Results 1  10
of
82
Tycoon: An Implementation of a Distributed, Marketbased Resource Allocation System”, Multiagent Grid Systems
"... Distributed clusters like the Grid and PlanetLab enable the same statistical multiplexing efficiency gains for computing as the Internet provides for networking. One major challenge is allocating resources in an economically efficient and lowlatency way. A common solution is proportional share, whe ..."
Abstract

Cited by 89 (6 self)
 Add to MetaCart
(Show Context)
Distributed clusters like the Grid and PlanetLab enable the same statistical multiplexing efficiency gains for computing as the Internet provides for networking. One major challenge is allocating resources in an economically efficient and lowlatency way. A common solution is proportional share, where users each get resources in proportion to their predefined weight. However, this does not allow users to differentiate the value of their jobs. This leads to economic inefficiency. In contrast, systems that require reservations impose a high latency (typically minutes to hours) to acquire resources. We present Tycoon, a market based distributed resource allocation system based on proportional share. The key advantages of Tycoon are that it allows users to differentiate the value of their jobs, its resource acquisition latency is limited only by communication delays, and it imposes no manual bidding overhead on users. We present experimental results using a prototype implementation of our design. 1
Competitive Auctions
, 2002
"... We study a class of singleround, sealedbid auctions for items in unlimited supply, such as digital goods. We introduce the notion of competitive auctions. A competitive auction is truthful (i.e., encourages buyers to bid their utility) and yields profit that is roughly within a constant factor of ..."
Abstract

Cited by 83 (10 self)
 Add to MetaCart
We study a class of singleround, sealedbid auctions for items in unlimited supply, such as digital goods. We introduce the notion of competitive auctions. A competitive auction is truthful (i.e., encourages buyers to bid their utility) and yields profit that is roughly within a constant factor of the profit of optimal fixed pricing for all inputs. We justify the use of optimal fixed pricing as a benchmark for evaluating competitive auction profit. We show that several randomized auctions are truthful and competitive and that no truthful deterministic auction is competitive. Our results extend to bounded supply markets, for which we also get truthful and competitive auctions.
Worstcase optimal redistribution of VCG payments
 In Proceedings of the ACM Conference on Electronic Commerce (EC
, 2007
"... For allocation problems with one or more items, the wellknown VickreyClarkeGroves (VCG) mechanism is efficient, strategyproof, individually rational, and does not incur a deficit. However, the VCG mechanism is not (strongly) budget balanced: generally, the agents ’ payments will sum to more than ..."
Abstract

Cited by 49 (15 self)
 Add to MetaCart
(Show Context)
For allocation problems with one or more items, the wellknown VickreyClarkeGroves (VCG) mechanism is efficient, strategyproof, individually rational, and does not incur a deficit. However, the VCG mechanism is not (strongly) budget balanced: generally, the agents ’ payments will sum to more than 0. If there is an auctioneer who is selling the items, this may be desirable, because the surplus payment corresponds to revenue for the auctioneer. However, if the items do not have an owner and the agents are merely interested in allocating the items efficiently among themselves, any surplus payment is undesirable, because it will have to flow out of the system of agents. In 2006, Cavallo [3] proposed a mechanism that redistributes some of the VCG payment back to the agents, while maintaining efficiency, strategyproofness, individual rationality, and the
A multiplechoice secretary algorithm with applications to online auctions
 In ACMSIAM Symposium on Discrete Algorithms
"... In the classical secretary problem, a set S of numbers is presented to an online algorithm in random order. At any time the algorithm may stop and choose the current element, and the goal is to maximize the probability of choosing the largest element in the set. We study a variation in which the alg ..."
Abstract

Cited by 40 (3 self)
 Add to MetaCart
(Show Context)
In the classical secretary problem, a set S of numbers is presented to an online algorithm in random order. At any time the algorithm may stop and choose the current element, and the goal is to maximize the probability of choosing the largest element in the set. We study a variation in which the algorithm is allowed to choose k elements, and the goal is to maximize their sum. We present an algorithm whose competitive ratio is 1 O(~/~). To our knowledge, this is the first algorithm whose competitive ratio approaches 1 as k ~ cx~. As an application we solve an open problem in the theory of online auction mechanisms. 1
Online Algorithms for Market Clearing
, 2002
"... In this paper we study the problem of online market clearing where there is one commodity in the market being bought and sold by multiple buyers and sellers whose bids arrive and expire at different times. The auctioneer is faced with an online clearing problem of deciding which buy and sell bids to ..."
Abstract

