114,678 research outputs found
Seamless Service Provisioning for Mobile Crowdsensing: Towards Integrating Forward and Spot Trading Markets
The challenge of exchanging and processing of big data over Mobile
Crowdsensing (MCS) networks calls for the new design of responsive and seamless
service provisioning as well as proper incentive mechanisms. Although
conventional onsite spot trading of resources based on real-time network
conditions and decisions can facilitate the data sharing over MCS networks, it
often suffers from prohibitively long service provisioning delays and
unavoidable trading failures due to its reliance on timely analysis of complex
and dynamic MCS environments. These limitations motivate us to investigate an
integrated forward and spot trading mechanism (iFAST), which entails a new
hybrid service trading protocol over the MCS network architecture. In iFAST,
the sellers (i.e., mobile users with sensing resources) can provide long-term
or temporary sensing services to the buyers (i.e., sensing task owners). iFast
enables signing long-term contracts in advance of future transactions through a
forward trading mode, via analyzing historical statistics of the market, for
which the notion of overbooking is introduced and promoted. iFAST further
enables the buyers with unsatisfying service quality to recruit temporary
sellers through a spot trading mode, upon considering the current
market/network conditions. We analyze the fundamental blocks of iFAST, and
provide a case study to demonstrate its superior performance as compared to
existing methods. Finally, future research directions on reliable service
provisioning for next-generation MCS networks are summarized
Modelling and trading the Greek stock market with gene expression and genetic programing algorithms
This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of
their customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the
Cloud computing providers are unable to predict geographic distribution of
users consuming their services, hence the load coordination must happen
automatically, and distribution of services must change in response to changes
in the load. To counter this problem, we advocate creation of federated Cloud
computing environment (InterCloud) that facilitates just-in-time,
opportunistic, and scalable provisioning of application services, consistently
achieving QoS targets under variable workload, resource and network conditions.
The overall goal is to create a computing environment that supports dynamic
expansion or contraction of capabilities (VMs, services, storage, and database)
for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across
multiple vendor clouds. We have validated our approach by conducting a set of
rigorous performance evaluation study using the CloudSim toolkit. The results
demonstrate that federated Cloud computing model has immense potential as it
offers significant performance gains as regards to response time and cost
saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape
Climate-Related Investing Across Asset Classes
Responsible investment -- understood as the incorporation of environmental, social, and governance (ESG) information into investment analysis -- is a discipline that allows investors to:- Better assess long-term risks and opportunities in their portfolios; and- Better align their investment strategies with opportunities to create longterm wealth for investors and society alike.It is a tool for investors who seek to improve long-term financial returns through enhanced ESG analysis. It also appeals to mission or impact investors, who seek to achieve defined social and/or environmental goals while achieving targeted rates of return. In both cases, investors use responsible investment as a tool to improve their ability to achieve their goals.Climate change is among the most important issues addressed by today's responsible investment universe. The physical risks of climate change, the likelihood of major changes in political and regulatory investment environments as a result of climate change, the opportunities associated with a radical global transformation to a low-carbon economy -- these issues create far-reaching implications for investors as they make decisions about their investment strategies, and as they evaluate particular fund managers and investment opportunities. New ideas, products, and methods have entered the market to address the long-term implications of climate change.This short handbook takes as its premise that a climate lens reveals risks and opportunities across all elements of an investor's portfolio. Every asset class offers investors an opportunity to pursue climate-friendly investments, to mitigate exposure to climate risk, and to engage stakeholders to improve climate-related performance across the range of investment opportunities
Explorations in Evolutionary Design of Online Auction Market Mechanisms
This paper describes the use of a genetic algorithm (GA) to find optimal parameter-values for trading agents that operate in virtual online auction “e-marketplaces”, where the rules of those marketplaces are also under simultaneous control of the GA. The aim is to use the GA to automatically design new mechanisms for agent-based e-marketplaces that are more efficient than online markets designed by (or populated by) humans. The space of possible auction-types explored by the GA includes the Continuous Double Auction (CDA) mechanism (as used in most of the world’s financial exchanges), and also two purely one-sided mechanisms. Surprisingly, the GA did not always settle on the CDA as an optimum. Instead, novel hybrid auction mechanisms were evolved, which are unlike any existing market mechanisms. In this paper we show that, when the market supply and demand schedules undergo sudden “shock” changes partway through the evaluation process, two-sided hybrid market mechanisms can evolve which may be unlike any human-designed auction and yet may also be significantly more efficient than any human designed market mechanism
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