Big Data Processing in Cloud Computing Environments

Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Massive growth in the scale of data or big data generated through cloud computing has been observed. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis. This paper introduces several Big Data processing techniques from system and application aspects. First, from the view of cloud data management and Big Data processing mechanisms, we present the key issues of Big Data. Following, we present the cloud computing for Big Data and related work. Furthermore, we also discuss, Big Data moving to the cloud. Finally, we present the conclusion and future work

A Survey on Big Data and Cloud Computing

In the information age, analyzing and extracting the knowledge from the data is a challenging task since the data are being accumulated massively from various sources and sectors. These massively accumulated data is known as big data since it possess the characteristics such as high volume, different variety and high velocity. Processing these big data using the normal work station is quiet complex since it is saturated with the vertical scalability. Therefore, processing the big data is a challenging task. Hence, the cloud computing arrives for handling massive data for storing and analyzing them to obtain the knowledge and make decisions to improve the productivity and the services. Therefore, conducting the study on the big data and the cloud computing is important to promote the research and development activities in the field of the big data and the cloud computing. Therefore, this paper presents a survey on the big data and cloud computing

BIG DATA AND CLOUD COMPUTING

Big Data may be a data analysis methodology enabled by recent advances in technologies and architecture. However, big data leads to enormous commitment of hardware and processing resources, which prevents the adoption costs of massive data technology for small and medium-sized businesses.Cloud computing offers the promise of massive data implementation to small and medium sized businesses.Big processing is performed through a programming paradigm referred to as MapReduce. Typically, implementation of the MapReduce paradigm requires networked attached storage and multiprocessing . The computing needs of MapReduce programming are often beyond what small and medium sized business are ready to commit. Cloud computing is on-demand network access to computing resources, provided by an outdoor entity. Common deployment models for cloud computing include platform as a service (PaaS), software as a service (SaaS), infrastructure as a service (IaaS), and hardware as a service (HaaS). The three sorts of cloud computing are the general public cloud, the private cloud, and therefore the hybrid cloud. A public cloud is that the pay- as-you-go services. a personal cloud is internal data center of a business not available to the overall public but supported cloud structure. The hybrid cloud may be a combination of the general public cloud and personal cloud. Three major reasons for little to medium sized businesses to use cloud computing for giant data technology implementation are hardware cost reduction, processing cost reduction, and skill to check the worth of massive data. the main concerns regarding cloud computing are security and loss ofcontrol.&nbsp

Optimal Control of Applications for Hybrid Cloud Services

Development of cloud computing enables to move Big Data in the hybrid cloud services. This requires research of all processing systems and data structures for provide QoS. Due to the fact that there are many bottlenecks requires monitoring and control system when performing a query. The models and optimization criteria for the design of systems in a hybrid cloud infrastructures are created. In this article suggested approaches and the results of this build.Comment: 4 pages, Proc. conf. (not published). arXiv admin note: text overlap with arXiv:1402.146

Optimized Error Detection in Cloud User for Networking Services

Big sensor data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity it is difficult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a flexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efficient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN