Maximizing Cloud Revenue using Dynamic Pricing of Multiple Class Virtual Machines

Alzhouri, Fadi; Agarwal, Anjali; Liu, Yan

doi:10.1109/tcc.2018.2878023

Cited by 16 publications

(11 citation statements)

References 33 publications

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“…Our scenario consists on a distributed provision of Software as a Service (SaaS) for educational platforms in the public administration of Andalusia (the biggest region of Spain in terms of size and population), which have to be dynamically 1 A landing page with supplementary material including our prototype and the comprehensive comparative analysis is available at https://isa-group. github.io/2020-10-billing-lifecycle/ deployed and scaled up to support online teaching needs.…”

Section: A Motivating Scenariomentioning

confidence: 99%

“…special rate when network traffic egress between zones in the same region) they do not provide a way to specify compensations or discounts as defined in our proposal. Other recent works are focusing on defining pricing models considering resources allocation optimisation mechanisms, as in [1], where Alzhouri et al investigate a dynamic pricing for re-using stagnant virtual machines. Specifically, their approach manages multiple classes of virtual machines in order to achieve the maximum expected revenue within a finite discrete time horizon.…”

Section: A On Pricing Modelsmentioning

confidence: 99%

“…Within the highly dynamic and evolving cloud computing market, a proper service pricing and billing life cycle definition is crucial for providers and consumers alike. On the one hand, providers need to customise their service offerings, taking into account several aspects such as their own infrastructure, the target market, incurred costs, discount policies, purchasing options, and the actual billing process life cycle, which can vary from fixed billing during a period of time to a real-time, fluctuating price based on demandand-supply chains [1]- [3]. On the other hand, customers face an ever growing number of service offerings to choose from, where they need to analyse which are the optimal options with respect to their requirements and budget, as well as carefully monitor that the billing conforms to the Customer Agreement (CA) and especially to its associated Service Level Agreement (SLA) [4], [5], i.e.…”

Section: Introductionmentioning

confidence: 99%

“…We validate our solution in Sec. IV, applying it to the motivating scenario, evaluating its performance, and also comparing some tools which can be used to support billing 1 . We discuss in Sec.…”

Section: Introductionmentioning

confidence: 99%

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A Flexible Billing Life Cycle for Cloud Services Using Augmented Customer Agreements

et al. 2021

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Cloud computing constant evolution requires dynamic adjustments to service pricing and billing terms, considering provider infrastructures, customer requirements, and discount policies, among others. In this context, Customer Agreements (CA) are used to regulate the service provision including, among other information, the agreed service level in SLAs, pricing, and billing terms. Although many existing proposals and industrial tools support the definition of pricing and billing of cloud services, there is a lack in terms of customisation and automated monitoring tools integrated with the billing process. In this article, we provide a flexible billing proposal supporting CA that considers not only SLA terms but also customisable pricing and billing terms, possibly including compensations (i.e. discounts or overcharges) that apply when specified conditions are met. In addition, we developed an automated monitoring and analysis tool that we validate in a real-world industrial scenario. Moreover, we analyse and compare more than 50 existing industrial tools with our proposal, highlighting the advantages of its rule-based approach.

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Section: A Motivating Scenariomentioning

confidence: 99%

Section: A On Pricing Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Flexible Billing Life Cycle for Cloud Services Using Augmented Customer Agreements

et al. 2021

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“…Nevertheless, the SPS unit scales the price up and down on the grounds of supply and demand. To accomplish this task, we employed the dynamic pricing scheme from [25] to control prices over the horizon; the time look ahead is one hour. The dynamic pricing scheme is formulated as a finite horizon stochastic Markov decision process.…”

Section: E Spot Price Schemementioning

confidence: 99%

Dynamic Resource Management for Cloud Spot Markets

et al. 2020

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Resource management for cloud computing environments that are characterized by many layers emerges as a critical task for cloud computing providers. Such providers are compelled by the demands and strategies of stochastic customers to adopt dynamic resource management for the top-bottom scaling of the cloud resources on the basis of variable needs. Resource management in the infrastructure as a service layer relies on virtual machine (VM) characteristics, such as estimated VM classes. Given that a cloud provider offers a variety of VM classes that differ as regards the size of computing resources (e.g., central processing unit, memory, and input/output devices), optimizing cloud resources to maximize cloud revenue is a challenging dilemma. More specifically, the dynamic management of resources in cloud spot markets is confronted with various severe obstacles. In consideration of these issues, this study investigated a dynamic resource management model for cloud spot markets and put forward an efficient model that manages spare resources for the purpose of expanding cloud revenue. The model estimates the available spare capacity of a spot market, evaluates the maximum expected revenue of stagnant VMs on the basis estimated cumulative capacity, and locates the optimum VM combinations that bear complementary workloads and capacities and can coexist in a certain host. Our model also improves the understanding of cloud resource scaling and generates inferences that can be adopted in managing cloud resources for all layers as well as Reserved and On-Demand markets.

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A deep reinforcement learning-based approach for pricing in the competing auction-based cloud market

Shi

Huang

Shi

2022

SOCA

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Maximizing Cloud Revenue using Dynamic Pricing of Multiple Class Virtual Machines

Cited by 16 publications

References 33 publications

A Flexible Billing Life Cycle for Cloud Services Using Augmented Customer Agreements

A Flexible Billing Life Cycle for Cloud Services Using Augmented Customer Agreements

Dynamic Resource Management for Cloud Spot Markets

A deep reinforcement learning-based approach for pricing in the competing auction-based cloud market

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