Cloud service selection is becoming more complex with the arrival of a large number of cloud providers offering various service packages on the market. These cloud service packages are generally provisioned by Spot, On-demand and Reserved Instances. Typically, a user's service requirements contain many independent sub-tasks (Bag-of-Tasks), and have budget limitations and additional constraints. To select reasonable cloud instances to run the user's sub-tasks, we propose a strategy, OSPN (Optimal Service Provisioning with Negotiation), to support the allocation of tasks to services offered by multi-cloud providers. OSPN consists of two phases: in the first phase, a one-to-many parallel Spot Instance pricing negotiation is applied; in the second phase, service provisioning strategy profiles on the three types of cloud instances are calculated. Specifically, the first phase employs an improved double auction in which the price and availability of providers' instances are taken into account; then the second phase gives the utility Nash equilibrium model and derives the optimal provisioning strategy profiles. The experimental results show that our service provisioning strategy is more cost-effective, namely, the most gains of both the user and providers in the changing scenes, and the least payments of the user than the existing relevant strategies.

中文翻译：

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OSPN：具有协商任务包应用程序的最佳服务供应

随着市场上提供各种服务包的大量云提供商的到来，云服务选择变得越来越复杂。这些云服务包通常由 Spot、按需和预留实例提供。通常，用户的服务需求包含许多独立的子任务（Bag-of-Tasks），并且有预算限制和附加约束。为了选择合理的云实例来运行用户的子任务，我们提出了一种策略 OSPN（Optimal Service Provisioning with Negotiation），以支持将任务分配给多云提供商提供的服务。OSPN 包括两个阶段：在第一阶段，应用一对多并行 Spot 实例定价协商；在第二阶段，计算了三种类型的云实例上的服务供应策略配置文件。具体而言，第一阶段采用改进的双重拍卖，其中考虑了供应商实例的价格和可用性；然后第二阶段给出效用纳什均衡模型并推导出最优供应策略配置文件。实验结果表明，与现有相关策略相比，我们的服务提供策略更具成本效益，即在变化的场景中用户和提供者的收益最大，用户支付的费用最少。然后第二阶段给出效用纳什均衡模型并推导出最优供应策略配置文件。实验结果表明，与现有相关策略相比，我们的服务提供策略更具成本效益，即在变化的场景中用户和提供者的收益最大，用户支付的费用最少。然后第二阶段给出效用纳什均衡模型并推导出最优供应策略配置文件。实验结果表明，与现有相关策略相比，我们的服务提供策略更具成本效益，即在变化的场景中用户和提供者的收益最大，用户支付的费用最少。