A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing

Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1

This paper identifies and defines suitable market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. --Grid Computing

Walverine: A Walrasian Trading Agent

TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverine's optimal bidding approach, as well as several other features of its overall strategy, are potentially applicable in a broad class of trading environments.trading agent, trading competition, tatonnement, competitive equilibrium

Decision Taking for Selling Thread Startup

Decision Taking is discussed in the context of the role it may play for a selling agent in a search market, in particular for agents involved in the sale of valuable and relatively unique items, such as a dwelling, a second hand car, or a second hand recreational vessel. Detailed connections are made between the architecture of decision making processes and a sample of software technology based concepts including instruction sequences, multi-threading, and thread algebra. Ample attention is paid to the initialization or startup of a thread dedicated to achieving a given objective, and to corresponding decision taking. As an application, the selling of an item is taken as an objective to be achieved by running a thread that was designed for that purpose

Development of automated dynamic bidding agents for final price prediction in online auctions

University of Technology, Sydney. Faculty of Engineering and Information Technology.Online auctions have emerged as a well-recognised paradigm of item exchange over the past few years. In these environments, software agents are being used increasingly and promisingly to bid on or trade goods. This thesis presents an automated dynamic bidding agent framework that makes use of machine learning techniques to forecast bid amounts in simultaneous auctions of the same or similar items. The availability of numerous auctions of similar items complicates the situation of bidders who wish to choose the auction where their participation will give maximum surplus. These bidders also face a perpetual dilemma about how to predict an item’s bargain price. Further, the diverse price dynamics of auctions for the same or similar items affect both the choice of auction and the valuation of the auctioned items. There is, thus, a critical need to characterise auctions based on their price dynamics before selecting one to compete in and assessing the true value of the auctioned items. The main contributions of this thesis are its development of: (i) an automated dynamic bidding agent framework, (ii) an initial price estimation methodology for choosing an auction and assessing the value of auctioned goods, (iii) a final price prediction methodology that designs bidding strategies for buyers with different bidding behaviours and (iv) a simulated electronic marketplace for implementing and evaluating the performance of bidding agents. The automated dynamic bidding agent (ADBA) framework selects an auction to participate in and predicts its final price in two phases: the first gives an initial estimation and the second phase delivers a final price prediction. The methodology for initial price estimation finds an auction to compete in and assesses the value of the auctioned item using data mining techniques. It handles the problem of diverse price dynamics in auctions for the same or similar items, using a clustering-based bid mapping and selection approach to locate the auction where participation would give maximum surplus. The value of the item is assessed with parametric and non-parametric machine learning approaches to predict the auction’s closing price. The proposed approach is validated using real online auction datasets. These results demonstrate that this clustering-based price prediction approach outperforms existing methodologies in terms of prediction accuracy. This thesis also introduces a methodology for final price estimation which designs bidding strategies to address buyers’ different bidding behaviours. This draws on two approaches: negotiation decision functions and fuzzy reasoning techniques. The bidding strategies are designed based on the bidder's own attitude to win the auction and the behaviour of rival bidders. A simulated electronic marketplace is implemented and developed using Java Agent DEvelopment Framework (JADE). The marketplace is also used to demonstrate the performance of the bidding strategies. The outcomes for heterogeneous and homogeneous bidders are measured separately in a wide variety of test environments subject to different auction settings and bidding restrictions. The results show that ADBA agents who follow this study’s bidding strategies outperform other existing agents in most settings in terms of their success rate and expected utility

Decentralized Resource Scheduling in Grid/Cloud Computing

In the Grid/Cloud environment, applications or services and resources belong to different organizations with different objectives. Entities in the Grid/Cloud are autonomous and self-interested; however, they are willing to share their resources and services to achieve their individual and collective goals. In such open environment, the scheduling decision is a challenge given the decentralized nature of the environment. Each entity has specific requirements and objectives that need to achieve. In this thesis, we review the Grid/Cloud computing technologies, environment characteristics and structure and indicate the challenges within the resource scheduling. We capture the Grid/Cloud scheduling model based on the complete requirement of the environment. We further create a mapping between the Grid/Cloud scheduling problem and the combinatorial allocation problem and propose an adequate economic-based optimization model based on the characteristic and the structure nature of the Grid/Cloud. By adequacy, we mean that a comprehensive view of required properties of the Grid/Cloud is captured. We utilize the captured properties and propose a bidding language that is expressive where entities have the ability to specify any set of preferences in the Grid/Cloud and simple as entities have the ability to express structured preferences directly. We propose a winner determination model and mechanism that utilizes the proposed bidding language and finds a scheduling solution. Our proposed approach integrates concepts and principles of mechanism design and classical scheduling theory. Furthermore, we argue that in such open environment privacy concerns by nature is part of the requirement in the Grid/Cloud. Hence, any scheduling decision within the Grid/Cloud computing environment is to incorporate the feasibility of privacy protection of an entity. Each entity has specific requirements in terms of scheduling and privacy preferences. We analyze the privacy problem in the Grid/Cloud computing environment and propose an economic based model and solution architecture that provides a scheduling solution given privacy concerns in the Grid/Cloud. Finally, as a demonstration of the applicability of the approach, we apply our solution by integrating with Globus toolkit (a well adopted tool to enable Grid/Cloud computing environment). We also, created simulation experimental results to capture the economic and time efficiency of the proposed solution

Agent-based simulation of electricity markets: a literature review

Liberalisation, climate policy and promotion of renewable energy are challenges to players of the electricity sector in many countries. Policy makers have to consider issues like market power, bounded rationality of players and the appearance of fluctuating energy sources in order to provide adequate legislation. Furthermore the interactions between markets and environmental policy instruments become an issue of increasing importance. A promising approach for the scientific analysis of these developments is the field of agent-based simulation. The goal of this article is to provide an overview of the current work applying this methodology to the analysis of electricity markets. --