## 摘要

Balls-and-bins games have been a successful tool for modeling load balancing problems. In this paper, we study a new scenario, which we call the ball-recycling game, defined as follows: Throw m balls into n bins i.i.d. according to a given probability distribution p. Then, at each time step, pick a non-empty bin and recycle its balls: take the balls from the selected bin and re-throw them according to p. This balls-and-bins game closely models memory-access heuristics in databases. The goal is to have a bin-picking method that maximizes the recycling rate, defined to be the expected number of balls recycled per step in the stationary distribution. We study two natural strategies for ball recycling: Fullest Bin, which greedily picks the bin with the maximum number of balls, and Random Ball, which picks a ball at random and recycles its bin. We show that for general p, Random Ball is Θ(1)-optimal, whereas Fullest Bin can be pessimal. However, when p = u, the uniform distribution, Fullest Bin is optimal to within an additive constant.

原文 | English |
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頁面 | 2527-2546 |

頁數 | 20 |

DOIs | |

出版狀態 | Published - 1 1月 2019 |

事件 | 30th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2019 - San Diego, United States 持續時間: 6 1月 2019 → 9 1月 2019 |

### Conference

Conference | 30th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2019 |
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國家/地區 | United States |

城市 | San Diego |

期間 | 6/01/19 → 9/01/19 |