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SPSC: Stream Processing Framework Atop Serverless Computing for Industrial Big Data.

Zinuo Cai, Zebin Chen, Xinglei Chen

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    This summary is machine-generated.

    We introduce SPSC, a novel serverless stream computing framework. SPSC enhances big data processing efficiency by using stateless Lambda functions, outperforming traditional systems like Apache Flink.

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    Area of Science:

    • Computer Science
    • Data Engineering
    • Distributed Systems

    Background:

    • The increasing volume of data from smart manufacturing and information technologies necessitates efficient processing.
    • Existing distributed stream processing systems (e.g., Apache Flink, Spark) face challenges with resource underutilization and high latency.
    • Big data applications require scalable and performant solutions.

    Purpose of the Study:

    • To propose SPSC, a serverless-based stream computing framework designed to address the limitations of current big data processing systems.
    • To achieve efficient task and data parallelism in stream processing.
    • To evaluate the performance of the SPSC framework.

    Main Methods:

    • Developed SPSC, a serverless framework utilizing atomic streams and stateless Lambda functions as operators.
    • Implemented a prototype on Amazon Web Services (AWS) using AWS Lambda, AWS Simple Queue Service (SQS), and AWS DynamoDB.
    • Discretized events into atomic streams and employed context-irrelevant Lambda functions for parallel processing.

    Main Results:

    • The SPSC framework demonstrated superior performance compared to Alibaba's real-time computing Flink version.
    • SPSC achieved a 10.12% performance improvement with comparable overhead.
    • The serverless approach facilitated inherent data parallelism and task parallelism.

    Conclusions:

    • SPSC offers an efficient and performant alternative for big data stream processing in smart manufacturing and related fields.
    • Serverless architectures can effectively address challenges of resource underutilization and latency in big data scenarios.
    • The proposed framework shows significant potential for real-time data processing applications.