Dynamic Pricing Mechanism Research Articles

GT-WGS: an efficient and economic tool for large-scale WGS analyses based on the AWS cloud service

BackgroundWhole-genome sequencing (WGS) plays an increasingly important role in clinical practice and public health. Due to the big data size, WGS data analysis is usually compute-intensive and IO-intensive. Currently it usually takes 30 to 40 h to finish a 50× WGS analysis task, which is far from the ideal speed required by the industry. Furthermore, the high-end infrastructure required by WGS computing is costly in terms of time and money. In this paper, we aim to improve the time efficiency of WGS analysis and minimize the cost by elastic cloud computing.ResultsWe developed a distributed system, GT-WGS, for large-scale WGS analyses utilizing the Amazon Web Services (AWS). Our system won the first prize on the Wind and Cloud challenge held by Genomics and Cloud Technology Alliance conference (GCTA) committee. The system makes full use of the dynamic pricing mechanism of AWS. We evaluate the performance of GT-WGS with a 55× WGS dataset (400GB fastq) provided by the GCTA 2017 competition. In the best case, it only took 18.4 min to finish the analysis and the AWS cost of the whole process is only 16.5 US dollars. The accuracy of GT-WGS is 99.9% consistent with that of the Genome Analysis Toolkit (GATK) best practice. We also evaluated the performance of GT-WGS performance on a real-world dataset provided by the XiangYa hospital, which consists of 5× whole-genome dataset with 500 samples, and on average GT-WGS managed to finish one 5× WGS analysis task in 2.4 min at a cost of $3.6.ConclusionsWGS is already playing an important role in guiding therapeutic intervention. However, its application is limited by the time cost and computing cost. GT-WGS excelled as an efficient and affordable WGS analyses tool to address this problem. The demo video and supplementary materials of GT-WGS can be accessed at https://github.com/Genetalks/wgs_analysis_demo.

A Shapley-Value Mechanism for Bandwidth On Demand between Datacenters

Recent studies in cloud resource allocation and pricing have focused on computing and storage resources but not network bandwidth. Cloud users nowadays customarily deploy services across multiple geo-distributed datacenters, with significant inter-datacenter traffic generated, paid by cloud providers to ISPs. An effective bandwidth allocation and charging mechanism is needed between the cloud provider and the cloud users. Existing volume based static charging schemes lack market efficiency. This work presents the first dynamic pricing mechanism for inter-datacenter on-demand bandwidth, via a Shapley value based auction. Our auction is expressive enough to accept bids as a flat bandwidth rate plus a time duration, or a data volume with a transfer deadline. We start with an offline auction, design an optimal end-to-end traffic scheduling approach, and exploit the Shapley value in computing payments. Our auction is truthful, individual rational, budget balanced and approximately efficient in social welfare. An online version of the auction follows, where decisions are made instantly upon the arrival of each user's realtime transmission demand. We propose an efficient online traffic scheduling algorithm, and approximate the offline Shapley value based payments on the fly. We validate our mechanism design with solid theoretical analysis, as well as trace-driven simulation studies.

Asymmetric Dynamic Price Mechanism for Symmetric Buyers

We investigate a finite-horizon dynamic pricing problem of a seller under limited commitment. Even when the buyers are ex-ante symmetric to the seller, the seller can charge different prices to different buyers. We show that under the class of posted-price mechanisms this asymmetric treatment of symmetric buyers strictly revenue-dominates symmetric treatment. The seller im- plements this by using a priority-based deterministic tie-breaking rule instead of using a random tie-breaking rule. The effect of asymmetric treatment on revenue increment increases monotonically as we increase the time horizon of the game.

