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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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    1. Wang, Jun & Zhu, Xiaoyan, 2021. "Joint optimization of condition-based maintenance and inventory control for a k-out-of-n:F system of multi-state degrading components," European Journal of Operational Research, Elsevier, vol. 290(2), pages 514-529.
    2. Debabrata Das & Nirmal Baran Hui & Vipul Jain, 2019. "Optimization of stochastic, (Q, R) inventory system in multi-product, multi-echelon, distributive supply chain," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(5), pages 405-418, October.
    3. Ghalebsaz-Jeddi, Babak & Shultes, Bruce C. & Haji, Rasoul, 2004. "A multi-product continuous review inventory system with stochastic demand, backorders, and a budget constraint," European Journal of Operational Research, Elsevier, vol. 158(2), pages 456-469, October.
    4. Tamjidzad, Shahrzad & Mirmohammadi, S. Hamid, 2015. "An optimal (r, Q) policy in a stochastic inventory system with all-units quantity discount and limited sharable resource," European Journal of Operational Research, Elsevier, vol. 247(1), pages 93-100.
    5. Rui L. Lopes & Gonçalo Figueira & Pedro Amorim & Bernardo Almada-Lobo, 2020. "Cooperative coevolution of expressions for (r,Q) inventory management policies using genetic programming," International Journal of Production Research, Taylor & Francis Journals, vol. 58(2), pages 509-525, January.
    6. Mehmood Khan & Matloub Hussain & Leopoldo Eduardo Cárdenas-Barrón, 2017. "Learning and screening errors in an EPQ inventory model for supply chains with stochastic lead time demands," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4816-4832, August.
    7. Roger C. Schroeder, 1974. "Managerial inventory formulations with stockout objectives and fiscal constraints," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 21(3), pages 375-388, September.
    8. Yanyan Yang & Shenle Pan & Eric Ballot, 2017. "Innovative vendor-managed inventory strategy exploiting interconnected logistics services in the Physical Internet," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2685-2702, May.
    9. Handfield, Robert & Warsing, Don & Wu, Xinmin, 2009. "(Q,r) Inventory policies in a fuzzy uncertain supply chain environment," European Journal of Operational Research, Elsevier, vol. 197(2), pages 609-619, September.
    10. Xie, Chen & Wang, Liangquan & Yang, Chaolin, 2021. "Robust inventory management with multiple supply sources," European Journal of Operational Research, Elsevier, vol. 295(2), pages 463-474.
    11. Tang, Shaolong & Wang, Wenjie & Cho, Stella & Yan, Hong, 2018. "Reducing emissions in transportation and inventory management: (R, Q) Policy with considerations of carbon reduction," European Journal of Operational Research, Elsevier, vol. 269(1), pages 327-340.
    12. Hon-Shiang Lau, 1997. "Simple formulas for the expected costs in the newsboy problem: An educational note," European Journal of Operational Research, Elsevier, vol. 100(3), pages 557-561, August.
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