Optimal service pricing for a cloud cache

Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource-economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental results prove the efficiency of the proposed solution

PRICE DEMAND MODEL FOR A CLOUD CACHE

Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental study shows the efficiency of the solution

A dynamic pricing algorithm for a network of virtual resources

A service chain is a combination of network services (e.g. network address translation (NAT), a firewall, etc.) that are interconnected to support an application (e.g. video-on-demand). Building a service chain requires a set of specialized hardware devices each of which need to be configured with their own command syntax. By moving management functions out of forwarding hardware into controller software, software-defined networking (SDN) simplifies provisioning and reconfiguration of service chains. By moving the network functions out of dedicated hardware devices into software running on standard x86 servers, network function virtualization (NFV) turns the deployment of a service chain into a more (cost)-efficient and flexible process. In an SDN/NFV-based architecture, those service chains are composed of virtual network functions (VNFs) that need to be mapped to physical network components. In literature, several algorithmic approaches exist to do so efficiently and cost-effectively. However, once mapped, a simple revenue model is used for pricing the requested substrate resources. This often leads to a loss of revenue for the infrastructure provider. In this paper, we propose a more advanced, dynamic pricing algorithm for pricing the requested substrate resources. The proposed algorithm increases the infrastructure provider's revenue based on historic data, current infrastructure utilization levels and the pricing of competitors. Our experimental evaluation shows that the proposed algorithm increases the revenue of the infrastructure provider significantly, independent of the average network utilization.Peer Reviewe

Resource Selection in Collaborative Cloud Computing Based on User Preference

ABSTRACT: In many researches they have created one environment with multiple clouds for scalable capabilities. The demand for scalable resources in some applications has been rapidly increased. By understanding the mutual dependencies between resources, utilization of users, user preferences a collaborative cloud computing has been introduced, It can be achieve emphasized efficient management of resources and user satisfaction between distributed resources in Collaborative cloud computing. These which do collaboratively supported by many organizations (Google, Microsoft, Amazon). Collaborative cloud computing (CCC) is next logical step in evolution of cloud and service grid technologies. Here mainly discussing about how resource selection done in collaborative cloud, resource selection based on user preferences, which mostly user preferred resources. This environment is to point out the suitable resources, ability and capability of resources, user requirements, and availability of resources. So that users can choose credible cloud platform

Differential pricing integrated with multi-product, multi-machine, multi-worker cost function for resource service providers in cloud manufacturing

Cloud manufacturing (CMfg) platform offers an open marketplace, where a Resource Service Provider (RSP) can benefit from Price Discrimination (PD) in return for specific services. However, the literature focused mainly on operator-based pricing and overlooked RSP pricing control. Therefore, this study formulates a profit function in which revenue is enhanced by adjusting prices according to customer types, while cost accounting is done by resource allocation based Material Flow Cost Accounting (MFCA) because MFCA provides a comprehensive guideline towards waste minimization. The proposed model is formulated into MINLP problem with multiple factors such as; part types, batch size, part routes, machine types, energy consumption, worker types and material handling cost as well as price sensitive customer behaviour and demand. Further, ANOVA is applied for factors analysis. The results suggests that customer types and demand are positively correlated, while parts, machines, and worker types are negatively correlated with profit. The model is also compared with reference price effect and fixed pricing strategy. Results validate that to benefit from diverse customer behaviour in CMfg, PD along with optimal resource allocation provides an effective solution for profit maximization. Model is also compared with reference price effect and fixed pricing strategy to validate its effectiveness

Optimisation en Temps Réel: Optimiser les Performances des Procédés Chimiques malgré l’Incertitude et les Erreurs de Modélisation

This document summarizes the author’s research activities since 1999. They mainly focus on the modelling, the control and the optimization of processes and contributed to the development of real-time optimization methods (RTO), that is, process optimization methods that are capable of driving a real process to optimal performances despite uncertainty, modeling errors and process disturbances. The main philosophy of these methods is to use available process measurement to correct the nominal model-based inputs. Several methodological contributions have been recently obtained in the fields of RTO via modifier adaptation (RTO-MA) but also in the establishment of sufficient conditions for feasibility and optimality that are applicable to any RTO algorithm. The need for the availabilty of an adequate model has been circumvented by the use of convex model approximations and the applicability of RTO-MA was extended to the optimization of discontinuous processes and to the use use transient information for the optimization of steady-state performances of continuous processes. These techniques have been successfully applied to industrial polymerization reactors, to experimental and industrial fuel cells stacks, to the iterative controller tuning problem and to the simulated production of energy using tethered kites. Research has been also performed in the field of modelling and control of blood sugar concentration for patients with Type I diabetes. Attention was paid to the construction of model-based prediction tools that are consistent with standard therapy