Pricing of perishable products with a speculator and strategic customers

ABSTRACTThis study develops a two-stage dynamic pricing strategy for the retailer based on the consideration of the number of myopic consumers, the reservation prices of bargain-hunting consumers, the deferral purchases of strategic customers, and the arbitrage behavior of the speculator with an aim to maximize the revenue from selling such a perishable product. Likewise, upon learning the retailer’s price, the speculator and strategic consumers would develop their own purchasing strategies accordingly. A backward induction procedure is employed to analyze the related decisions regarding the retailer and the speculator. This study deals with the competitive condition by the use of game theory to all players. According to the results of the numerical analyses, there are two major findings. First, more strategic consumers would result in more deferral purchases, and more revenue is anticipated by the speculator on condition that the retailer had sold out his inventory on hand in the primary selling stage. Second, the speculator acts as a dynamic pricing mechanism in the market, and is able to reduce the retailer’s risk under specific conditions, and thus the existence of a speculator seems harmless and profitable for the retailer.

A Comprehensive Model With Fast Solver for Optimal Energy Scheduling in RTP Environment

In the smart grid environment, demand side management makes it possible to encourage consumers participating more actively via proper pricing schemes. In this paper, we analyze previous real-time pricing models and then provide a comprehensive model. A new criterion for designing real-time pricing models is proposed, which can guarantee that the optimal solution obtained from centralized algorithms is the same as that from the distributed algorithms. Furthermore, a fast distributed dual gradient algorithm is proposed to achieve the optimal solution. Compared with the widely used distributed dual sub-gradient algorithm, theoretically, the proposed one does not only accelerate the convergence rate, but also overcome the possible non-convergence during iteration process, which is a demerit in the traditional method. This new solver is of great importance in the application of dynamic pricing mechanism due to real-time requirement. More specifically, this new algorithm can largely reduce the information exchange between the energy provider and its customers, making the proposed method more practical. The simulations also validate its effectiveness and efficiency in solving real-time pricing problems.

Economic Levers for Mitigating Interest Flooding Attack in Named Data Networking

As a kind of unwelcome, unavoidable, and malicious behavior, distributed denial of service (DDoS) is an ongoing issue in today’s Internet as well as in some newly conceived future Internet architectures. Recently, a first step was made towards assessing DDoS attacks in Named Data Networking (NDN)—one of the promising Internet architectures in the upcoming big data era. Among them, interest flooding attack (IFA) becomes one of the main serious problems. Enlightened by the extensive study on the possibility of mitigating DDoS in today’s Internet by employing micropayments, in this paper we address the possibility of introducing economic levers, say, dynamic pricing mechanism, and so forth, for regulating IFA in NDN.

Marginal Costs for District Heating

Abstract District Heating (DH) is facing great challenges on raising production efficiency and reducing production cost in order to improve its competence. A novel pricing mechanism is an effective instrument to promote the sustainable development of DH systems. Based on the data on the daily dynamic operation of DH systems, this work calculated the practical marginal costs of a DH system, which can serve as the basis for developing a new dynamic pricing mechanism. Two methods, namely setting the price of electricity and entropy drop method were used to allocate the fuel costs in order to calculate the variable cost of heat production. Results show that the dynamics of marginal costs can indicate the increase in the variable costs with the increase in heat production. The calculated marginal costs were further compared with real heat prices. It was found that although heat prices varied with temperature in general, it could not reflect the changes in the technology of heat production, and therefore, could not represent the practical production costs.

Consideration of the Impacts of a Smart Neighborhood Load on Transformer Aging

Smart grid solutions with enabling technologies such as energy management systems (EMSs) and smart meters promote the vision of smart households, which also allows for active demand side in the residential sector. These technologies enable the control of residential consumption, local small-scale generation, and energy storage systems to respond to time-varying prices. However, shifting loads simultaneously to lower price periods is likely to put extra stress on distribution system assets such as distribution transformers. Especially, additional new types of loads/appliances such as electric vehicles (EVs) can introduce even more burden on the operation of these assets, which is an issue that needs special attention. Such extra stress can cause accelerated aging of distribution system assets and significantly affect the reliability of the system. In this paper, the impact of a smart neighborhood load on distribution transformer aging is investigated. The EMS of each household is designed to respond to prices and other signals emitted by the responsive load serving entity within the relevant demand response strategy. An optimization framework based on mixed-integer linear programming is presented in order to define the EMS structure. Then, the equivalent aging of the distribution transformer is examined with a thermal model under different scenarios. The case studies that are presented indicate that the integration of EVs in residential premises may indeed cause accelerated aging of the distribution transformers, while the need to investigate the efficiency of dynamic pricing mechanisms is rendered evident.

