Michal Forišek, Branislav Katreniak, Jana Katreniaková, Rastislav Královič, Richard Královič, Vladimír Koutný, Dana Pardubská, Tomáš Plachetka, Branislav Rovan
The paper investigates a version of the resource allocation problem arising in the wireless networking, namely in the OVSF code reallocation process. In this setting a complete binary tree of a given height $n$ is considered, together with a sequence of requests which have to be served in an online manner. The requests are of two types: an insertion request requires to allocate a complete subtree of a given height, and a deletion request frees a given allocated subtree. In order to serve an insertion request it might be necessary to move some already allocated subtrees to other locations in order to free a large enough subtree. We are interested in the worst case average number of such reallocations needed to serve a request.
It was proved in previous work that the competitive ratio of the optimal online algorithm solving this problem is between 1.5 and O(n). We partially answer the question about its exact value by giving an O(1)-competitive online algorithm.
Same model has been used in the context of memory management systems, and analyzed for the number of reallocations needed to serve a request in the worst case. In this setting, our result is a corresponding amortized analysis.
In this paper we address two problems, for which we present novel, efficient, algorithmic solutions. The first problem is motivated by practical situations and is concerned with the efficient estimation of the upload bandwidth of a machine, particularly in the context of a peer-to-peer content sharing and distribution application. The second problem is more of a theoretical nature and considers a constrained communication resource allocation situation.
Inspired by online ad allocation, we study online stochastic packing linear programs from theoretical and practical standpoints. We first present a near-optimal online algorithm for a general class of packing linear programs which model various online resource allocation problems including online variants of routing, ad allocations, generalized assignment, and combinatorial auctions. As our main theoretical result, we prove that a simple primal-dual training-based algorithm achieves a (1 - o(1))-approximation guarantee in the random order stochastic model. This is a significant improvement ove…
We show that the popular water-filling algorithm for maximizing the mutual information in parallel Gaussian channels is sub-modular. The sub-modularity of water-filling algorithm is then used to derive online basestation allocation algorithms, where mobile users are assigned to one of many possible basestations immediately and irrevocably upon arrival without knowing the future user information. The goal of the allocation is to maximize the sum-rate of the system under power allocation at each basestation. We present online algorithms with competitive ratio of at most 2 when compared to offlin…
We present a unified framework for designing and analyzing algorithms for online budgeted allocation problems (including online matching) and their generalization, the Online Generalized Assignment Problem (OnGAP). These problems have been intensively studied as models of how to allocate impressions for online advertising. In contrast to previous analyses of online budgeted allocation algorithms (the so-called "balance" or "water-filling" family of algorithms) our analysis is based on the method of randomized dual fitting, analogous to the recent analysis of the RANKING algorithm for online ma…
We consider the problem of online allocation (matching and assortments) of reusable resources where customers arrive sequentially in an adversarial fashion and allocated resources are used or rented for a stochastic duration that is drawn independently from known distributions. Focusing on the case of large inventory, we give an algorithm that is $(1-1/e)$ competitive for general usage distributions. At the heart of our result is the notion of a relaxed online algorithm that is only subjected to fluid approximations of the stochastic elements in the problem. The output of this algorithm serves…
In this paper, we study a general online linear programming problem whose formulation encompasses many practical dynamic resource allocation problems, including internet advertising display applications, revenue management, various routing, packing, and auction problems. We propose a model, which under mild assumptions, allows us to design near-optimal learning-based online algorithms that do not require the a priori knowledge about the total number of online requests to come, a first of its kind. We then consider two variants of the problem that relax the initial assumptions imposed on the pr…
For online resource allocation problems, we propose a new demand arrival model where the sequence of arrivals contains both an adversarial component and a stochastic one. Our model requires no demand forecasting; however, due to the presence of the stochastic component, we can partially predict future demand as the sequence of arrivals unfolds. Under the proposed model, we study the problem of the online allocation of a single resource to two types of customers, and design online algorithms that outperform existing ones. Our algorithms are adjustable to the relative size of the stochastic comp…
