Amir Danak

Bio: Amir Danak is an academic researcher from McGill University. The author has contributed to research in topics: Bidding & Common value auction. The author has an hindex of 4, co-authored 6 publications receiving 61 citations.

Resource Allocation with Supply Adjustment in Distributed Computing Systems

Abstract: We present two resource-allocation mechanisms for on-demand computing services in parallel and distributed systems, where users pay for their actual usage of the computational resources. We specialize our solution for allocation of grid resources which is a challenging issue due to the dynamic behavior of the system. The problem is studied from the seller's point of view and adjustment of the market capacity is proposed as his strategic move to maximize his profit from resource sharing.

Efficient Bidding in Dynamic Grid Markets

Abstract: We analyze rational strategies of users in a dynamic grid market. We consider efficient usage of the shared resources in modeling users' preference relations, an objective that prevents congestion and consequently the collapse of the grid system. A repeated auction-based allocation protocol is presented for sharing the computational grid resources. We present a utility-maximizing bidding algorithm and illustrate the transient and long-term attitudes of users in an equilibrium of the dynamic resource-allocation game. It is shown that efficient bidding improves the long-term profits of the grid users.

Bidding efficiently in repeated auctions with entry and observation costs

Abstract: This paper introduces an efficient bidding strategy for budget-constrained buyers in repeated auctions with entry fees. We present a general algorithm that is applicable to distributed resource allocation. The game is modeled on an economically reasonable assumption [1] according to which any player can participate in an auction after paying for information about the value of the auctioned item, and for the preparation of his bid. We address learning by each bidder of an optimal participation strategy for spending his budget profitably, based on the history of his successes and failures in past transactions. Players' transient and long-term attitudes are illustrated in a symmetric Bayesian equilibrium of a market-based network resource allocation problem.

A Robust Learning Approach to Repeated Auctions With Monitoring and Entry Fees

Abstract: In this paper, we present a strategic bidding framework for repeated auctions with monitoring and entry fees. We motivate and formally define the desired properties of our framework and present a recursive bidding algorithm, according to which buyers learn to avoid submitting bids in stages where they have a relatively low chance of winning the auctioned item. The proposed bidding strategies are computationally simple as players do not need to recompute the sequential strategies from the data collected to date. Pursuing the proposed efficient bidding (EB) algorithm, players monitor their relative performance in the course of the game and submit their bids based on their current estimate of the market condition. We prove the stability and robustness of the proposed strategies and show that they dominate myopic and random bidding strategies using an experiment in search engine marketing.

Approximately optimal bidding policies for repeated first-price auctions

Abstract: We suggest approximately optimal bidding strategies for games, where similar items are auctioned repeatedly. Considering players’ bounded rationality in practice, the results can further motivate the application of market-based approaches in resource allocation problems. We specialize the bidding algorithm for first-price payment schemes, the building blocks of several simplified selling mechanisms, that are common in practice.

Lecture notes in business information processing

Abstract: LNBIP reports state-of-the-art results in areas related to business information systems and industrial application software development – timely, at a high level, and in both printed and electronic form. The type of material published includes • Proceedings (published in time for the respective event) • Postproceedings (consisting of thoroughly revised and/or extended final papers) • Other edited monographs (such as, for example, project reports or invited volumes) • Tutorials (coherently integrated collections of lectures given at advanced courses, seminars, schools, etc.) • Award-winning or exceptional theses LNBIP is abstracted/indexed in DBLP, EI and Scopus. LNBIP volumes are also submitted for the inclusion in ISI Proceedings.

Estimation of a Dynamic Auction Game

Abstract: This paper proposes an estimation method for a repeated auction game under the presence of capacity contraints The estimation strategy is computationally simple as it does not require solving for the equilibrium of the game It uses a two stage approach In the first stage the distribution of bids conditional on state variables is estimated using data on bids, bidder characteristics and contract characteristics In the second stage, an expression of the expected sum of future profits based on the distribution of bids is obtained, and costs are inferred based on the first order condition of optimal bids We apply the estimation method to repeated highway construction procurement auctions in the state of California between May 1996 and May 1999 In this market, previously won uncompleted contracts reduce the probability of winning further contracts We quantify the effect of intertemporal constraints on bidders' costs and on bids Due to the intertemporal effect and also to bidder asymmetry, the auction can be inefficient Based on the estimates of costs, we quantify efficiency losses

A Truthful Dynamic Workflow Scheduling Mechanism for Commercial Multicloud Environments

Abstract: The ultimate goal of cloud providers by providing resources is increasing their revenues. This goal leads to a selfish behavior that negatively affects the users of a commercial multicloud environment. In this paper, we introduce a pricing model and a truthful mechanism for scheduling single tasks considering two objectives: monetary cost and completion time. With respect to the social cost of the mechanism, i.e., minimizing the completion time and monetary cost, we extend the mechanism for dynamic scheduling of scientific workflows. We theoretically analyze the truthfulness and the efficiency of the mechanism and present extensive experimental results showing significant impact of the selfish behavior of the cloud providers on the efficiency of the whole system. The experiments conducted using real-world and synthetic workflow applications demonstrate that our solutions dominate in most cases the Pareto-optimal solutions estimated by two classical multiobjective evolutionary algorithms.

Component Ranking for Fault-Tolerant Cloud Applications

Abstract: Cloud computing is becoming a mainstream aspect of information technology. More and more enterprises deploy their software systems in the cloud environment. The cloud applications are usually large scale and include a lot of distributed cloud components. Building highly reliable cloud applications is a challenging and critical research problem. To attack this challenge, we propose a component ranking framework, named FTCloud, for building fault-tolerant cloud applications. FTCloud includes two ranking algorithms. The first algorithm employs component invocation structures and invocation frequencies for making significant component ranking. The second ranking algorithm systematically fuses the system structure information as well as the application designers' wisdom to identify the significant components in a cloud application. After the component ranking phase, an algorithm is proposed to automatically determine an optimal fault-tolerance strategy for the significant cloud components. The experimental results show that by tolerating faults of a small part of the most significant components, the reliability of cloud applications can be greatly improved.

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images.

Abstract: This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.