Demand Side Management Using Time of Use and Elasticity Price

Demand Side Management (DSM) has been traditionally seen as a mean of reducing peak electricity demand so that utilities can delay building further capacity. In fact, by reducing the overall load on an electricity network, DSM has various beneficial effects, including mitigating electrical system emergencies, reducing the number of blackouts and increasing system reliability. In this paper we propose a methodology to evaluate DSM in electric distribution systems, based on elasticity and time of use pricing concepts. This evaluation is performed through an optimal power flow model considering linear constraints. The results of the proposed method depend, to a great extent, on two parameters: the maximum share of load which consumers are able to shift and the elasticities used to express the consumer's level of participation in the dynamic pricing mechanism. Simulation results confirm the benefits of Time of Use Demand Side Management reducing the peak-to-average ratio of the total power demand, as well as each user's individual daily electricity charges. Demand Side Management (DSM) has been traditionally seen as a mean of reducing peak electricity demand so that utilities can delay building further capacity. In fact, by reducing the overall load on an electricity network, DSM has various beneficial effects, including mitigating electrical system emergencies, reducing the number of blackouts and increasing system reliability. In this paper we propose a methodology to evaluate DSM in electric distribution systems, based on elasticity and time of use pricing concepts. This evaluation is performed through an optimal power flow model considering linear constraints. The results of the proposed method depend, to a great extent, on two parameters: the maximum share of load which consumers are able to shift and the elasticities used to express the consumer's level of participation in the dynamic pricing mechanism. Simulation results confirm the benefits of Time of Use Demand Side Management reducing the peak-to-average ratio of the total power demand, as well as each user's individual daily electricity charges.

Dynamic Pricing for Cloud Service Negotiation

Big players in cloud service market like Microsoft, Amazon and Google keeps changing their service price. Increasing cloud service offerings have motivated cloud providers to develop cloud service marketplace such as AWS, Google Apps and Cloud Surfing. However, these marketplaces lack dynamic pricing mechanism. The idea of dynamic pricing in the cloud services market has emerged in recent years and there is a need to design and implement an appropriate pricing model for managing the service demand and capacity. This paper reviews dynamic pricing implementation in cloud services and similar areas. This paper reviews recent concurrent cloud SLA negotiation frameworks. A concurrent cloud SLA negotiation mechanism with adaptive pricing is proposed. The proposed mechanism is designed to address multiplicity, information sharing and complexity issues in current frameworks. © 2016 American Scientific Publishers. All rights reserved.

An Axiomatic Framework for Ex-Ante Dynamic Pricing Mechanisms in Smart Grid

In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes. We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously. We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities.

Research of Charging(Discharging) Orderly and Optimizing Load Curve for Electric Vehicles Based on Dynamic Electric Price and V2G

Firstly, using the Monte Carlo method and simulation analysis, this paper builds models for the behaviour of electric vehicles, the conventional charging model and the fast charging model. Secondly, this paper studies the impact that the number of electric vehicles which get access to power grid has on the daily load curve. Then, the paper put forwards a dynamic pricing mechanism of electricity, and studies how this dynamic pricing mechanism guides the electric vehicles to charge orderly. Last but not the least, the paper presents a V2G mechanism. Under this mechanism, electric vehicles can charge orderly and take part in the peak shaving. Research finds that massive electric vehicles’ access to the power grid will increase the peak-valley difference of daily load curve. Dynamic pricing mechanism and V2G mechanism can effectively lead the electric vehicles to take part in peak-shaving, and optimize the daily load curve.

