Online Auction Design Research Articles

Dynamic VM Scaling: Provisioning and Pricing through an Online Auction

Today's IaaS clouds allow dynamic scaling of VMs allocated to a user, according to real-time demand of the user. There are two types of scaling: horizontal scaling (scale-out) by allocating more VM instances to the user, and vertical scaling (scale-up) by boosting resources of VMs owned by the user. It has been a daunting issue how to efficiently allocate the resources on physical servers to meet the scaling demand of users on the go, which achieves the best server utilization and user utility. An accompanying critical challenge is how to effectively charge the incremental resources, such that the economic benefits of both the cloud provider and cloud users are guaranteed. There has been online auction design dealing with dynamic VM provisioning, where the resource bids are not related to each other, failing to handle VM scaling where later bids may rely on earlier bids of the same user. As the first in the literature, this paper designs an efficient, truthful online auction for resource provisioning and pricing in the practical cases of dynamic VM scaling, where: (i) users bid for customized VMs to use in future durations, and can bid again in the following time to increase resources, indicating both scale-up and scale-out options; (ii) the cloud provider packs the demanded VMs on heterogeneous servers for energy cost minimization on the go. We carefully design resource prices maintained for each type of resource on each server to achieve threshold-based online allocation and charging, as well as a novel competitive analysis technique based on submodularity of the offline objective, to show a good competitive ratio is achieved. The efficacy of the online auction is validated through solid theoretical analysis and trace-driven simulations.

Oracle-efficient Online Learning and Auction Design

We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Follow-the-Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by Hazan and Koren [31], who showed that oracle-efficient algorithms do not exist in general [30] and asked whether one can identify properties under which oracle-efficient online learning may be possible. Our auction-design framework considers an auctioneer learning an optimal auction for a sequence of adversarially selected valuations with the goal of achieving revenue that is almost as good as the optimal auction in hindsight, among a class of auctions. We give oracle-efficient learning results for: (1) VCG auctions with bidder-specific reserves in single-parameter settings, (2) envy-free item pricing in multi-item auctions, and (3) s-level auctions of Morgenstern and Roughgarden [43] for single-item settings. The last result leads to an approximation of the overall optimal Myerson auction when bidders’ valuations are drawn according to a fast-mixing Markov process, extending prior work that only gave such guarantees for the i.i.d. setting. Finally, we derive various extensions, including: (1) oracle-efficient algorithms for the contextual learning setting in which the learner has access to side information (such as bidder demographics), (2) learning with approximate oracles such as those based on Maximal-in-Range algorithms, and (3) no-regret bidding in simultaneous auctions, resolving an open problem of Daskalakis and Syrgkanis [14].

An efficient online auction for resource leasing in cloud radio access networks

This work studies the emerging C-RAN market in a 5G wireless network where mobile operators lease computation and communication resources from the tower company to serve wireless users. We propose an online C-RAN auction where each mobile operator bids for three types of resources in a future time window: wireless spectrum at base stations (BSs), front-haul link capacities, and mobile BS instances at the mobile cloud. We target an online C-RAN auction that executes in polynomial time, elicits truthful bids from mobile operators, and maximizes the social welfare of the C-RAN eco-system with both spectrum cost at BSs and server cost at the mobile cloud considered. We consider two business models of the tower company, and show how the marriage of i) a new Fenchel dual approach to convex optimization with ii) the posted pricing framework for online auction design can help achieve the three goals simultaneously, and evaluate the efficiency of our online C-RAN auction through both theoretical analysis and empirical studies.

