Tight Bounds for Clairvoyant Dynamic Bin Packing

In this article, we focus on the Clairvoyant Dynamic Bin Packing (DBP) problem, which extends the Classical Online Bin Packing problem in that items arrive and depart over time and the departure time of an item is known upon its arrival. The problem naturally arises when handling cloud-based networks. We focus specifically on the MinUsageTime objective function, which aims to minimize the overall usage time of all bins that are opened during the packing process. Earlier work has shown a O(log mu/ log log mu) upper bound on the algorithm's competitiveness, where mu is defined as the ratio between the maximal and minimal durations of all items. We improve the upper bound by giving a O(root log mu)-competitive algorithm. We then provide a matching lower bound of Omega(root log mu) on the competitive ratio of any online algorithm, thus closing the gap with regard to this problem. We then focus on what we call the class of aligned inputs and give a O(log log mu)-competitive algorithm for this case, beating the lower bound of the general case by an exponential factor. Surprisingly enough, the analysis of our algorithm that we present is closely related to various properties of binary strings.