Salient Store: Enabling Smart Storage for Continuous Learning Edge Servers
<|reference_start|>Salient Store: Enabling Smart Storage for Continuous Learning Edge Servers: As continuous learning based video analytics continue to evolve, the role of efficient edge servers in efficiently managing vast and dynamic datasets is becoming increasingly crucial. Unlike their compute architecture, storage and archival system for these edge servers has often been under-emphasized. This is unfortunate as they contribute significantly to the data management and data movement, especially in a emerging complute landscape where date storage and data protection has become one of the key concerns. To mitigate this, we propose Salient Store that specifically focuses on the integration of Computational Storage Devices (CSDs) into edge servers to enhance data processing and management, particularly in continuous learning scenarios, prevalent in fields such as autonomous driving and urban mobility. Our research, gos beyond the compute domain, and identifies the gaps in current storage system designs. We proposes a framework that aligns more closely with the growing data demands. We present a detailed analysis of data movement challenges within the archival workflows and demonstrate how the strategic integration of CSDs can significantly optimize data compression, encryption, as well as other data management tasks, to improve overall system performance. By leveraging the parallel processing capabilities of FPGAs and the high internal bandwidth of SSDs, Salient Store reduces the communication latency and data volume by ~6.2x and ~6.1x, respectively. This paper provides a comprehensive overview of the potential of CSDs to revolutionize storage, making them not just data repositories but active participants in the computational process.<|reference_end|>
@article{mishra2024salient,
    title={Salient Store: Enabling Smart Storage for Continuous Learning Edge
  Servers},
    author={Cyan Subhra Mishra, Deeksha Chaudhary, Jack Sampson, Mahmut Taylan
  Knademir, Chita Das},
    journal={arXiv preprint arXiv:2410.05435},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05435},
    primaryClass={cs.AR}
}