A Brokering Algorithm for Cost & QoS-Based Winner Determination in Combinatorial Auctions

Combinatorial auctions are efficient mechanisms for allocating resource in complex marketplace. Winner determination, which is NP-complete, is the core problem in combinatorial auctions. This paper proposes an immune partheno-genetic algorithm (IPGA) for solving this problem. Firstly, a zero-one programming model is built for the winner determination problem with XOR-bids and OR-bids. Then, steps of constructing three partheno-genetic operators and an immune operator are introduced. In the immune operation, new heuristics are designed for vaccines selection and vaccination. Simulation results show that the IPGA achieves good performance in large size problems and the immune operator can improve the searching ability and increase the converging speed greatly.

Algorithm for optimal winner determination in combinatorial auctions

We propose the use of logic-based preference representation languages based on weighted propositional formulas for specifying bids in a combinatorial auction. We then develop several heuristics for a branch-and-bound search algorithm for determining the winning bids in this framework and report on their empirical performance. The logic-based approach is attractive due to its high degree of flexibility in designing a range of different bidding languages within a single conceptual framework.

The combinatorial auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing. It can obtain high economic efficiency and user flexibility by allowing bidders to submit bids for combinations of different items instead of only for individual items. However, the problem of allocating items among the bidders to maximize the auctioneers’ revenue, i.e., the winner determination problem (WDP), is NP-complete to solve and inapproximable. Existing works for WDPs are generally based on mathematical optimization techniques and most of them focus on the single-unit WDP, where each item only has one unit. On the contrary, few works consider the multi-unit WDP in which each item may have multiple units. Given that the multi-unit WDP is more complicated but prevalent in cloud computing, we propose leveraging machine learning (ML) techniques to develop a novel low-complexity algorithm for solving this problem with negligible revenue loss. Specifically, we model the multi-unit WDP as an augmented bipartite bid-item graph and use a graph neural network (GNN) with half-convolution operations to learn the probability of each bid belonging to the optimal allocation. To improve the sample generation efficiency and decrease the number of needed labeled instances, we propose two different sample generation processes. We also develop two novel graph-based post-processing algorithms to transform the outputs of the GNN into feasible solutions. Through simulations on both synthetic instances and a specific virtual machine (VM) allocation problem in a cloud computing platform, we validate that our proposed method can approach optimal performance with low complexity and has good generalization ability in terms of problem size and user-type distribution.

In Combinatorial Auctions, multiple goods (items) are available for auction simultaneously, and bidders bid for combinations of goods called bundles. In the single unit combinatorial auction problem, there is only single unit of each item present and the items are considered to be indivisible. This forms the supply side constraint resulting in a need by the seller to decide on which bundles to allocate for maximizing the revenue. Determining these winning bundles is an NP-complete problem and is known by the name of Winner Determination Problem (WDP) in literature. Combinatorial auction mechanism has a wide range of practical applications such as allocation of railroads, auction of adjacent pieces of real estate, auction of airport landing slots, distributed job shop scheduling etc. The optimal heuristic search algorithms like CASS and CABOB proposed for solving the WDP take a lot of CPU time for solving large problem instances. Though these algorithms give optimal solutions, they would be useful only in scenarios where time is not at all a constraint, and the bidding is a one time affair. This poses a limitation in conducting combinatorial auctions with multiple rounds of bidding and hence hinders the widespread use of combinatorial auctions on the patterns of traditional English auction and internet auctions. Thus faster solution of WDP holds the key to widespread use and popularity of combinatorial auctions. This paper attempts to address the above issue, and proposes a simple local search technique LSWDP (Local Search for Winner Determination Problem) that runs very fast and provides solutions quite close to optimal for a number of applications. It also outputs optimal solutions in many instances taking only a fraction of the CPU time taken by CASS. In addition LSWDP was found to outperform the anytime algorithm CASS given a time cutoff. This paper also presents an enhancement called LSCN (Local Search using Complimentary Neighborhood) that provides still better solutions in terms of close to optimal behavior and achieves a much better strike rate in hitting optimal solutions. LSCN runs slower than LSWDP but still takes only a fraction of the CPU time taken by CASS thus encouraging the use of local search for combinatorial auctions. Finally, an extension to multi-partition search has been proposed and analyzed.

