Online Combinatorial Auctions Research Articles

Design of online combinatorial auction mechanism for urban land transfer.

With the wide application of the "Tender, Auction, and Listing" system for land in China, it is of paramount importance to establish a sound land auction mechanism so as to avoid unreasonable land allocation. However, existing studies only focus on developers who are merely concerned about land combinations and offline auctions with low efficiency and high land unsold rate. To further improve land auction mechanism, we modeled the problem of land allocation as an online combinatorial auction. On this basis, we defined a land correction coefficient and designed an optimal online combinatorial auction mechanism that enabled developers to submit multiple combinations of land. Our designed mechanism proved to satisfy both incentive compatibility (IC) and individual rationality (IR), which can prevent developers from lying in the auction while winning higher revenue, reducing land unsold rate, increasing developer turnover rate and promoting the stability of land resource allocation rate. Therefore, online combinatorial auction mechanism, both applicable and efficient, is a practical solution for improving land auctions in China.

Solving the winner determination problem with discounted bids in transportation auctions

AbstractContinuing advances in modern technologies have transformed the procedure of transportation procurement through auctions in supply chain management (SCM). This study examines the online combinatorial auction (CA), which serves customers placed at the nodes of a transportation network, with particular consideration given to carbon emissions. The CA mechanism allows early shipments of the carriers to improve their load consolidation and reduce their repositioning trips. Sustainability and carbon emissions are considered by prioritizing the carrier’s carbon reduction performances. Two models are examined under the carbon emission regulations (Carbon tax and Cap-and-offset) to choose the winners in the CA. Our aim is to minimize the cost of transportation procurement and reduce carbon emissions by incorporating the green reputation-based winner determination problem within the procurement model. Computational experiments reveal the positive impact of prioritization and discounted offers in reducing both transportation costs and the number of empty trips. Indeed, our results show the introduction of the discounted bids allows a reduction of about 2% in the transportation cost for the shipper and 24% of empty movements, on average, for the carriers.

A Cooperative Defense Framework Against Application-Level DDoS Attacks on Mobile Edge Computing Services

Mobile edge computing (MEC), extending computing services from cloud to edge, is recognized as one of key pillars to facilitate real-time services and tackle backhaul bottleneck. However, it is not economically efficient to attach intensive security appliances to every MEC node to defend application-level DDoS attacks and ensure the availability of services. Thus, we explore the elasticity of security defense among MEC nodes by proposing a COoperative DEfense ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CODE</i> ) framework for MEC, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CODE4MEC</i> . CODE4MEC aims to adapt to traffic changes by coordinating container-carried defensive resources among cooperative MEC nodes in an automatic way. Towards this aim, we propose four control plane functions to enable a life-cycle management for CODE4MEC, namely, CODE triggering, scheduling, coordination and releasing. However, an effective CODE4MEC requires non-trivial algorithmic schemes, in particular for CODE scheduling and coordination functions. We thus design an online combinatorial auction mechanism for real-time CODE scheduling, and prove a tighter performance bound relative to prior arts. As for CODE coordination, a flow-based traffic and context information coordination scheme is proposed to enable classical defense schemes to work properly and efficiently. Finally, using a combination of real testbed and simulation evaluations, we validate the effectiveness of CODE4MEC.

Online Combinatorial Auctions for Resource Allocation With Supply Costs and Capacity Limits

We study a general online combinatorial auction problem in algorithmic mechanism design. A provider allocates multiple types of capacity-limited resources to customers that arrive in a sequential and arbitrary manner. Each customer has a private valuation function on bundles of resources that she can purchase (e.g., a combination of different resources such as CPU and RAM in cloud computing). The provider charges payment from customers who purchase a bundle of resources and incurs an increasing supply cost with respect to the totality of resources allocated. The goal is to maximize the social welfare, namely, the total valuation of customers for their purchased bundles, minus the total supply cost of the provider for all the resources that have been allocated. We adopt the competitive analysis framework and provide posted-price mechanisms with optimal competitive ratios. Our pricing mechanism is optimal in the sense that no other online algorithms can achieve a better competitive ratio. We validate the theoretic results via empirical studies of online resource allocation in cloud computing. Our numerical results demonstrate that the proposed pricing mechanism is competitive and robust against system uncertainties and outperforms existing benchmarks.

Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems

We present prior robust algorithms for a large class of resource allocation problems where requests arrive one-by-one (online), drawn independently from an unknown distribution at every step. We design a single algorithm that, for every possible underlying distribution, obtains a 1−ϵ fraction of the profit obtained by an algorithm that knows the entire request sequence ahead of time. The factor ϵ approaches 0 when no single request consumes/contributes a significant fraction of the global consumption/contribution by all requests together. We show that the tradeoff we obtain here that determines how fast ϵ approaches 0, is near optimal: We give a nearly matching lower bound showing that the tradeoff cannot be improved much beyond what we obtain. Going beyond the model of a static underlying distribution, we introduce the adversarial stochastic input model, where an adversary, possibly in an adaptive manner, controls the distributions from which the requests are drawn at each step. Placing no restriction on the adversary, we design an algorithm that obtains a 1−ϵ fraction of the optimal profit obtainable w.r.t. the worst distribution in the adversarial sequence. Further, if the algorithm is given one number per distribution, namely the optimal profit possible for each of the adversary’s distribution, then we design an algorithm that achieves a 1−ϵ fraction of the weighted average of the optimal profit of each distribution the adversary picks. In the offline setting we give a fast algorithm to solve very large linear programs (LPs) with both packing and covering constraints. We give algorithms to approximately solve (within a factor of 1+ϵ) the mixed packing-covering problem with O (γ m log ( n /δ)/ϵ 2 ) oracle calls where the constraint matrix of this LP has dimension n × m , the success probability of the algorithm is 1−δ, and γ quantifies how significant a single request is when compared to the sum total of all requests. We discuss implications of our results to several special cases including online combinatorial auctions, network routing, and the adwords problem.

Approximation Algorithms for Online User Association in Multi-Tier Multi-Cell Mobile Networks

The constantly growing wireless bandwidth demand is pushing wireless networks to multi-tier architectures consisting of a macrocell tier and a number of dense small cell deployment tiers. In such a multi-tier multi-cell environment, the classic problem of associating users to base stations becomes both more challenging and more critical to the overall network performance. Most previous analytical work is focused on designing static user-cell association algorithms, which, to achieve optimality, are periodically applied whenever there are new user arrivals, thus potentially inducing a large number of re-associations for previously arrived users. On the other hand, practical online algorithms that do not allow any such user re-association are often based on heuristics and may not have any performance guarantees. In this paper, we propose online algorithms for the multi-tier multi-cell user association problem that have provable performance guarantees, which improve previously known bounds by a sizable amount. The proposed algorithms are motivated by online combinatorial auctions, while capturing and leveraging the relative sparsity of choices in wireless networks as compared with auction setups. Our champion algorithm is a $\frac {1}{2-a^{-1}}$ approximation algorithm, where $a$ is the maximum number of feasible associations for a user and is, in general, small due to path loss. Our analysis considers the state-of-the-art wireless technologies, such as massive and multiuser MIMO, and practical aspects of the system such as the fact that highly mobile users have a preference to connect to larger cell tiers to keep the signaling overhead low. In addition to establishing formal performance bounds, we also conduct simulations under realistic assumptions, which establish the superiority of the proposed algorithm over existing approaches under real-world scenarios.

