scalability, nevertheless cannot perform as good as parallel databases. Deriving an architectural hybrid model of best of both worlds that can support high performance and scalability at the same time.
Precise offered the software that helped its clients to manage the performance of their information technology (IT) systems. Precise is in the performance management and availability market. Its products are designed to manage the performance applications utilizing Oracle database. The company had focus on a small range of core products but provided users high quality that promised. Precise offered the software license and services. The main products were insight products, SQL and Presto. Precise/SQL accounted for 86% of all Precise’s software licensing fees. The company has strong trained account reps with very strong relationships with key clients. End-to-end response time is extremely important to ensure the system ran efficiently and effectively. All of the available products focused on the performance of each of the components of the system. The sales cycle is 6 to 12 months on average. Precise realized from the feedback of its consumers that they should provide right solutions to its clients rather than the products. However, a full-functionality end-to-end performance tool needs a long time to be developed. It’s going to take six and nine months to get a basic product with purely monitoring only. The fully
The aim of this paper is to explore different aspects of the MapReduce framework. The primary focus will be given on how MapReduce framework follows the principles and techniques of distributed and parallel programming in the context of concurrent, parallel and distributed computing. In the following sections of the report, there will be a brief introduction of the MapReduce platform and how it is related to distributed and parallel computing. Following that, the discussion will be on the phases and job life cycle of MapReduce-based programming, the functionalities of the different components of a MapReduce job, implementation of MapReduce and the challenges in the implementations. Hence, the paper covers different aspects of the methodology, implementations, issues and examples of implementation of the MapReduce framework.