335 research outputs found

    Resource Management in Large-scale Systems

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    The focus of this thesis is resource management in large-scale systems. Our primary concerns are energy management and practical principles for self-organization and self-management. The main contributions of our work are: 1. Models. We proposed several models for different aspects of resource management, e.g., energy-aware load balancing and application scaling for the cloud ecosystem, hierarchical architecture model for self-organizing and self-manageable systems and a new cloud delivery model based on auction-driven self-organization approach. 2. Algorithms. We also proposed several different algorithms for the models described above. Algorithms such as coalition formation, combinatorial auctions and clustering algorithm for scale-free organizations of scale-free networks. 3. Evaluation. Eventually we conducted different evaluations for the proposed models and algorithms in order to verify them. All the simulations reported in this thesis had been carried out on different instances and services of Amazon Web Services (AWS). All of these modules will be discussed in detail in the following chapters respectively

    LAND MARKETS IN AGENT BASED MODELS OF STRUCTURAL CHANGE

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    Replaced with revised version of paper 02/22/08.Land Economics/Use, Research Methods/ Statistical Methods,

    Right Place, Right Time:Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty

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    For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models

    Ecosystem services auctions: the last decade of research

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    ReviewAuctions offer potential cost-effectiveness improvements over other mechanisms for payments for ecosystem services (PES) contract allocation. However, evidence-based guidance for matching design to application is scarce and research priorities are unclear. To take stock of the current state of the art, we conducted a systematic review and thematic content analysis of 56 peer-reviewed journal articles discussing ES auctions published in the last decade. Auctions were approached from three overlapping perspectives: mechanism design, PES, and policy analysis. Five major themes emerged: (1) performance, including measures like cost-effectiveness and PES criteria like additionality; (2) information dynamics like price discovery and communication effects; (3) design innovations like risk-integrating and spatially coordinated mechanisms; (4) contextual variables like policy context and cultural values; and (5) participation factors. Additional attention from policymakers and continued efforts to coordinate research in this diverse and interdisciplinary subfield may be beneficialinfo:eu-repo/semantics/publishedVersio

    Auction design and auction outcomes

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    We study the impact of spectrum auction design on the prices paid by telecommunications operators for two decades across 85 countries. Our empirical strategy combines information about competition in the local market, the level of adoption and a wide range of socio-economic indicators and process specific variables. Using a micro dataset of almost every mobile spectrum auction performed so far—both regional and national—we show that auction design affects final prices paid. Two designs (SMRA with augmented switching and CCA with core pricing) result in auctions with systematically higher normalized returns. Further, we document that spectrum ownership appears to affect prices paid in subsequent auctions. We discuss the mechanisms of cost minimization and foreclosure faced by operators in different regulatory environments. Our findings have implications for policy-makers and regulators

    Auction Protocols for Decentralized Scheduling

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    Scheduling is the problem of allocating resources to alternate possible uses over designated periods of time. Several have proposed (and some have tried) market-based approaches to decentralized versions of the problem, where the competing uses are represented by autonomous agents. Market mechanisms use prices derived through distributed bidding protocols to determine an allocation, and thus solve the scheduling problem. To analyze the behavior of market schemes, we formalize decentralized scheduling as a discrete resource allocation problem, and bring to bear some relevant economic concepts. Drawing on results from the literature, we discuss the existence of equilibrium prices for some general classes of scheduling problems, and the quality of equilibrium solutions. To remedy the potential nonexistence of price equilibria due to complementarity in preference, we introduce additional markets in combinations of basic goods. We present some auction mechanisms and bidding protocols corresponding to the two market structures, and analyze their computational and economic properties. Finally, we consider direct revelation mechanisms, and compare to the market-based approach.http://deepblue.lib.umich.edu/bitstream/2027.42/50443/1/gebfinal.pd
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