Dynamic Bin Packing for On-Demand Cloud Resource Allocation

Abstract

Dynamic Bin Packing (DBP) is a variant of classical bin packing, which assumes that items may arrive and depart at arbitrary times. Existing works on DBP generally aim to minimize the maximum number of bins ever used in the packing. In this paper, we consider a new version of the DBP problem, namely, the MinTotal DBP problem which targets at minimizing the total cost of the bins used over time. It is motivated by the request dispatching problem arising from cloud gaming systems. We analyze the competitive ratios of the modified versions of the commonly used First Fit, Best Fit, and Any Fit packing (the family of packing algorithms that open a new bin only when no currently open bin can accommodate the item to be packed) algorithms for the MinTotal DBP problem. We show that the competitive ratio of Any Fit packing cannot be better than $\mu +1$ , where $\mu$ is the ratio of the maximum item duration to the minimum item duration. The competitive ratio of Best Fit packing is not bounded for any given $\mu$ . For First Fit packing, if all the item sizes are smaller than $\frac{1}{\beta }$ of the bin capacity ( $\beta >1$ is a constant), the competitive ratio has an upper bound of $\frac{\beta }{\beta -1}\cdot \mu + \frac{3\beta }{\beta -1}+1$ . For the general case, the competitive ratio of First Fit packing has an upper bound of $2\mu + 7$ . We also propose a Hybrid First Fit packing algorithm that can achieve a competitive ratio no larger than $\frac{5}{4}\mu + \frac{19}{4}$ when $\mu$ is not known and can achieve a competitive ratio no larger than $\mu + 5$ when $\mu$ is known.