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Online Stochastic Bin Packing

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Motivated by the problem of packing Virtual Machines on physical servers in the cloud, we study the problem of one-dimensional online stochastic bin packing. Items with sizes i.i.d. from an unknown distribution with integral support arrive as a stream and must be packed on arrival in bins of size B, also an integer. The size of an item is known when it arrives and the goal is to minimize the waste, defined to be the total unused space in non-empty bins. While there are many heuristics for online stochastic bin packing, all such heuristics are either optimal for only certain classes of item size distributions, or rely on learning the distribution. The state-of-the-art Sum of Squares heuristic (Csirik et al.) obtains sublinear (in number of items seen) waste for distributions where the expected waste for the optimal offline algorithm is sublinear, but has a constant factor larger waste for distributions with linear waste under OPT. Csirik et al. solved this problem by learning the distribution and solving an LP to inject phantom jobs in the arrival stream. We present two distribution-agnostic bin packing heuristics that achieve additive O(sqrt{n}) waste compared to OPT for all distributions. Our algorithms are gradient descent on suitably defined Lagrangian relaxations of the bin packing Linear Program. The first algorithm is very similar to the SS algorithm, but conceptually packs the bins top-down instead of bottom-up. This motivates our second heuristic that uses a different Lagrangian relaxation to pack bins bottom-up. Next, we consider the more general problem of online stochastic bin packing with item departures where the time requirement of an item is only revealed when the item departs. Our algorithms extend as is to the case of item departures. We also briefly revisit the Best Fit heuristic which has not been studied in the scenario of item departures yet.


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