11,850 research outputs found
Privacy Management and Optimal Pricing in People-Centric Sensing
With the emerging sensing technologies such as mobile crowdsensing and
Internet of Things (IoT), people-centric data can be efficiently collected and
used for analytics and optimization purposes. This data is typically required
to develop and render people-centric services. In this paper, we address the
privacy implication, optimal pricing, and bundling of people-centric services.
We first define the inverse correlation between the service quality and privacy
level from data analytics perspectives. We then present the profit maximization
models of selling standalone, complementary, and substitute services.
Specifically, the closed-form solutions of the optimal privacy level and
subscription fee are derived to maximize the gross profit of service providers.
For interrelated people-centric services, we show that cooperation by service
bundling of complementary services is profitable compared to the separate sales
but detrimental for substitutes. We also show that the market value of a
service bundle is correlated with the degree of contingency between the
interrelated services. Finally, we incorporate the profit sharing models from
game theory for dividing the bundling profit among the cooperative service
providers.Comment: 16 page
Spectrum sharing models in cognitive radio networks
Spectrum scarcity demands thinking new ways to
manage the distribution of radio frequency bands so that its use is more effective. The emerging technology that can enable this paradigm shift is the cognitive radio. Different models for
organizing and managing cognitive radios have emerged, all with specific strategic purposes. In this article we review the allocation spectrum patterns of cognitive radio networks and
analyse which are the common basis of each model.We expose the vulnerabilities and open challenges that still threaten the adoption
and exploitation of cognitive radios for open civil networks.L'escassetat de demandes d'espectre fan pensar en noves formes de gestionar la distribució de les bandes de freqüència de ràdio perquè el seu ús sigui més efectiu. La tecnologia emergent que pot permetre aquest canvi de paradigma és la ràdio cognitiva. Han sorgit diferents models d'organització i gestió de les ràdios cognitives, tots amb determinats fins estratègics. En aquest article es revisen els patrons d'assignació de l'espectre de les xarxes de ràdio cognitiva i s'analitzen quals són la base comuna de cada model. S'exposen les vulnerabilitats i els desafiaments oberts que segueixen amenaçant l'adopció i l'explotació de les ràdios cognitives per obrir les xarxes civils.La escasez de demandas de espectro hacen pensar en nuevas formas de gestionar la distribución de las bandas de frecuencia de radio para que su uso sea más efectivo. La tecnología emergente que puede permitir este cambio de paradigma es la radio cognitiva. Han surgido diferentes modelos de organización y gestión de las radios cognitivas, todos con determinados fines estratégicos. En este artículo se revisan los patrones de asignación del espectro de las redes de radio cognitiva y se analizan cuales son la base común de cada modelo. Se exponen las vulnerabilidades y los desafíos abiertos que siguen amenazando la adopción y la explotación de las radios cognitivas para abrir las redes civiles
A Study of Competitive Cloud Resource Pricing under a Smart Grid Environment
In the current IaaS cloud market, to achieve profit maximization, multiple cloud providers compete non-cooperatively by offering diverse price rates. At the same time, tenant consumers judiciously adjust demands accordingly, which in turn affects cloud resource prices. In this paper, we tackle this fundamental but daunting cloud price competition problem with Bertrand game modeling, and propose a dynamic game to achieve Nash equilibrium in a distributed manner. Specifically, we realistically consider spot electricity prices under a smart grid environment, and systematically investigate the impact of different system parameters such as network delay, renewable availability, and cloud resource substitutability. We also perform stability analysis to investigate the convergence of the proposed dynamic game to Nash equilibrium. Cooperation among cloud providers can achieve aggregate cloud profit maximization, but is subject to strategic manipulations. We then propose our Striker strategy to stimulate cooperation, the efficiency of which is validated by repeated game analysis. Our evaluation is augmented with realistic electricity prices in the spot energy market, and reveals insightful observations for both theoretic analysis and practical pricing scheme design.published_or_final_versio
A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques
Cloud computing as a fairly new commercial paradigm, widely investigated by
different researchers, already has a great range of challenges. Pricing is a
major problem in Cloud computing marketplace; as providers are competing to
attract more customers without knowing the pricing policies of each other. To
overcome this lack of knowledge, we model their competition by an
incomplete-information game. Considering the issue, this work proposes a
pricing policy related to the regret minimization algorithm and applies it to
the considered incomplete-information game. Based on the competition based
marketplace of the Cloud, providers update the distribution of their strategies
using the experienced regret. The idea of iteratively applying the algorithm
for updating probabilities of strategies causes the regret get minimized
faster. The experimental results show much more increase in profits of the
providers in comparison with other pricing policies. Besides, the efficiency of
a variety of regret minimization techniques in a simulated marketplace of Cloud
are discussed which have not been observed in the studied literature. Moreover,
return on investment of providers in considered organizations is studied and
promising results appeared
Economics of Spot Instance Service: A Two-stage Dynamic Game Apporach
This paper presents the economic impacts of spot instance service on the
cloud service providers (CSPs) and the customers when the CSPs offer it along
with the on-demand instance service to the customers. We model the interaction
between CSPs and customers as a non-cooperative two-stage dynamic game. Our
equilibrium analysis reveals (i) the techno-economic interrelationship between
the customers' heterogeneity, resource availability, and CSPs' pricing policy,
and (ii) the impacts of the customers' service selection (spot vs. on-demand)
and the CSPs' pricing decision on the CSPs' market share and revenue, as well
as the customers' utility. The key technical challenges lie in, first, how we
capture the strategic interactions between CSPs and customers, and second, how
we consider the various practical aspects of cloud services, such as
heterogeneity of customers' willingness to pay for the quality of service (QoS)
and the fluctuating resource availability. The main contribution of this paper
is to provide CSPs and customers with a better understanding of the economic
impact caused by a certain price policy for the spot service when the
equilibrium price, which from our two-stage dynamic game analysis, is able to
set as the baseline price for their spot service
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