An Online Emergency Demand Response Mechanism for Cloud Computing

This article studies emergency demand response (EDR) mechanisms from a data center perspective, where a cloud participates in a mandatory EDR program while receiving computing job bids from cloud users in an online fashion. We target a realistic EDR mechanism where (i) the cloud provider dynamically packs different types of resources on servers into requested VMs and computes job schedules to meet users’ requirements, (ii) the power consumption of servers in the cloud is limited by the grid through the EDR program, and (iii) the operation cost of the cloud is considered in the calculation of social welfare, measured by an electricity cost that consists of both volume charge and peak charge. We propose an online auction for dynamic cloud resource provisioning that is under the control of the EDR program, runs in polynomial time, achieves truthfulness, and close-to-optimal social welfare for the cloud ecosystem. In the design of the online auction, we first propose a new framework, compact exponential LPs, to handle job scheduling constraints in the time domain. We then develop a posted pricing auction framework toward the truthful online auction design, which leverages the classic primal-dual technique for approximation algorithm design. We evaluate our online auctions through both theoretical analysis and empirical studies driven by real-world traces.

An Efficient Cloud Market Mechanism for Computing Jobs With Soft Deadlines

This paper studies the cloud market for computing jobs with completion deadlines, and designs efficient online auctions for cloud resource provisioning. A cloud user bids for future cloud resources to execute its job. Each bid includes: 1) a utility, reflecting the amount that the user is willing to pay for executing its job and 2) a soft deadline, specifying the preferred finish time of the job, as well as a penalty function that characterizes the cost of violating the deadline. We target cloud job auctions that executes in an online fashion, runs in polynomial time, provides truthfulness guarantee, and achieves optimal social welfare for the cloud ecosystem. Towards these goals, we leverage the following classic and new auction design techniques. First, we adapt the posted pricing auction framework for eliciting truthful online bids. Second, we address the challenge posed by soft deadline constraints through a new technique of compact exponential-size LPs coupled with dual separation oracles. Third, we develop efficient social welfare approximation algorithms using the classic primal-dual framework based on both LP duals and Fenchel duals. Empirical studies driven by real-world traces verify the efficacy of our online auction design.

Incentivizing Device-to-Device Load Balancing for Cellular Networks: An Online Auction Design

The device-to-device load balancing (D2D-LB) paradigm has been advocated in recent small-cell architecture design for cellular networks. The idea is to exploit inter-cell D2D communication and dynamically relay traffic of a busy cell to adjacent under-utilized cells to improve spectrum temporal efficiency, addressing a fundamental drawback of small-cell architecture. Technical challenges of D2D-LB have been studied in previous works. The potential of D2D-LB, however, cannot be fully realized without providing proper incentive mechanism for device participation. In this paper, we address this economical challenge using an online procurement auction framework. In our design, multiple sellers (devices) submit bids to participate in D2D-LB and the auctioneer (cellular service provider) evaluates all the bids and decides to purchase a subset of them to fulfill load balancing requirement with the minimum social cost. Different from similar auction design studies for cellular offloading, battery limit of relaying devices imposes a time-coupled capacity constraint that turns the underlying problem into a challenging multi-slot one. Furthermore, the dynamics in the input to the multi-slot auction problem emphasize the need for online algorithm design. We first tackle the single-slot version of the problem, show that it is NP-hard, and design a polynomial-time offline algorithm with a small approximation ratio. Building upon the single-slot results, we design an online algorithm for the multi-slot problem with sound competitive ratio. Our auction algorithm design ensures that truthful bidding is a dominant strategy for devices. Extensive experiments using real-world traces demonstrate that our proposed solution achieves near offline-optimum and reduces the cost by 45% compared with an alternative heuristic.

A Framework for Truthful Online Auctions in Cloud Computing with Heterogeneous User Demands