In this paper we present a distributed system that is capable of solving the Winner Determination Problem (WDP) for combinatorial auctions by use of a genetic algorithm. Combinatorial auctions are simple, allowing agents to bid on combination of items rather than making several individual bids. These types of auctions lead to more economical efficient allocations of goods in situations where the agents' have preference over bundles of goods. However, determining the winner(s) has been shown to be a NP-complete problem. This means that there is no simple way to solving the WDP. Our approach is to use a distributed system that solves the WDP using a genetic algorithm that is being distributed. In this way we are able to take advantage of the benefits that distributed systems offer. The results obtained from the system are compared to IBM's algorithm, known as CPLEX.

Exact Algorithms for the Matrix Bid Auction

Auctions, and combinatorial auctions (CAs), have been successfully employed to solve coordination problems in a wide range of application domains. However, the scale of CAs that can be optimally solved is small because of the complexity of the winner determination problem (WDP), namely of finding the bids that maximise the auctioneer’s revenue. A way of approximating the solution of a WDP is to solve its linear programming relaxation. The recently proposed Alternate Direction Dual Decomposition algorithm (AD 3) has been shown to efficiently solve large-scale LP relaxations. Hence, in this paper we show how to encode the WDP so that it can be approximated by means of AD 3. Moreover, we present PAR-AD 3, the first parallel implementation of AD 3. PAR-AD 3 shows to be up to 12.4 times faster than CPLEX in a single-thread execution, and up to 23 times faster than parallel CPLEX in an 8-core architecture. Therefore PAR-AD 3 becomes the algorithm of choice to solve large-scale WDP LP relaxations for hard instances. Furthermore, PAR-AD 3 has potential when considering large-scale coordination problems that must be solved as optimisation problems.

In this paper, on the basis of the analysis of common market model and some economic theories in the cloud computing resource management process, we propose a cloud resource management model based on combinatorial double auction. In order to solve the winner determination problem (WDP) in the combinatorial double auction, a cloud resource combinatorial double auction algorithm based on genetic algorithm and simulated annealing algorithm is proposed. Simulation results reveal that the algorithm combines genetic algorithm with simulated annealing algorithm (SAGA) outperforms genetic algorithm on fitness value and stability, and as the number of bidders increase, the solution have higher fitness value can be obtained.

This special issue includes four articles that were presented in the 15th Workshop on Information Technology and Systems (WITS’05), held in December 10–11, 2005, Las Vegas, Nevada. Since its first meeting in 1991 in MIT, WITS has become a premier annual forum for discussion and interaction among scholars with research interests in the cutting edge issues of information technology and systems. WITS’05 addressed many important research issues as the corporate world evolves toward service-oriented computing that can support business on demand, enabled by the emergence of web services, agent technologies, wireless computing, e-commerce standards, P2P technologies, the business web, among other technologies. The four selected articles have been extended substantially and went through two rounds of reviews. Collectively, these four articles explore new patterns and mechanisms that extend the new boundaries of information technologies and systems. It is our pleasure to make these four fine articles available to the community through the journal of Information Technology and Management. We thank all authors and reviewers for their diligent work that made this special issue possible. Next, we give a brief overview of the four papers in terms of their main discoveries and important contributions. The first article, ‘‘Combining probability models and Web mining models: A framework for proper name transliteration’’ by Zhou, Huang, and Chen combines probability models and web mining models to develop a framework for proper name transliteration. The authors argue that web pages are created today in almost every popular language. However, language boundaries prevent information sharing and discovery across countries. Their paper studies transliteration (referred to that proper names are translated phonetically) issue on the Web. Previous transliteration models can be categorized into four approaches: a rule-based approach, a machine learning approach, a statistical approach, and a web mining approach. This paper proposes a generic proper name transliteration framework which improves the traditional statistical approach by incorporating an enhanced Hidden Markov Model and a web mining model. Evaluation of the proposed framework is very promising as it boosted the performance by 79.05%. Singh and Sen in their article ‘‘Exploring local search in winner determination problem,’’ report their research in developing a new combinatorial auction mechanism for a wide range of practical applications such as allocation of railroads, auction of adjacent pieces of real estate, auction of airport landing slots, and distributed job shop scheduling. Determining the winning bundles in combinatorial auctions is an NP-complete problem. The inefficiency problem in existing approaches to combinatorial auction poses a limitation in conducting combinatorial auctions with multiple rounds of bidding and hinders the widespread use of combinatorial auctions on the Internet. This paper proposes a local search algorithm for combinatorial auctions, which takes only fraction of the CPU time taken by T. Han College of Business, University of Nevada, NV, Las Vegas 89154, USA

