Showing 4 open source projects for "cloud computing projects"

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  • 1
    ElasticJob

    ElasticJob

    Distributed scheduled job framework

    ElasticJob is a distributed scheduling solution consisting of two separate projects, ElasticJob-Lite and ElasticJob-Cloud. ElasticJob-Lite is a lightweight, decentralized solution that provides distributed task sharding services. ElasticJob-Cloud uses Mesos to manage and isolate resources. It uses a unified job API for each project. Developers only need code one time and can deploy at will. Support job sharding and high availability in distributed system.
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  • 2
    Nebula Graph

    Nebula Graph

    A distributed, fast open-source graph database

    The graph database built for super large-scale graphs with milliseconds of latency. Optimized SUBGRAPH and FIND PATH for better performance. Optimized query paths to reduce redundant paths and time complexity. Optimized the method to get properties for better performance of MATCH statements. Nebula Graph adopts the Apache 2.0 license, one of the most permissive free software licenses in the world. Free as in freedom, because, under the Apache 2.0 license, you can use, copy, modify and...
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  • 3
    LEACrypt

    LEACrypt

    TTAK.KO-12.0223 Lightweight Encryption Algorithm Tool

    The Lightweight Encryption Algorithm (also known as LEA) is a 128-bit block cipher developed by South Korea in 2013 to provide confidentiality in high-speed environments such as big data and cloud computing, as well as lightweight environments such as IoT devices and mobile devices. LEA is one of the cryptographic algorithms approved by the Korean Cryptographic Module Validation Program (KCMVP) and is the national standard of Republic of Korea (KS X 3246). LEA is included in the ISO/IEC 29192-2:2019 standard (Information security - Lightweight cryptography - Part 2: Block ciphers). ...
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  • 4

    MarDRe

    MapReduce-based tool to remove duplicate DNA reads

    ...MarDRe is the Big Data counterpart of ParDRe (link above), which employs HPC technologies (i.e., hybrid MPI/multithreading) to reduce runtime on multicore systems. Instead, MarDRe takes advantage of the MapReduce programming model to significantly improve ParDRe performance on distributed systems, especially on cloud-based infrastructures. Written in pure Java to maximize cross-platform compatibility, MarDRe is built upon the open-source Apache Hadoop project, the most popular distributed computing framework for Big Data processing.
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