The Development of Cloud Computing Service Pricing—Based on the Life Cycle Theory

The application of cloud computing is conducive to the efficient allocation of resources and cost reduction. With the vigorous development of cloud technology, more and more enterprises have involved in cloud services, so the competition among providers is increasingly fierce. How to formulate a reasonable price strategy is one of the current matters urgently needing solved for the long term benefit of the market. Only by establishing the good pricing strategy can enterprises better seize market and build a stable customer relationship. Based on the concept of cloud computing, this paper makes a brief comment on the study of the literature of the three types of pricing mechanisms in the business life cycle of cloud computing. It is pointed out that dynamic pricing can better meet the interests of both sides of supply and demand and become the main trend of future pricing. Future research needs to study the characteristics of the cloud service life cycle from the cloud services industry. Consider the long-term benefits of customers and suppliers, and develop dynamic pricing mechanism.

SipaaS: Spot instance pricing as a Service framework and its implementation in OpenStack

SummaryDesigning dynamic pricing mechanisms that efficiently price resources in line with a provider's profit maximization goal is a key challenge in cloud computing environments. Despite the large volume of research published on this topic, there is no publicly available software system implementing dynamic pricing for Infrastructure as a Service cloud spot markets. This paper presents the implementation of a framework called Spot instance pricing as a Service (SipaaS) that supports an auction mechanism to price and allocate virtual machine instances. SipaaS is an open‐source project offering a set of web services to price and sell virtual machine instances in a spot market resembling the Amazon EC2 spot instances. Cloud providers, who aim at utilizing SipaaS, should install add‐ons in their existing platform to make use of the framework. As an instance, we provide an extension to the Horizon – the OpenStack dashboard project – to employ SipaaS web services and to add a spot market environment to OpenStack. To validate and evaluate the system, we conducted an experimental study with a group of 10 users utilizing the provided spot market in a real environment. Results show that the system performs reliably in a practical test environment. Copyright © 2015 John Wiley & Sons, Ltd.

Dynamic pricing of excess capacity in production networks by fuzzy logic

The research concerns a make to order manufacturing environment and two classes of customers who submit orders: core customers (long-term partnership) and short-term customers. The orders submitted by the short-term customers depend on the price and service level proposed by the firm. This research proposes a pricing policy based on a fuzzy tool to set the price for short-term customers in production networks. The fuzzy tool captures the state of the manufacturing system in terms of workload of the manufacturing system, reliability and demand fluctuations. The fuzzy tool developed supports the cooperation mechanism among the enterprises of the production network. A discrete event simulation environment is used to test the proposed approach in a static and dynamic environment. The numerical results are compared to the case without cooperation among the firms within the production network. The simulation results show how the proposed approach outperforms the other policies in all conditions tested. The combination of fuzzy dynamic price and cooperation mechanism improves the performance with low movements of orders among the partners of the network; this means that the transportation costs have a low impact.

Adaptive Tactical Pricing in Multi‐Agent Supply Chain Markets Using Economic Regimes

ABSTRACTIn today's complex and dynamic supply chain markets, information systems are essential for effective supply chain management. Complex decision making processes on strategic, tactical, and operational levels require substantial timely support in order to contribute to organizations' agility. Consequently, there is a need for sophisticated dynamic product pricing mechanisms that can adapt quickly to changing market conditions and competitors' strategies. We propose a two‐layered machine learning approach to compute tactical pricing decisions in real time. The first layer estimates prevailing economic conditions—economic regimes—identifying and predicting current and future market conditions. In the second layer, we train a neural network for each regime to estimate price distributions in real time using available information. The neural networks compute offer acceptance probabilities from a tactical perspective to meet desired sales quotas. We validate our approach in the trading agent competition for supply chain management. When competing against the world's leading agents, the performance of our system significantly improves compared to using only economic regimes to predict prices. Profits increase significantly even though the prices and sales volume do not change significantly. Instead, tactical pricing results in a more efficient sales strategy by reducing both finished goods and components inventory costs.

