Individualized Pricing for a Cloud Provider Hosting Interactive Applications

2020 ◽  
Vol 12 (4) ◽  
pp. 130-147
Author(s):  
Hossein Jahandideh ◽  
Julie Ward Drew ◽  
Filippo Balestrieri ◽  
Kevin McCardle
Keyword(s):  
Dynamic Pricing ◽  
Large Scale ◽  
Mobile Apps ◽  
Dynamic Program ◽  
Cloud Services ◽  
Cloud Provider ◽  
Stochastic Dynamic ◽  
Interactive Applications ◽  
Pricing Mechanism ◽  
Stochastic Dynamic Program

We consider a cloud provider that hosts interactive applications, such as mobile apps and online games. Depending on the traffic of users for an application, the provider commits a subset of its resources (hardware capacity) to serve the application. The provider must choose a dynamic pricing mechanism to indirectly select the applications hosted and maximize revenue. We model the provider’s pricing problem as a large-scale stochastic dynamic program. To approach this problem, we propose a tractable approach to enable decomposing the multidimensional stochastic dynamic program into single-dimensional subproblems. We then extend the proposed framework to define an individualized dynamic pricing mechanism for the cloud provider. We present novel upper bounds on the optimal revenue to evaluate the performance of our pricing mechanism. The computational results show that a contract-based model of selling interactive cloud services achieves significantly greater revenue than the prevalent alternative and that our pricing scheme attains near-optimal revenue.

A Stochastic Dynamic Program for Valuing Options on Futures

2013 ◽  
Vol 34 (12) ◽  
pp. 1185-1201 ◽  
Author(s):  
Mohamed A. Ayadi ◽  
Hatem Ben-Ameur ◽  
Tymur Kirillov ◽  
Robert Welch
Keyword(s):  
Dynamic Program ◽  
Stochastic Dynamic ◽  
Stochastic Dynamic Program

scholarly journals Loading-balance relay-selective strategy based on stochastic dynamic program

2018 ◽  
Vol 23 (4) ◽  
pp. 493-500
Author(s):  
Wei Zhao ◽  
Lin Zhao ◽  
Weidong Wu ◽  
Sigen Chen ◽  
Shaohui Sun ◽  
...  
Keyword(s):  
Dynamic Program ◽  
Stochastic Dynamic ◽  
Selective Strategy ◽  
Stochastic Dynamic Program

Dynamic Pricing of Relocating Resources in Large Networks

2020 ◽  
Author(s):  
Santiago R. Balseiro ◽  
David B. Brown ◽  
Chen Chen
Keyword(s):  
Lagrangian Relaxation ◽  
Dynamic Pricing ◽  
Asymptotic Optimality ◽  
Infinite Horizon ◽  
Stochastic Dynamic ◽  
Large Network ◽  
Performance Bound ◽  
Pricing Policy ◽  
Hub And Spoke ◽  
Stochastic Dynamic Program

Motivated by applications in shared vehicle systems, we study dynamic pricing of resources that relocate over a network of locations. Customers with private willingness to pay sequentially request to relocate a resource from one location to another, and a revenue-maximizing service provider sets a price for each request. This problem can be formulated as an infinite-horizon stochastic dynamic program, but it is difficult to solve, as optimal pricing policies may depend on the locations of all resources in the network. We first focus on networks with a hub-and-spoke structure, and we develop a dynamic pricing policy and a performance bound based on a Lagrangian relaxation. This relaxation decomposes the problem over spokes and is thus far easier to solve than the original problem. We analyze the performance of the Lagrangian-based policy and focus on a supply-constrained large network regime in which the number of spokes (n) and the number of resources grow at the same rate. We show that the Lagrangian policy loses no more than O(ln n/n) in performance compared with an optimal policy, thus implying asymptotic optimality as n grows large. We also show that no static policy is asymptotically optimal in the large network regime. Finally, we extend the Lagrangian relaxation to provide upper bounds and policies to general networks with multiple interconnected hubs and spoke-to-spoke connections and to incorporate relocation times. We also examine the performance of the Lagrangian policy and the Lagrangian relaxation bound on some numerical examples, including examples based on data from RideAustin. This paper was accepted by David Simchi-Levi, revenue management and market analytics.

Evaluation of the Dynamic Pricing Model in Cloud

2020 ◽  
Vol 12 (3) ◽  
pp. 20-29
Author(s):  
Meetu Kandpal ◽  
Kalyani Ashesh Patel
Keyword(s):  
Dynamic Pricing ◽  
Consumer Behaviour ◽  
Cloud Services ◽  
The Internet ◽  
Cloud Provider ◽  
Pricing Model ◽  
For Profit ◽  
Pricing Policies ◽  
Dynamic Resource ◽  
Profit Maximisation

Many of the companies started moving towards cloud computing because of its characteristics like pay as you go, easy to use, scalable, multi-tenant, secure, etc. There are many cloud providers providing cloud services over the internet. There are number of pricing policies for the user. These pricing policies can be categorised as static and dynamic pricing. This paper represents the enhancement and evaluation of the proposed dynamic resource pricing model for cloud resources based on the consumer behaviour. For model evaluation, one tail two sample t-test has been used. The results show the model will be helpful to the cloud provider for profit maximisation.

Individualized Pricing for a Cloud Provider Hosting Interactive Applications

2019 ◽  
Author(s):  
Hossein Jahandideh ◽  
Kevin F. McCardle ◽  
Julie Drew ◽  
Filippo Balestrieri
Keyword(s):  
Cloud Provider ◽  
Interactive Applications

scholarly journals Networking for Cloud Robotics: The DewROS Platform and Its Application

2021 ◽  
Vol 10 (2) ◽  
pp. 34
Author(s):  
Alessio Botta ◽  
Jonathan Cacace ◽  
Riccardo De Vivo ◽  
Bruno Siciliano ◽  
Giorgio Ventre
Keyword(s):  
Response Time ◽  
Large Data ◽  
Cloud Services ◽  
Cloud Provider ◽  
Human Beings ◽  
Minimal Impact ◽  
Network Connection ◽  
Cloud Robotics ◽  
Networking Technologies ◽  
The Impact

With the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System (ROS) which embodies the three-layer, Dew-Robotics architecture, where computation and storage can be distributed among the robot, the network devices close to it, and the Cloud. After presenting the design and implementation of DewROS, we show its application in a real use-case called SHERPA, which foresees a mixed ground and aerial robotic platform for search and rescue in an alpine environment. We used DewROS to analyze the video acquired by the drones in the Cloud and quickly spot signs of human beings in danger. We perform a wide experimental evaluation using different network technologies and Cloud services from Google and Amazon. We evaluated the impact of several variables on the performance of the system. Our results show that, for example, the video length has a minimal impact on the response time with respect to the video size. In addition, we show that the response time depends on the Round Trip Time (RTT) of the network connection when the video is already loaded into the Cloud provider side. Finally, we present a model of the annotation time that considers the RTT of the connection used to reach the Cloud, discussing results and insights into how to improve current Cloud Robotics applications.

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