The MapReduce framework manages Big Data sets by splitting the large datasets into a set of distributed blocks and processes them in parallel. Data compression and in-memory file systems are widely used methods in Big Data processing to reduce resource-intensive I/O operations and improve I/O rate correspondingly. The article presents a performance-efficient modular and configurable decision-making robust service relying on data compression and in-memory data storage indicators. The service consists of Recommendation and Prediction modules, predicts the execution time of a given job based on metrics, and recommends the best configuration parameters to improve Hadoop and Spark frameworks' performance. Several CPU and data-intensive applications and micro-benchmarks have been evaluated to improve the performance, including Log Analyzer, WordCount, and K-Means.
The optimization of large-scale data sets depends on the technologies and methods used. The MapReduce model, implemented on Apache Hadoop or Spark, allows splitting large data sets into a set of blocks distributed on several machines. Data compression reduces data size and transfer time between disks and memory but requires additional processing. Therefore, finding an optimal tradeoff is a challenge, as a high compression factor may underload Input/Output but overload the processor. The paper aims to present a system enabling the selection of the compression tools and tuning the compression factor to reach the best performance in Apache Hadoop and Spark infrastructures based on simulation analyzes.
Earth observation (EO) satellite data is essential to environmental monitoring. At a national and regional level, the open data cubes harness the power of satellite data by providing application programming interfaces and services to the end-users. The volume and the complexity of satellite observations are increasing, demanding novel approaches for data storing, managing, and processing. High-performance computing (HPC) and cloud platforms may improve Big EO data processing performance. However, it is necessary to consider several vital aspects for efficient and flexible EO data processing, such as the interoperability from cloud-HPC and EO data repositories, automatic provisioning and scaling of cloud-HPC resources, cost-effectiveness, support of new EO data formats and open-source packages, or linkage with data cube platforms. The article proposes a scalable EO data processing platform interoperable from cloud-HPC and EO data repositories. The platform enables linking any data repository supporting web coverage service or SpatioTemporal Asset Catalog Application Programming Interfaces (STAC-API), and any cloud or HPC resource supporting scheduling system API for providing access to the cluster backends.
High-performance computing is a good choice for handling Big Earth Observation data, allowing the processing of the data in a distributed and performance-efficient way using in-memory computing frameworks. The data compression technique reduces the amount of storage and network transfer time and improves processing performance. The article aims to investigate the effectiveness of widely used distributed data processing frameworks in conjunction with lossless data compression techniques, to find the optimal compression method and processing framework for specific earth observation workflows. Normalized Difference Vegetation Index has been evaluated for the territory of Armenia, obtaining data from the Sentinel satellite and considering the supported compression methods to compare the performance of in-memory Dask and Spark frameworks. Experiments show that the Zstandard compression method and the Dask framework are the best choices for such workflows.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.