Dynamic Pricing Scheme for Service Providers under Multi Server Cloud Environment

A Rajesh, T Seenivasan

Abstract


Cloud computing is used to share resources under data sources and computational applications. Hardware, software and information are provided in cloud environment. Cloud resources and services are provided in two methods federated and commercial cloud models. Mutual resource and service sharing is performed under the federated cloud model. Pricing schemes are used in commercial clouds. Commercial clouds are constructed with infrastructure vendors, service providers, and consumers. An infrastructure vendor maintains basic hardware and software facilities. A service provider rents resources from the infrastructure vendors, builds appropriate multiserver systems and provides various services to users. A consumer submits a service request to a service provider, receives the desired result from the service provider with certain service-level agreement.

Pricing model of a service provider in cloud computing is based on two components, income and cost. In a service provider the income is the service charge to users and the cost is the renting cost plus the utility cost paid to infrastructure vendors. Service charge and business cost factors are used to maximize the profit for a service provider. Multiserver configuration, Service Level Agreement (SLA), service and application load properties are used to assign service costs. Consumer satisfaction, Quality of Service (QoS) and penalty parameters are used to decide the service costs. Renting cost, energy cost and service provider margin are also used for the service cost estimation process. Multiserver system is treated as M/M/m queuing model. Server speed and power consumption strategy is divide into two models such as idle- speed model and the constant-speed model. The weighting time of a service request is derived using the probability density function.

The service pricing model is improved to manage on demand, reservation, peak demand and peek supply situations. Data usage cost and communication cost metrics are added to the service charge functions. Dynamic service function selection model is integrated with the system. Service request and access levels are analyzed to estimate the profit level of the service provider.

Keywords


DVFS, Virtual Batching, Request Batching, Server Consolidation CPU resource allocation.

References


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