Auction-style pricing policies can effectively reflect the underlying trends in demand and supply for the cloud resources, and thereby attracted a research interest recently. In particular, a desirable cloud auction design should be (1) online to timely reflect the fluctuation of supply-demand relations, (2) expressive to support the heterogeneous user demands, and (3) truthful to discourage users from cheating behaviors. Meeting these requirements simultaneously is non-trivial, and most existing auction mechanism designs do not directly apply. To meet these goals, this paper conducts the first work on a framework for truthful online cloud auctions where users with heterogeneous demands could come and leave on the fly. Concretely speaking, we first design a novel bidding language, wherein users’ heterogeneous requirement on their desired allocation time, application type, and even how they value among different possible allocations can be flexibly and concisely expressed. Besides, building on top of our bidding language we propose COCA, an incentive-Compatible (truthful) Online Cloud Auction mechanism. To ensure truthfulness with heterogenous and online user demand, the design of COCA is driven by a monotonic payment rule and a utility-maximizing allocation rule. Moreover, our theoretical analysis shows that the worst-case performance of COCA can be well-bounded, and our further discussion shows that COCA performs well when some other important factors in online auction design are taken into consideration. Finally, in simulations the performance of COCA is seen to be comparable to the well-known off-line Vickrey-Clarke-Groves (VCG) mechanism [19] .

Distrust, Fear and Emotional Learning: An Online Auction Perspective

This exploratory research explores the emotional perspective of online distrust, where distrust is a fear. Fear is one of the negative emotions where the situational state is motive-inconsistent and uncertainty is present. A review of literature explores distrust as a fear, emotions, emotional learning and identifies cognitive dimensions consistent with fear. These cognitive dimensions are motive-inconsistent situational state, uncertainty, goal-path obstacle, effort, and agency/control. Textual data is collected from an online auction community website to support this research. The dimensions consistent with fear are identified within the data collected and analyzed using qualitative analysis. Goal-path obstacles are identified within the data as; value incongruence, user isolation, user inefficacy, and financial. The goal-path obstacles identified are consistent with types of fear. The research findings are discussed in terms of the theory and offers suggestions to practitioners to improve the design of online auctions by reducing the effects of fear and emotional learning.

Design of online auctions: Proxy versus non-proxy settings

Recent years have witnessed the rapid development of online auctions. Currently, some online auctions, such as eBay, introduce a proxy bidding policy, under which bidders submit their maximum bids and delegate to a proxy agent to automatically outbid other competitors for the top bidder, whereas other online auctions do not. This paper compares these two widely used auction mechanisms (proxy setting and non-proxy setting) and characterizes the equilibrium bidding behavior and the seller's expected revenue. We find the proxy auction outperforms the non-proxy auction in terms of the seller's expected revenue. This dominance result is not prone to the specific bid announcement policy, the bidder's knowledge regarding the number of bidders, the impact of traffic congestion along the bidding process, the number of items sold through the auction, and the existence of a reserve price. We further find that the proxy setting usually fails to sustain the truthful bidding as a dominant strategy equilibrium even if no minimum bid increments are adopted, and the possibility of a low-valuation-bidder dilemma where the low-valuation bidders could be better off if all bidders collude to bid at the last minute. We also discuss the dramatically different equilibrium bidding behaviors under the two auction mechanisms.

Consumer-to-Consumer Internet Auction Models

Internet auctions have become an increasingly common method for exchanging goods and services across the world both among consumers themselves, as well as between businesses and consumers. These Internet auction mechanisms have the scope of incorporating procedures of much greater complexity and variety, and they exhibit characteristics and properties that are quite distinct from conventional auctions. In this paper, the authors provide an experimental study of the performance characteristics and operational behaviour of a number of online auction models, including the fixed time forward auctions, the Vickrey auctions, and models with soft close variable auction times. These online auction models are studied through systematic simulation experiments, based on a series of operational assumptions, which characterize the arrival rate of bids, as well as the distribution from which the private values of buyers are sampled. Suggestions for efficient online auction design and procedures for improving auction performance are given, and the behaviour of the average auction income and average auction duration are quantified and compared.

