MapReduce is presented by Dean and Ghemawt [1] as a programming model for parallelizing the computations of data intensive application across a cluster. It has two primitives, map and reduce. Map computes a set of intermediate key/value pairs for each record, then reduce is applied to all the values with similar keys so it may forms a smaller set of values. Several implementation of MapReduce have been implemented according to the hardware infrastructure. Phoenix [2], as an example, is a shared-memory implementation of MapReduce. Apache Hadoop is another example implemented for running applications on large cluster built of commodity hardware [3]. MATE-CG [4] is another map-reduce-like framework, implemented appropriately for programming the CPU+GPU clusters. MATE-CG aims the acceleration of map-reduce applications on parallel heterogeneous environments, especially CPU+GPU clusters. It enables accelerating different types of applications, supporting three schemes. Based on its dataset, an application could be accelerated by one of the CPU-only, GPU-only and CPU-n-GPU schemes, to get the best performance. Appropriate APIs allow the implementer to specify reduction functions for CPU and GPU. GPU reduction is implemented by CUDA kernels to perform on GPUs. Also the user defines application specific partitioner and splitter functions. The former is used to partition the dataset among the computing nodes and the latter splits the data blocks into smaller chunks to be processed on the CPUs and GPUs. In the runtime system, the CPU is used to execute partitioning, scheduling, etc. GPU is mainly responsible for accelerating the computation. Based on the user defined partitioning parameter, data is distributed among the nodes. After comple... ... middle of paper ... ...us tasks. A user defined splitting is being performed on the input to derive the tasks. Taking into account the number of cores or a user defined value, each node requests for some tasks. A node does not request a task before it has completed the previous tasks. In order to be fault tolerant, MARLA uses a specific fault tolerance scheme. Failed tasks are submitted to another worker. If the task succeeds with another worker, the faulty node is given a strike. Nodes with three strikes are considered as faulty node and not allowed to participate in the processing. This scheme avoids expensive data relocation used by many other implementations to achieve fault tolerance. To enable the mentioned specifications, MARLA implemented with three main parts, the Splitter for I/O management, the TaskController for concurrency management and the FaultTracker for fault tolerance.
A distributed system is a collection of independent computers (nodes) that appears to its users as a single coherent
In the past number of years data has grown exponentially. This growth in data has created problems that and a race to better monitor, monetize, and organize it. Oracle is in the forefront of helping companies from different industries better handle this growing concern with data. Oracle provides analytical platforms and an architectural platform to provide solutions to companies. Furthermore, Oracle has provided software such as Oracle Business Intelligence Suite and Oracle Exalytics that have been instrumental in organizing and analyzing the phenomenon known as Big Data.
One of the biggest problems that affect everyone is data aggregation. The more the technology develop, the powerful and dangerous it gets. Today there are many companies that aggregate a lot of information about us. Those companies gathering our data from different sources, which create a detailed record about us. Since all services have been computerized whether it is handled directly or indirectly through computers, there is no way to hide your information. We used computers, because they are faster, better, and accurate more that any human being. It solved many problems; however, it created new ones. Data does not means anything if it stands alone, because it is only recoded facts and figure, yet when it organized and sorted, it become information. These transformed information. Data aggregation raises many questions such as, who is benefiting from data aggregation? What is the impact on us (the users)? In this paper I will discuses data aggregation and the ethics and legal issues that affect us.
Now we can say that an enterprise data warehouse could be used to manage the big data and the extreme workloads but we would find that often it is more efficient to preprocess the data before storing it in the warehouse. Let’s consider an example even data from hardware sensors have a lar...
Big Data has become very popular now-a-days, because of the unlimited data coming from various sources and is difficult to handle by data base management systems. As the information that is gathered from different sources is very large and complex, it has become complex and not easy to process all the information through traditional way of data processing. And here it comes big data into a picture. It’s one of the most popular technologies which have its applications in almost every field. This paper explores the value of real time big data in education, and health care. “Big Data is when your data becomes so large that you have to start innovating how to collect, store, organize, analyze and share it”. (Gobble, MaryAnne M., 2013)
transform service design and delivery so that personalised and streamlined services, that accurately and specifically meet individual’s needs, can be delivered to them in a timely manner. Improved service delivery could cover areas as diverse as remote medical diagnostics, major infrastructure management, personalised social security benefits delivery, improved first responder and emergency services, reduction of fraudulent or criminal activity across both government and private sectors, and the development of innovative new services as the growth and availability of Public Sector Information (PSI) becomes more prevalent. The private sector holds huge amounts of data about its customers and in many cases leads the way in how this data is
35 Meng Xiaofeng and Ci Xiang, 2013 : Big Data Management: Concepts,Techniques and Challenges pp 4-6
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
Because of the robust Linux programmer community, several “flavour’s” of Linux (known as “vendors”) are available, and each is specialized in a slightly different way. This robust operating system is being widely adopted by IT professionals in growing businesses because of its high quality, reliability, and price.
This white paper identifies some of the considerations and techniques which can significantly improve the performance of the systems handling large amounts of data.
It simplifies the storage and processing of large amounts of data, eases the deployment and operation of large-scale global products and services, and automates much of the administration of large-scale clusters of computers.
The Database Management System (DBMS) is software that enables the users to define, create, maintain and control the access to the database. It is a software that interact with the user’s applications programs and it database. Meanwhile, information retrieval system is a system that involved the activity that the systems obtain the information. The obtaining information action need the information from it resources.
Frameworks with monstrous amounts of processors for the most part take one of two ways: In one methodology (e.g., in conveyed processing), countless machines (e.g., laptops) appropriated over a system (e.g., the Internet) dedicate some or the sum of their time to tackling a typical issue; every individual workstation (customer) accepts and finishes a lot of people little assignments, reporting the outcomes to a focal server which incorporates the undertaking effects from all the customers into the general solution.[4][5] In an alternate approach, countless processors are set in close nearness to one another( (e.g. in a machine bunch); this spares significant time moving information around and makes it feasible for the processors to cooperate (instead of on particular errands), for instance in cross section and hypercube architectures.