In cloud computing, service providers rent heterogeneous servers from cloud providers, i.e., Infrastructure as a Service (IaaS), to meet requests of consumers. The heterogeneity of servers and impatience of consumers pose great challenges to service providers for profit maximization. In this article, we transform this problem into a multi-queue model where the optimal expected response time of each queue is theoretically analyzed. A multi-queue request scheduling algorithm framework is proposed to maximize the total profit of service providers, which consists of three components: request stream splitting, requests allocation, and server assignment. A request stream splitting algorithm is designed to split the arriving requests to minimize the response time in the multi-queue system. An allocation algorithm, which adopts a one-step improvement strategy, is developed to further optimize the response time of the requests. Furthermore, an algorithm is developed to determine the appropriate number of required servers of each queue. After statistically calibrating parameters and algorithm components over a comprehensive set of random instances, the proposed algorithms are compared with the state-of-the-art over both simulated and real-world instances. The results indicate that the proposed multi-queue request scheduling algorithm outperforms the other algorithms with acceptable computational time.

中文翻译：

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用于 IaaS 云中利润最大化的多队列请求调度


在云计算中，服务提供商向云提供商租用异构服务器，即基础设施即服务(IaaS)，以满足消费者的请求。服务器的异构性和消费者的急躁情绪给服务提供商的利润最大化带来了巨大的挑战。在本文中，我们将这个问题转化为多队列模型，从理论上分析了每个队列的最佳期望响应时间。为了最大化服务提供商的总利润，提出了一种多队列请求调度算法框架，该算法框架由三个部分组成：请求流拆分、请求分配和服务器分配。请求流分割算法旨在分割到达的请求，以最小化多队列系统中的响应时间。开发了一种采用一步改进策略的分配算法，以进一步优化请求的响应时间。此外，还开发了一种算法来确定每个队列所需服务器的适当数量。在对一组全面的随机实例进行统计校准参数和算法组件之后，将所提出的算法与模拟实例和真实实例的最新算法进行比较。结果表明，所提出的多队列请求调度算法在可接受的计算时间上优于其他算法。