Beyond Monotone Delays for Multi-Level Aggregation

Abstract

In the online Multi-Level Aggregation Problem (MLAP), requests arrive over time and are associated with nodes of a given weighted rooted tree of depth D. Each request must eventually be served by performing a service. Serving a request consists of selecting a rooted subtree that contains the request’s node, incurring a service cost equal to the total weight of the selected subtree. To reduce service costs, multiple requests may be served simultaneously by selecting a single rooted subtree that spans all of them. In addition, each request is associated with a penalty function that specifies the cost incurred when the request is served at a particular time. The objective is to minimize the total cost, consisting of both service costs and penalty costs.
Most previous work on MLAP assumes monotone non-decreasing penalty functions, commonly referred to as delay functions. Only very recent results consider penalty functions that initially decrease and subsequently increase, and even then only for the special cases of depths D = 1 and D = 2, namely the Joint Replenishment Problem (JRP). 
In this work, we extend previous results in two ways. First, we allow arbitrary penalty functions, which may decrease and increase multiple times. Second, we study the general MLAP with arbitrary tree depth D under these arbitrary penalty functions. We present a randomized algorithm which is O(D log n log(nDW))-competitive, where W is the maximum service window among all penalty functions after normalizing the Lipschitz parameter of each penalty function to be 1 and normalizing the minimum positive edge weight incident to the root to be 1; and n is the number of requests. We note that our algorithm runs in polynomial-time, and even for D = 1 the problem admits hardness of approximation of Ω(log n) for polynomial time algorithms.
As mentioned above, prior to our work even for trees of depth D = 1,2, non-monotone penalty functions have been studied only in special cases of functions that decrease and increase only once. In contrast, for such trees we obtain O(log n log (nW))-competitive algorithms for arbitrary non-monotone penalty functions.