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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Cloud Customer's Historical Record Based Resource Pricing
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Cloud Customer's Historical Record Based Resource Pricing

机译：云客户基于历史记录的资源定价

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摘要

Media content in its digital form has been rapidly scaling up, resulting in popularity gain of cloud computing. Cloud computing makes it easy to manage the vastly increasing digital content. Moreover, additional features like, omnipresent access, further service creation, discovery of services, and resource management also play an important role in this regard. The forthcoming era is interoperability of multiple clouds, known as cloud federation or inter-cloud computing. With cloud federation, services would be provided through two or more clouds. Once matured and standardized, inter-cloud computing is supposed to provide services which would be more scalable, better managed, and efficient. Such tasks are provided through a middleware entity called cloud broker. A broker is responsible for reserving resources, managing them, discovering services according to customer's demands, Service Level Agreement (SLA) negotiation, and match-making between the involved service provider and the customer. So far existing studies discuss brokerage in a narrow focused way. In the research outcome presented in this paper, we provide a holistic brokerage model to manage on-demand and advance service reservation, pricing, and reimbursement. A unique feature of this study is that we have considered dynamic management of customer's characteristics and historical record in evaluating the economics related factors. Additionally, a mechanism of incentive and penalties is provided, which helps in trust build-up for the customers and service providers, prevention of resource underutilization, and profit gain for the involved entities. For practical implications, the framework is modeled on Amazon Elastic Compute Cloud (EC2) On-Demand and Reserved Instances service pricing. For certain features required in the model, data was gathered from Google Cluster trace.
机译：数字形式的媒体内容已迅速扩大规模，从而导致了云计算的普及。云计算使管理大量增加的数字内容变得容易。此外，无所不在的访问，进一步的服务创建，服务发现和资源管理等其他功能在这方面也起着重要作用。即将到来的时代是多个云的互操作性，称为云联合或云间计算。使用云联合，将通过两个或更多云提供服务。一旦成熟和标准化，云间计算应该提供的服务将具有更高的可扩展性，更好的管理和效率。此类任务是通过称为云代理的中间件实体提供的。经纪人负责保留资源，管理资源，根据客户需求发现服务，服务水平协议（SLA）协商以及相关服务提供商与客户之间的匹配。到目前为止，现有研究以狭窄的重点讨论经纪业务。在本文提出的研究成果中，我们提供了一个整体经纪模型来管理按需和提前服务预订，定价和报销。这项研究的独特之处在于我们在评估经济相关因素时考虑了对客户特征和历史记录的动态管理。此外，还提供了一种激励和惩罚机制，有助于建立客户和服务提供商的信任关系，防止资源利用不足，并为所涉及的实体带来利润。出于实际考虑，该框架以Amazon Elastic Compute Cloud（EC2）按需和预留实例服务价格为模型。对于模型所需的某些功能，数据是从Google Cluster跟踪中收集的。

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