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Multi-stream (Q,r) model and optimization for data prefetching

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  • Zhu, Xiaoyan
  • Wang, Jun
  • Yuan, Qi
  • Zhang, Zhe

Abstract

Modern high-end computing systems, such as storage servers used in Youtube and Tiktok, serve large numbers of concurrent streams, each of which requires aggressive prefetching. This multi-stream prefetching problem, which strives to serve as many requests as possible from the memory cache and minimize response time, remains as an open challenge in computer science research. To address the efficient resource management for data prefetching, this paper introduces a novel method adopted from inventory management of multiple products in operations research. It proposes a unique constrained multi-stream (Q,r) model which simultaneously determines the prefetching degree (order quantity) Q and trigger distance (reorder point) r for each application stream, taking into account the distinct data request rates of the streams. The model has the objective of minimizing the cache miss level (backorder level), which determines the access latency, as well as constraints on the cache space (inventory space) and the total prefetching frequency (total order frequency). Specifically, the disk access time (lead time) is a function of both the prefetching degree and the total prefetching frequency, the latter of which represents the system load. We present the analytical properties of the model, provide numerical optimization examples, and conduct sensitivity analysis to further demonstrate the insights of this prefetching problem. Significantly, an empirical evaluation proves the effectiveness of the prefetching policy provided by our model.

Suggested Citation

  • Zhu, Xiaoyan & Wang, Jun & Yuan, Qi & Zhang, Zhe, 2022. "Multi-stream (Q,r) model and optimization for data prefetching," European Journal of Operational Research, Elsevier, vol. 302(1), pages 130-143.
    • Handle: RePEc:eee:ejores:v:302:y:2022:i:1:p:130-143
    DOI: 10.1016/j.ejor.2021.12.007
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    References listed on IDEAS

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