An Online Auction Framework for Dynamic Resource Provisioning in Cloud Computing

Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and allocating resources in cloud computing. This work, to the authors’ knowledge, represents the first online combinatorial auction designed for the cloud computing paradigm, which is general and expressive enough to both: 1) optimize system efficiency across the temporal domain instead of at an isolated time point; and 2) model dynamic provisioning of heterogeneous virtual machine (VM) types in practice. The final result is an online auction framework that is truthful, computationally efficient, and guarantees a competitive ratio $\approx 3.30$ in social welfare in typical scenarios. The framework consists of three main steps: 1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with a small additive loss in competitive ratio; 2) a randomized subframework that applies primal-dual optimization for translating a centralized cooperative social welfare approximation algorithm into an auction mechanism, retaining the competitive ratio while adding truthfulness; and 3) a primal-dual algorithm for approximating the one-shot optimization with a ratio close to $e$ . We also propose two extensions: 1) a binary search algorithm that improves the average-case performance; 2) an improvement to the online auction framework when a minimum budget spending fraction is guaranteed, which produces a better competitive ratio. The efficacy of the online auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework can be instructive in auction design for other related problems .

An online auction framework for dynamic resource provisioning in cloud computing

Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and resource allocation in cloud computing. This work, to the authors' knowledge, represents the first online combinatorial auction designed in the cloud computing paradigm, which is general and expressive enough to both (a) optimize system efficiency across the temporal domain instead of at an isolated time point, and (b) model dynamic provisioning of heterogeneous Virtual Machine (VM) types in practice. The final result is an online auction framework that is truthful, computationally efficient, and guarantees a competitive ratio ~ e + 1 over e -1 ~ 3.30 in social welfare in typical scenarios. The framework consists of three main steps: (1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with an additive loss of 1 over e -1 in competitive ratio, (2) a randomized auction sub-framework that applies primal-dual optimization for translating a centralized co-operative social welfare approximation algorithm into an auction mechanism, retaining a similar approximation ratio while adding truthfulness, and (3) a primal-dual update plus dual fitting algorithm for approximating the one-shot optimization with a ratio λ close to e. The efficacy of the online auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework, as well as its three independent modules, can be instructive in auction design for other related problems.

Experimenting with Proxy Agents in Online Combinatorial Auctions

To promote online eBay-like combinatorial auctions, the authors experiment with a distributive agent based mechanism PRACA which attempts to address two research issues. First, the scheme uses an algorithm CompDL to incrementally solve the Winner Determination Problem under limited memory for any package that the bidder may be interested to bid at any time. This helps the bidder to bid effectively knowing the current state of the auction. CompDL thereby allows a large number of items to be put on auction. Second, PRACA supplies an autonomous proxy agent to each bidder who logs in to an ongoing auction; this agent bids on the bidder's behalf and relays data on requested packages to the bidder. Therefore, PRACA helps in reducing operational overheads of seller and bidder in online combinatorial auctions. Both schemes have been implemented and the efficacy of the approach is demonstrated by experiments.

TRADE: A truthful online combinatorial auction for spectrum allocation in cognitive radio networks

AbstractAuctions have been shown to be able to tackle the problem of spectrum scarcity effectively, but most of existing works only focus on static scenarios. They cannot deal with the requests of spectrum users as they arrive and leave dynamically. Bidders can either cheat by bidding untruthfully or cheat about the arrival and departure time. In this paper, we model the radio spectrum allocation problem as a sealed‐bid online combinatorial auction and propose a truthful mechanism called TRADE. TRADE is a truthful and an individual rational mechanism with polynomial time complexity. It can prevent bidders from cheating in the auction while achieving good bidder satisfaction, spectrum utilization, and social welfare. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Design and implementation of a secure multi-agent marketplace

A multi-agent marketplace, MAGNET (Multi-AGent Negotiation Testbed), is a promising solution for conducting online combinatorial auctions. The trust model of MAGNET is somewhat different from other on-line auction systems since the marketplace, which mediates all communications between agents, acts as a partially trusted third party. We identify the security vulnerabilities of MAGNET and present a solution that overcomes these weaknesses. Our solution makes use of three different existing technologies with standard cryptographic techniques: a publish/subscribe system to provide simple and general messaging, time-release cryptography to provide guaranteed non-disclosure of the bids, and anonymous communication to hide the identity of the bidders until the end of the auction. Using these technologies, we successfully minimize the trust on the market as well as increase the security of the whole system. The protocol that we have developed can be adapted for use by other agent-based auction systems, that use a third party to mediate transactions.