Cloud Market Maker: An automated dynamic pricing marketplace for cloud users

Cloud providers commonly incur heavy upfront set up costs which remain almost constant whether they serve a single or many customers. In order to generate a return on this investment, a suitable pricing strategy is required by providers. Established industries such as the airlines employ dynamic pricing to maximize their revenues. In order to increase their resource utilization rates, cloud providers could also use dynamic pricing for their services. At present however most providers use static schemes for pricing their resources. This work presents a new dynamic pricing mechanism for cloud providers. Furthermore, at present no platform exists that provides a dynamic unified view of the different cloud offerings in real-time. Due to a rapidly changing landscape and a limited knowledge of the cloud marketplace, consumers can often end up choosing a cloud provider that is more expensive or does not give them what they really need. This is because some providers spend significantly on advertising their services online. In order to assist cloud customers in the selection of a suitable resource and cloud providers in implementing dynamic pricing, this work describes an automated dynamic pricing marketplace and a decision support system for cloud users. We present a multi-agent multi-auction based system through which such services are delivered. An evaluation has been carried out to determine how effectively the Cloud Market Maker selects the resource, dynamically adjusts the price for the cloud users and the suitability of dynamic pricing for the cloud environment.

Dynamic pricing in subsidized performing arts

While current pricing strategies in public theaters are mostly based on static schemes, this paper analyzes the potential of dynamic pricing. Subsidized performing arts organizations face the challenge of selling a fixed capacity (available seats of a venue) to a broad and varied audience. Based on the literature and on empirical evidence from a survey among Swiss theaters, we discuss whether and to what extent yield management and uniform‐price auctions—two widespread dynamic pricing mechanisms in the Internet age—can be applied to public theaters in view of the particular goals and targets imposed by their source of funding and by subsidy contracts. Our analysis suggests that although the application of yield management may not be advantageous in the current environment, the introduction of dynamic elements (including auctions) to prevailing pricing strategies can help public theaters respond to temporal demand fluctuations and fill up performances with a varied audience. Copyright © 2015 John Wiley & Sons, Ltd.

Pricing of fluctuations in electricity markets

In an electric power system, demand fluctuations may result in significant ancillary cost to suppliers. Furthermore, in the near future, deep penetration of volatile renewable electricity generation is expected to exacerbate the variability of demand on conventional thermal generating units. We address this issue by explicitly modeling the ancillary cost associated with demand variability. We argue that a time-varying price equal to the suppliers’ instantaneous marginal cost may not achieve social optimality, and that consumer demand fluctuations should be properly priced. We propose a dynamic pricing mechanism that explicitly encourages consumers to adapt their consumption so as to offset the variability of demand on conventional units. Through a dynamic game-theoretic formulation, we show that (under suitable convexity assumptions) the proposed pricing mechanism achieves social optimality asymptotically, as the number of consumers increases to infinity. Numerical results demonstrate that compared with marginal cost pricing, the proposed mechanism creates a stronger incentive for consumers to shift their peak load, and therefore has the potential to reduce the need for long-term investment in peaking plants.

D2P: Distributed Dynamic Pricing Policyin Smart Grid for PHEVs Management

Future large-scale deployment of plug-in hybrid electric vehicles (PHEVs) will render massive energy demand on the electric grid during peak-hours. We propose an intelligent distributed dynamic pricing (D2P) mechanism for the charging of PHEVs in a smart grid architecture-an effort towards optimizing the energy consumption profile of PHEVs users. Each micro-grid decides realtime dynamic price as home-price and roaming-price, depending on the supply-demand curve, to optimize its revenue. Consequently, two types of energy services are considered-home micro-grid energy, and foreign micro-grid energy. After designing the PHEVs' mobility and battery models, the pricing policies for the home-price and the roaming-price are presented. A decision making process to implement a cost-effective charging and discharging method for PHEVs is also demonstrated based on the real-time price decided by the micro-grids. We evaluate and compare the results of distributed pricing policy with other existing centralized/distributed ones. Simulation results show that using the proposed architecture, the utility corresponding to the PHEVs increases by approximately 34 percent over that of the existing ones for optimal charging of PHEVs.