4,921 research outputs found
Peer-to-peer and community-based markets: A comprehensive review
The advent of more proactive consumers, the so-called "prosumers", with
production and storage capabilities, is empowering the consumers and bringing
new opportunities and challenges to the operation of power systems in a market
environment. Recently, a novel proposal for the design and operation of
electricity markets has emerged: these so-called peer-to-peer (P2P) electricity
markets conceptually allow the prosumers to directly share their electrical
energy and investment. Such P2P markets rely on a consumer-centric and
bottom-up perspective by giving the opportunity to consumers to freely choose
the way they are to source their electric energy. A community can also be
formed by prosumers who want to collaborate, or in terms of operational energy
management. This paper contributes with an overview of these new P2P markets
that starts with the motivation, challenges, market designs moving to the
potential future developments in this field, providing recommendations while
considering a test-case
Challenges for the comprehensive management of cloud services in a PaaS framework
The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its lifecycle management, a one stop shop for Cloud services and a PaaS level resource management featuring elasticity. 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud-aware immigrant technologies
Co-creative expertise: Auran games and fury: A case study
This article discusses the ways in which the relations among professional and non-professional participants in co-creative relations are being reconfigured as part of the shift from a closed industrial paradigm of expertise toward open and distributed expertise networks. This article draws on ethnographic consultancy research undertaken throughout 2007 with Auran Games, a Brisbane, Australia based games developer, to explore the co-creative relationships between professional developers and gamers. This research followed and informed Auranâs online community management and social networking strategies for Fury (http://unleashthefury.com), a massively multiplayer online game released in October 2007. This paper argues that these co-creative forms of expertise involve co-ordinating expertises through social-network markets
Integration of decentralized economic models for resource self-management in application layer networks
Resource allocation is one of the challenges for self-management of large scale distributed applications running in a dynamic and heterogeneous environment. Considering Application Layer Networks (ALN) as a general term for such applications including computational Grids, Content Distribution Networks and P2P applications, the characteristics of the ALNs and the environment preclude an efficient resource allocation by a central instance. The approach we propose integrates ideas from decentralized economic models into the architecture of a resource allocation middleware, which allows the scalability towards the participant number and the robustness in very dynamic environments. At the same time, the pursuit of the participants for their individual goals should benefit the global optimization of the application. In this work, we describe the components of this middleware architecture and introduce an ongoing prototype.Peer Reviewe
Critical Management Issues for Implementing RFID in Supply Chain Management
The benefits of radio frequency identification (RFID) technology in the supply chain are fairly compelling. It has the potential to revolutionise the efficiency, accuracy and security of the supply chain with significant impact on overall profitability. A number of companies are actively involved in testing and adopting this technology. It is estimated that the market for RFID products and services will increase significantly in the next few years. Despite this trend, there are major impediments to RFID adoption in supply chain. While RFID systems have been around for several decades, the technology for supply chain management is still emerging. We describe many of the challenges, setbacks and barriers facing RFID implementations in supply chains, discuss the critical issues for management and offer some suggestions. In the process, we take an in-depth look at cost, technology, standards, privacy and security and business process reengineering related issues surrounding RFID technology in supply chains
Consensus-based approach to peer-to-peer electricity markets with product differentiation
With the sustained deployment of distributed generation capacities and the
more proactive role of consumers, power systems and their operation are
drifting away from a conventional top-down hierarchical structure. Electricity
market structures, however, have not yet embraced that evolution. Respecting
the high-dimensional, distributed and dynamic nature of modern power systems
would translate to designing peer-to-peer markets or, at least, to using such
an underlying decentralized structure to enable a bottom-up approach to future
electricity markets. A peer-to-peer market structure based on a Multi-Bilateral
Economic Dispatch (MBED) formulation is introduced, allowing for
multi-bilateral trading with product differentiation, for instance based on
consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is
described to solve the MBED in fully decentralized manner. A set of realistic
case studies and their analysis allow us showing that such peer-to-peer market
structures can effectively yield market outcomes that are different from
centralized market structures and optimal in terms of respecting consumers
preferences while maximizing social welfare. Additionally, the RCI solving
approach allows for a fully decentralized market clearing which converges with
a negligible optimality gap, with a limited amount of information being shared.Comment: Accepted for publication in IEEE Transactions on Power System
A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management
On-chain data (metrics) of blockchain networks, akin to company fundamentals,
provide crucial and comprehensive insights into the networks. Despite their
informative nature, on-chain data have not been utilized in reinforcement
learning (RL)-based systems for cryptocurrency (crypto) portfolio management
(PM). An intriguing subject is the extent to which the utilization of on-chain
data can enhance an RL-based system's return performance compared to baselines.
Therefore, in this study, we propose CryptoRLPM, a novel RL-based system
incorporating on-chain data for end-to-end crypto PM. CryptoRLPM consists of
five units, spanning from information comprehension to trading order execution.
In CryptoRLPM, the on-chain data are tested and specified for each crypto to
solve the issue of ineffectiveness of metrics. Moreover, the scalable nature of
CryptoRLPM allows changes in the portfolios' cryptos at any time. Backtesting
results on three portfolios indicate that CryptoRLPM outperforms all the
baselines in terms of accumulated rate of return (ARR), daily rate of return
(DRR), and Sortino ratio (SR). Particularly, when compared to Bitcoin,
CryptoRLPM enhances the ARR, DRR, and SR by at least 83.14%, 0.5603%, and
2.1767 respectively
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Self-organizing peer-to-peer social networks
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.National Natural Science of Foundation of Chin
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