Profit Renting Schema for cloud Service Providers in Cloud Computing

Cloud computing is a classification of web computing and with respect to request computing where shared assets and data are given to the client's on-request. Profit is the most critical variable from the cloud service provider and it is essentially dictated by the setup of a cloud profit stage under given market request. A solitary long haul leasing plan is generally used to design a cloud stage, which can't ensure the quality of administration however prompts to genuine asset squander. To beat the disadvantages of single leasing plan, Double asset RR Renting plan is composed which is the blend of both here and now and long haul leasing. Twofold asset leasing plan ensures the quality of administration as well as lessen the asset squander. In which queuing model is utilized for occupation booking. Twofold asset leasing RR conspire not just gives the Qos to the clients by utilizing load adjusting round robin calculation additionally expand profit than single leasing plan. Thirdly, a profit intensification issue is anticipated the twofold leasing arrangement and the streamlined course of action of a cloud stage is gotten by dealing with the profit help issue. Finally, a movement of calculations coordinated to break down the profit of our proposed arrange with that of the single leasing arrangement. The results exhibit that our arrangement can't simply guarantee the organization way of all requesting, furthermore get more profit than the last

Profit Aware Load Balancing for Distributed Cloud Data Centers

Abstract—The advent of cloud systems has spurred the emergence of an impressive assortment of Internet services. Recent pressures on enhancing the profitability by curtailing surging dollar costs on energy have posed challenges to, as well as placed a new emphasis on, designing energy-efficient request dispatching and resource management algorithms. What further adds to the design challenge is the highly diverse nature of Internet service requests in terms of Quality-of-Service (QoS) constraints and business values. Nonetheless, most of the existing job scheduling and resource management solutions are for a single type of request and are profit oblivious. They are unable to reap the benefit of multi-service profit-aware algorithm designs. In this paper, we consider a cloud service provider operating geographically distributed data centers in a multi-electricitymarket environment, and propose an energy-efficient, profit- and cost-aware request dispatching and resource allocation algorithm to maximize a service provider’s net profit. We formulate the net profit maximization issue as a constrained optimization problem, using a unified task model capturing multiple cloud layers (e.g., SaaS, PaaS, IaaS.) The proposed approach maximizes a service provider’s net profit by judiciously distributing service requests to data centers, powering on/off an appropriate number of servers, and allocating server resources to dispatched requests. We conduct extensive experiments to validate our proposed algorithm. Results show that our proposed approach can improve a service provider’s net profit significantly. I

AUGURES : profit-aware web infrastructure management

Over the last decade, advances in technology together with the increasing use of the Internet for everyday tasks, are causing profound changes in end-users, as well as in businesses and technology providers. The widespread adoption of high-speed and ubiquitous Internet access, is also changing the way users interact with Web applications and their expectations in terms of Quality-of-Service (QoS) and User eXperience (UX). Recently, Cloud computing has been rapidly adopted to host and manage Web applications, due to its inherent cost effectiveness and on-demand scaling of infrastructures. However, system administrators still need to make manual decisions about the parameters that affect the business results of their applications ie., setting QoS targets and defining metrics for scaling the number of servers during the day. Therefore, understanding the workload and user behavior ¿the demand, poses new challenges for capacity planning and scalability ¿the supply, and ultimately for the success of a Web site. This thesis contributes to the current state-of-art of Web infrastructure management by providing: i) a methodology for predicting Web session revenue; ii) a methodology to determine high response time effect on sales; and iii) a policy for profit-aware resource management, that relates server capacity, to QoS, and sales. The approach leverages Machine Learning (ML) techniques on custom, real-life datasets from an Ecommerce retailer featuring popular Web applications. Where the experimentation shows how user behavior and server performance models can be built from offline information, to determine how demand and supply relations work as resources are consumed. Producing in this way, economical metrics that are consumed by profit-aware policies, that allow the self-configuration of cloud infrastructures to an optimal number of servers under a variety of conditions. While at the same time, the thesis, provides several insights applicable for improving Autonomic infrastructure management and the profitability of Ecommerce applications.Durante la última década, avances en tecnología junto al incremento de uso de Internet, están causando cambios en los usuarios finales, así como también a las empresas y proveedores de tecnología. La adopción masiva del acceso ubicuo a Internet de alta velocidad, crea cambios en la forma de interacción con las aplicaciones Web y en las expectativas de los usuarios en relación de calidad de servicio (QoS) y experiencia de usuario (UX) ofrecidas. Recientemente, el modelo de computación Cloud ha sido adoptado rápidamente para albergar y gestionar aplicaciones Web, debido a su inherente efectividad en costos y servidores bajo demanda. Sin embargo, los administradores de sistema aún tienen que tomar decisiones manuales con respecto a los parámetros de ejecución que afectan a los resultados de negocio p.ej. definir objetivos de QoS y métricas para escalar en número de servidores. Por estos motivos, entender la carga y el comportamiento de usuario (la demanda), pone nuevos desafíos a la planificación de capacidad y escalabilidad (el suministro), y finalmente el éxito de un sitio Web.Esta tesis contribuye al estado del arte actual en gestión de infraestructuras Web presentado: i) una metodología para predecir los beneficios de una sesión Web; ii) una metodología para determinar el efecto de tiempos de respuesta altos en las ventas; y iii) una política para la gestión de recursos basada en beneficios, al relacionar la capacidad de los servidores, QoS, y ventas. La propuesta se basa en aplicar técnicas Machine Learning (ML) a fuentes de datos de producción de un proveedor de Ecommerce, que ofrece aplicaciones Web populares. Donde los experimentos realizados muestran cómo modelos de comportamiento de usuario y de rendimiento de servidor pueden obtenerse de datos históricos; con el fin de determinar la relación entre la demanda y el suministro, según se utilizan los recursos. Produciendo así, métricas económicas que son luego aplicadas en políticas basadas en beneficios, para permitir la auto-configuración de infraestructuras Cloud a un número adecuado de servidores. Mientras que al mismo tiempo, la tesis provee información relevante para mejorar la gestión de infraestructuras Web de forma autónoma y aumentar los beneficios en aplicaciones de Ecommerce