Dynamic Pricing for Maximizing Cloud Revenue

Alzhouri, Fadi; Agarwal, Anjali; Liu, Yan; Bataineh, Ahmed Saleh

doi:10.1145/3007748.3007756

Cited by 5 publications

(6 citation statements)

References 18 publications

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“…Correspondingly, to solve such an integer linear programming (ILP) problem, particularly in a way that sustains large packages, the CA unit employs a column generation approach, as was done in [4] and [22]. More specifically, a branch and bound technique is adopted.…”

Section: A Capacity Estimationmentioning

confidence: 99%

“…If the newly proposed patterns improve the objective underlying the master problem, they will be appended to the pattern set as a new column. Interested readers are referred to [4] and [23] for more details on the aforementioned column generation technique. Algorithm 1 illustrates the detailed steps taken by the CE Unit to achieve its purpose.…”

Section: A Capacity Estimationmentioning

confidence: 99%

“…Whereas, Google initially offers Preemptible VMs for batch and fault-tolerant jobs, before terminating running instances when underlying resources are needed [3]. [4] identified the necessities and objectives that drive cloud providers to adopt a spot pricing strategy. The authors concluded that these companies offer VM instances for a limited time at a dynamic price, depending on the state of cloud resources and regardless of claims that they provide cost-saving services to customers.…”

Section: Introductionmentioning

confidence: 99%

“…• We characterized the relationships between VM virtualization and migration on one hand and between VM virtualization and VM spot markets on the other hand. Identifying and analyzing these relationships contributes to enhancing spot pricing schemes, such as those adopted by [4] and [7].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: A Capacity Estimationmentioning

confidence: 99%

Section: A Capacity Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Resource Management for Cloud Spot Markets

et al. 2020

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“…Service providers in the IoS could implement their own pricing mechanism, which may involve simple static pricing to advanced dynamic policies where prices may e.g. vary (even at small time scale) according to the actual demand [4]. The involvement of third-party and cloud services in making up a composite service in these dynamic and competitive environments (with all involved parties striving for maximization of their own profit) raises challenging questions that are new, even though one can learn from the past.…”

Section: Introductionmentioning

confidence: 99%

State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services

Mei

Berg²,

Ганчев

et al. 2018

Lecture Notes in Computer Science

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The explosive growth of the Internet has fundamentally changed the global society. The emergence of concepts like service-oriented architecture (SOA), Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Network as a Service (NaaS) and Cloud Computing in general has catalyzed the migration from the information-oriented Internet into an Internet of Services (IoS). This has opened up virtually unbounded possibilities for the creation of new and innovative services that facilitate business processes and improve the quality of life. However, this also calls for new approaches to ensuring quality and reliability of these services. The goal of this book chapter is to first analyze the state-of-the-art in the area of autonomous control for a reliable IoS and then to identify the main research challenges within it. A general background and high-level description of the current state of knowledge is presented. Then, for each of the three subareas, namely the autonomous management and real-time control, methods and tools for monitoring and service prediction, and smart pricing and competition in multi-domain systems, a brief general introduction and background are presented, and a list of key research challenges is formulated. Keywords: Internet of Services (IoS) Á Autonomous control Autonomous management Á Service monitoring Á Service prediction Smart pricing 3 Autonomous Management and Real-Time Control On a fundamental level, the working group WG1, associated with this research sub-area, was primarily concerned with the management and control of networks, services, applications, and compositions of services or applications. Of particular interest were management and control techniques that span multiple levels, e.g., the network and service level.

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A Novel Dynamic Pricing Approach for Preemptible Cloud Services

Peng,

Cheng

2023

IEEE Access

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Dynamic pricing for preemptible cloud services (DPPCS) is highly demanded to effectively utilize the excess capacity in cloud computing. However, the dynamic nature of excess capacity exhibits high non-stationarity, which is characterized by multi-temporal stochastic patterns with time-varying statistical properties. The non-stationarity results in the DPPCS problem being a Non-Stationary Markov Decision Process (NSMDP) with unknown transition probabilities. Moreover, DPPCS is constrained by a certain maximum preemption rate, further complicating the DPPCS problem as a Constrained NSMDP (CNSMDP). We transform the CNSMDP into a piecewise Lagrangian dual model, which converts the CNSMDP into an unconstrained optimization problem. To solve the above problem, we propose a novel Q-Learning approach for DPPCS. We first present estimation methods for the unknown environment parameters, including a detection method for identifying temporal pattern changes, and a diffusion approximation method for estimating the actual preemption rate. Then, we introduce a Lagrange multiplier updating method, which can strike a balance between revenue and the preemption rate in the reward function. Building upon the above methods, we develop a Constrained Non-Stationary Q-Learning (CNSQL) algorithm for DPPCS, which dynamically adjusts its learning process to adapt to the multi-temporal patterns. Through simulated experiments, we demonstrate the effectiveness of our proposed approach compared to state-of-the-art algorithms. It performs well in improving revenue generated from excess capacity while maintaining the actual preemption rate within the specified constraint.

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Dynamic Pricing for Maximizing Cloud Revenue

Cited by 5 publications

References 18 publications

Dynamic Resource Management for Cloud Spot Markets

Dynamic Resource Management for Cloud Spot Markets

State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services

A Novel Dynamic Pricing Approach for Preemptible Cloud Services

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