• SafePath

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    The need for real-time and large-scale data processing has led to the development of frameworks for distributed stream processing in clouds. To provide fast, scalable, and fault tolerant stream processing, recent Distributed Stream Processing Systems (DSPS) have proposed to treat streaming workloads as a series of batch jobs, instead of a series of records. Batch-based stream processing systems could process data at high rate, however, it also leads to large end-to-end latency. To minimize the end-to-end latency for batched processing system (Apache Spark Streaming), we developed an online algorithm, DyBBS that stands for Dynamic Block and Bath Sizing, which dynamically adapts block and batch interval based on the workload and operating conditions upon Spark Streaming.


    Big data processing and sharing in collaborative cloud-edge environment.
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    A Citizen-Centered and Trustworthy Data Sharing Platform for Connected Communities.
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