Cited by 38 (4 self)
 Add to MetaCart
In this paper we study the problem of online market clearing where there is one commodity in the market being bought and sold by multiple buyers and sellers whose bids arrive and expire at different times. The auctioneer is faced with an online clearing problem of deciding which buy and sell bids to match without knowing what bids will arrive in the future. For maximizing profit, we present a (randomized) online algorithm with a competitive ratio of ln(p max min )+1, when bids are in a range [p min ,p max ], which we show is the best possible. A simpler algorithm has a ratio twice this, and can be used even if expiration times are not known. For maximizing the number of trades, we present a simple greedy algorithm that achieves a factor of 2 competitive ratio if no moneylosing trades are allowed. Interestingly, we show that if the online algorithm is allowed to subsidize matches  match moneylosing pairs if it has already collected enough money from previous pairs to pay for them  then it can be 1competitive with respect to the optimal offline algorithm that is not allowed subsidy. That is, the ability to subsidize is at least as valuable as knowing the future. We also consider the objectives of maximizing buy or sell volume, and present algorithms that achieve a competitive ratio of 2(ln(p max /p min ) + 1), or ln(p max /p min ) + 1 if the online algorithm is allowed subsidization. We show the latter is the best possible competitive ratio for this setting. For social welfare maximization we also obtain an optimal competitive ratio, which is below ln(p max /p min ). We present all of these results as corollaries of theorems on online matching in an incomplete interval graph.
Addressing strategic behavior in a deployed microeconomic resource allocator
 In Proc. 3rd Workshop on Economics of PeertoPeer Systems
, 2005
"... While marketbased systems have long been proposed as solutions for distributed resource allocation, few have been deployed for production use in real computer systems. Towards this end, we present our initial experience using Mirage, a microeconomic resource allocation system based on a repeated co ..."
Abstract

Cited by 27 (1 self)
 Add to MetaCart
(Show Context)
While marketbased systems have long been proposed as solutions for distributed resource allocation, few have been deployed for production use in real computer systems. Towards this end, we present our initial experience using Mirage, a microeconomic resource allocation system based on a repeated combinatorial auction. Mirage allocates time on a heavilyused 148node wireless sensor network testbed. In particular, we focus on observed strategic user behavior over a fourmonth period in which 312,148 node hours were allocated across 11 research projects. Based on these results, we present a set of key challenges for marketbased resource allocation systems based on repeated combinatorial auctions. Finally, we propose refinements to the system’s current auction scheme to mitigate the strategies observed to date and also comment on some initial steps toward building an approximately strategyproof repeated combinatorial auction. 1
An IroningBased Approach to Adaptive Online Mechanism Design in SingleValued Domains
 In Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI’07), 94–101. Menlo Park
, 2007
"... Online mechanism design considers the problem of sequential decision making in a multiagent system with selfinterested agents. The agent population is dynamic and each agent has private information about its value for a sequence of decisions. We introduce a method (“ironing") to transform an ..."
Abstract

Cited by 24 (8 self)
 Add to MetaCart
Online mechanism design considers the problem of sequential decision making in a multiagent system with selfinterested agents. The agent population is dynamic and each agent has private information about its value for a sequence of decisions. We introduce a method (“ironing") to transform an algorithm for online stochastic optimization into one that is incentivecompatible. Ironing achieves this by canceling decisions that violate a form of monotonicity. The approach is applied to the CONSENSUS algorithm and experimental results in a resource allocation domain show that not many decisions need to be canceled and that the overhead of ironing is manageable.
Optimal coordinated planning amongst selfinterested agents with private state
 In Proceedings of the Twentysecond Annual Conference on Uncertainty in Artificial Intelligence (UAI’06
, 2006
"... Consider a multiagent system in a dynamic and uncertain environment. Each agent’s local decision problem is modeled as a Markov decision process (MDP) and agents must coordinate on a joint action in each period, which provides a reward to each agent and causes local state transitions. A social plan ..."
Abstract

Cited by 22 (13 self)
 Add to MetaCart
Consider a multiagent system in a dynamic and uncertain environment. Each agent’s local decision problem is modeled as a Markov decision process (MDP) and agents must coordinate on a joint action in each period, which provides a reward to each agent and causes local state transitions. A social planner knows the model of every agent’s MDP and wants to implement the optimal joint policy, but agents are selfinterested and have private local state. We provide an incentivecompatible mechanism for eliciting state information that achieves the optimal joint plan in a Markov perfect equilibrium of the induced stochastic game. In the special case in which local problems are Markov chains and agents compete to take a single action in each period, we leverage Gittins allocation indices to provide an efficient factored algorithm and distribute computation of the optimal policy among the agents. Distributed, optimal coordinated learning in a multiagent variant of the multiarmed bandit problem is obtained as a special case. 1
Automated online mechanism design and prophet inequalities
 In AAAI
, 2007
"... Recent work on online auctions for digital goods has explored the role of optimal stopping theory — particularly secretary problems — in the design of approximately optimal online mechanisms. This work generally assumes that the size of the market (number of bidders) is known a priori, but that the ..."
Abstract

Cited by 21 (5 self)
 Add to MetaCart
Recent work on online auctions for digital goods has explored the role of optimal stopping theory — particularly secretary problems — in the design of approximately optimal online mechanisms. This work generally assumes that the size of the market (number of bidders) is known a priori, but that the mechanism designer has no knowledge of the distribution of bid values. However, in many realworld applications (such as online ticket sales), the opposite is true: the seller has distributional knowledge of the bid values (e.g., via the history of past transactions in the market), but there is uncertainty about market size. Adopting the perspective of automated mechanism design, introduced by Conitzer and Sandholm, we develop algorithms that compute an optimal, or approximately optimal, online auction mechanism given