Southeast University（东南大学）；University of Victoria（维多利亚大学）；New Jersey Institute of Technology（新泽西理工学院）
With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation proce…
Design of {\it online algorithms} for assigning mobile users to basestations is considered with the objective of maximizing the sum-rate, when all users associated to any one basestation equally share each basestation's resources. Each user on its arrival reveals the rates it can obtain if connected to each of the basestations, and the problem is to assign each user to any one basestation irrevocably so that the sum-rate is maximized at the end of all user arrivals, without knowing the future user arrival or rate information or its statistics at each user arrival. Online algorithms with consta…
We study the performance of a best reply algorithm for online resource allocation problems with a diseconomy of scale. In an online resource allocation problem, we are given a set of resources and a set of requests that arrive in an online manner. Each request consists of a set of feasible allocations and an allocation is a set of resources. The total cost of an allocation vector is given by the sum of the resources' costs, where each resource's cost depends on the total load on the resource under the allocation vector. We analyze the natural online procedure where each request is allocated gr…
We study the following vertex-weighted online bipartite matching problem: $G(U, V, E)$ is a bipartite graph. The vertices in $U$ have weights and are known ahead of time, while the vertices in $V$ arrive online in an arbitrary order and have to be matched upon arrival. The goal is to maximize the sum of weights of the matched vertices in $U$. When all the weights are equal, this reduces to the classic \emph{online bipartite matching} problem for which Karp, Vazirani and Vazirani gave an optimal $\left(1-\frac{1}{e}\right)$-competitive algorithm in their seminal work~\cite{KVV90}. Our main resu…
We consider the following online allocation problem: Given a unit square S, and a sequence of numbers n_i between 0 and 1, with partial sum bounded by 1; at each step i, select a region C_i of previously unassigned area n_i in S. The objective is to make these regions compact in a distance-aware sense: minimize the maximum (normalized) average Manhattan distance between points from the same set C_i. Related location problems have received a considerable amount of attention; in particular, the problem of determining the "optimal shape of a city", i.e., allocating a single n_i has been studied. …
We study an online version of the max-min fair allocation problem for indivisible items. In this problem, items arrive one by one, and each item must be allocated irrevocably on arrival to one of $n$ agents, who have additive valuations for the items. Our goal is to make the least happy agent as happy as possible. In research on the topic of online allocation, this is a fundamental and natural problem. Our main result is to reveal the asymptotic competitive ratios of the problem for both the adversarial and i.i.d. input models. We design a polynomial-time deterministic algorithm that is asympt…
Modern Primal-Dual Frameworks for Prior-Free Online Resource Allocation
University of Chicago Booth School of Business（芝加哥大学布斯商学院）；Department of Industrial Engineering & Operations Research, University of California, Berkeley（加州大学伯克利分校工业工程与运筹学系）
School of Operations Research and Information Engineering, Cornell University（康奈尔大学运营研究与信息工程学院）
We develop a framework for designing simple and efficient policies for a family of online allocation and pricing problems, that includes online packing, budget-constrained probing, dynamic pricing, and online contextual bandits with knapsacks. In each case, we evaluate the performance of our policies in terms of their regret (i.e., additive gap) relative to an offline controller that is endowed with more information than the online controller. Our framework is based on Bellman Inequalities, which decompose the loss of an algorithm into two distinct sources of error: (1) arising from computatio…
Cainiao Network（菜鸟网络）；City University of Hong Kong（香港城市大学）；University of Science and Technology of China（中国科学技术大学）
In this paper, we investigate the online allocation problem of maximizing the overall revenue subject to both lower and upper bound constraints. Compared to the extensively studied online problems with only resource upper bounds, the two-sided constraints affect the prospects of resource consumption more severely. As a result, only limited violations of constraints or pessimistic competitive bounds could be guaranteed. To tackle the challenge, we define a measure of feasibility $ξ^*$ to evaluate the hardness of this problem, and estimate this measurement by an optimization routine with theoret…