Combinatorial auctions (CAs), where users bid on combination of items, have emerged as a useful tool for resource allocation in distributed systems. However, two main difficulties exist to the adoption of CAs in time-constrained environments. The first difficulty involves the computational complexity of winner determination. The second difficulty entails the computational complexity of eliciting utility valuations for all possible combinations of resources to different tasks. To address both issues, we developed a new algorithm, seeded genetic algorithm (SGA) for finding high quality solutions quickly. SGA uses a novel representational schema that produces only feasible solutions. We compare the winner determination performance of our algorithm with Casanova, another local stochastic search procedure, on typically hard-to-solve bid distributions. We show that SGA converges to a better solution than Casanova for large problem sizes. However, for many bid distributions, exact winner determination using integer programming approaches is very fast, even for large problem sizes. In these cases, SGA can still provide significant time savings by eliminating the requirement for formulating all possible bids.

Combinatorial auction, where bidders can bid on bundles of items, has been the subject of increasing interest in recent years. Although much research work has been conducted on combinatorial auctions, most has been focusing on the winner determination problem. A largely unexplored area of research in combinatorial auctions is the design of bidding strategies, in particular, those that can be used in open and dynamic markets in which market situation is generally constantly changing. Obviously, a good bidding strategy to be used in such realistic markets cannot be obtained by analytical methods, which require all market information be known before a solution can possibly be found. Machine learning based approaches are not completely appropriate, either, as an optimal strategy learned from one market generally no longer perform well when the market situation changes. In this paper, we propose a new adaptive bidding strategy for combinatorial auction-based resource allocation problem in such dynamic markets. A bidder adopting this strategy constantly perceives the market situation, and adaptively reviews and adjusts his bid determination, thus responding to the dynamic market in a timely manner. Experiment results show that agents adopting this adaptive bidding strategy perform very well, even without any prior knowledge about the market.

Winner determination in geometrical combinatorial auctions

Online auctions, including online Combinatorial Auctions, are important examples of e-commerce applications. In this paper, a Combinatorial Auction Web Platform (CAWP) is introduced. The platform enables both product selling and buying capabilities that can be realized in a combinatorial way. CAWP supports a Sealed-Bid Single-Unit type of Combinatorial Auctions. Easy customization for any selected problem domain is a distinguished feature of CAWP. Platform users are not expected to have any technical knowledge about how to solve the Winner Determination Problem (WDP) known to be critical for profit maximization of the auctioneers in Combinatorial Auctions.

Hierarchical task network (HTN) planning is well-known for being an efficient planning approach. This is mainly due to the success of the HTN planning system SHOP2. However, its performance depends on hand-designed search control knowledge. At the time being, there are only very few domain-independent heuristics, which are designed for differing hierarchical planning formalisms. Here, we propose an admissible heuristic for standard HTN planning, which allows to find optimal solutions heuristically. It bases upon the so-called task decomposition graph (TDG), a data structure reflecting reachable parts of the task hierarchy. We show (both in theory and empirically) that rebuilding it during planning can improve heuristic accuracy thereby decreasing the explored search space. The evaluation further studies the heuristic both in terms of plan quality and coverage.