Institutional dependencies in dynamic buyout price models for online auctions

There is disagreement in the auction literature on the question of whether dynamic Buy-It-Now (BIN) prices can increase the efficiency of online auctions, compared to static BIN price auctions. In a previous paper (Vragov et al. 2010), we reported experimental evidence that suggested, contrary to the current theoretical auction literature, that dynamic BIN pricing is indeed economically more efficient. The current paper presents a replication study of this research that interestingly fails to reproduce the earlier findings. It is based on the same general experimental design, but modifying the implementation of the specific trading institution by using a linearly declining BIN price (Online Linear Dutch Auction—OLDA) rather than the discrete one-time price change (Online One-time BIN-price Change Auction—OOBCA) that was used in the previous study. OLDA is more dynamic in nature than OOBCA, yet OOBCA outperforms a generalized static online auction design in the laboratory while the OLDA does not. We explain these different results, and thus resolve the ostensible contradiction between the two sets of experiments, by concluding that the specific implementation of the trading institution (i.e., the dynamic BIN pricing mechanism) has a significant effect on the overall market efficiency. In other words, our research finds that the efficiency of dynamic buyout price auctions is institution-dependent. This has important theoretical implications as most formal auction models assume that the auction outcome is institution-free. We also discuss some practical implications of our findings.

A meta-analysis on the effects of online auction design options: The moderating effect of value uncertainty

Online auction design options (the public reserve price, secret reserve option and buy-out option) are critical in determining auction outcomes (the number of bids, the probability of sale and auction price). However, previous studies about the impacts of online auction design options on auction outcomes have generated inconsistent or even contradictory results. To synthesize the inconsistencies and reach more substantive conclusions, we conduct this meta-analysis study. Furthermore, to explain the inconsistencies, we identify the value uncertainty of auction items as a key moderator on the impacts of auction design options on auction outcomes, and verify the moderating effects using meta-analysis methods. This study has three main findings: (i) the public reserve price has a positive effect on the auction price, and this effect is stronger when the value uncertainty of auction items is higher; (ii) the secret reserve option has a positive effect on the auction price when auction items are of low value uncertainty, but the magnitude of this effect decreases when the value uncertainty increases; (iii) the buy-out option has positive effects on both the probability of sale and the auction price when auction items are of low value uncertainty, but has negative effects on these two auction outcomes when auction items are of high value uncertainty.

Online Trust Mechanisms: A Domain Study of Online Auctions

This research investigates mechanisms that impact trust at online auctions. A qualitative study is discussed within this paper to identify mechanisms that impact trust at online auctions. The mechanisms identified are discussed within normative and grounded theoretical approaches to trust. A result of this discussion I seek to answer the question: what factors do people perceive as important to increase online trust prior to an online transaction? This discussion will help professionals understand the factors that emerge from normative and grounded theoretical approaches to online trust research. A better understanding of online trust adds to the body of theory and can be used to improve the design of online auctions.

Managing Online Auctions: Current Business and Research Issues

The Internet's computational power and flexibility have made auctions a widespread and integral part of both consumer and business markets. Though online auctions are a multi-billion dollar annual activity, with a growing variety of sophisticated trading mechanisms, scientific research on them is at an early stage. This paper analyzes the current state of management science research on online auctions. It develops a broad research agenda for issues such as the behavior of online auction participants, the optimal design of online auctions, the integration of auctions into the ongoing operation of firms, and the use of the data generated by online auctions to inform future trading mechanisms. These research areas will draw from applied and theoretical work spanning management science, economics, and information systems.

The Design of Online Auctions: Business Issues and Current Research

The Internet has made auctions a common and integral part of the way commerce is conducted today. In both the consumer and business markets the applicability of auction-based trading mechanisms has been expanded by the computational power and flexibility the Internet makes available. There is currently multi-billion dollar annual activity in the online auction market with a growing variety of sophisticated trading mechanisms. The broader range of applications for auctions and implementation choices means that there is a need for more scientific research on online auctions that can be used to optimize their design. In this paper we seek to characterize the current state of management science research on online auctions, present some new empirical research results, and develop a broad research agenda around many open business issues. We find that the research in this area is still at an early stage and there remain many opportunities to do interesting applied and theoretical work spanning the disciplines of management science, economics, and information systems. Such work will better inform managers about the proper design and use of online auctions.