A new revenue maximization model using customized plans in cloud service allocation (Applied on a real company case study)

Cloud computing is emerging as a promising field offering a variety of computing services to end users. These services are offered at different prices using various pricing schemes and techniques. End users will favor the service provider offering the best quality with the lowest price. Therefore, applying a fair pricing model will attract more customers and achieve higher revenues for service providers. This work focuses on a novel dynamic pricing model which is able to satisfy advance users requirements based on normal fixed price model. This paper considers many factors that affect pricing and user satisfaction, such as fairness, QoS, SLA, and more, by highlighting their importance in recent markets and propose a flexible model which tries to utilize all resources to the highest capacity and offers low prices for underutilized resources. The simulated results shows the appropriateness of dynamic pricing for sharing of computing resources, where providers want to have more customers as a managerial decision and even more income in total.Keywords: Cloud Computing; Digital Pricing; Dynamic Pricin

Competitive Cloud Resource Procurements via Cloud Brokerage

In current IaaS cloud markets, tenant consumers non-cooperatively compete for cloud resources via demand quantities, and the service quality is offered in a best effort manner. To better exploit tenant demand correlation, cloud brokerage services provide cloud resource multiplexing so as to earn profits by receiving volume discounts from cloud providers. A fundamental but daunting problem facing a tenant consumer is competitive resource procurements via cloud brokerage. In this paper, we investigate this problem via non-cooperative game modeling. In the static game, to maximize the experienced surplus, tenants judiciously select optimal demand responses given pricing strategies of cloud brokers and complete information of the other tenants' demands. We also derive Nash equilibrium of the non-cooperative game for competitive resource procurements. Performance evaluation on Nash equilibrium reveals insightful observations for both theoretical analysis and practical cloud resource procurements scheme design.published_or_final_versio

Cloud Index Tracking: Enabling Predictable Costs in Cloud Spot Markets

Cloud spot markets rent VMs for a variable price that is typically much lower than the price of on-demand VMs, which makes them attractive for a wide range of large-scale applications. However, applications that run on spot VMs suffer from cost uncertainty, since spot prices fluctuate, in part, based on supply, demand, or both. The difficulty in predicting spot prices affects users and applications: the former cannot effectively plan their IT expenditures, while the latter cannot infer the availability and performance of spot VMs, which are a function of their variable price. To address the problem, we use properties of cloud infrastructure and workloads to show that prices become more stable and predictable as they are aggregated together. We leverage this observation to define an aggregate index price for spot VMs that serves as a reference for what users should expect to pay. We show that, even when the spot prices for individual VMs are volatile, the index price remains stable and predictable. We then introduce cloud index tracking: a migration policy that tracks the index price to ensure applications running on spot VMs incur a predictable cost by migrating to a new spot VM if the current VM's price significantly deviates from the index price.Comment: ACM Symposium on Cloud Computing 201