Latency to end-users and regulatory requirements push cloud providers to operate many datacenters all around the globe to host their cloud services. An emerging problem under such geo-distributed architecture is to assign each user request to an appropriate datacenter to benefit both cloud providers (e.g., low bandwidth cost) and end-users (e.g., low latency)-known as request allocation. However, prior request allocation solutions have significant limitations: they either focus only on optimizing the benefits for one entity (e.g., providers or users), or ignore some practical yet indispensable factors (e.g., heterogeneous latency requirements of different users and diverse per unit bandwidth cost among different datacenters) when optimizing benefits for both entities. In this paper, we study the problem of minimizing the total bandwidth cost for cloud service providers while guaranteeing the latency requirement for end-users. Specifically, we formulate an integer programming with consideration of the diversities in both the delay of requests and per unit bandwidth cost of datacenters. To efficiently and practically solve this problem, we first relax the integer programming into a continuous convex optimization and then take the advantages of random sampling to enforce the solution to be a feasible one for the original integer programming. We have conducted rigorous theoretical analysis to prove that our algorithm can provide a considerable good competitive ratio. Extensive simulations demonstrate that our proposed algorithm can reduce the total bandwidth cost by 30% while guaranteeing the latency requirements of all requests, as compared to conventional methods.

中文翻译：

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延迟限制的成本最小化的地理分布云服务请求分配

最终用户的延迟和法规要求迫使云提供商在全球范围内运营许多数据中心来托管其云服务。在这种地理分布式架构下出现的一个新问题是将每个用户请求分配给适当的数据中心，以使云提供商（例如，低带宽成本）和最终用户（例如，低延迟）都受益，这被称为请求分配。但是，先前的请求分配解决方案有很大的局限性：它们要么只专注于优化一个实体（例如提供者或用户）的收益，要么忽略一些实际但必不可少的因素（例如不同用户的异构等待时间要求和每单位带宽的差异）优化两个实体的收益时，不同数据中心之间的成本）。在本文中，我们研究了在确保最终用户延迟要求的同时最大程度地降低云服务提供商的总带宽成本的问题。具体来说，我们在考虑请求延迟和数据中心每单位带宽成本的多样性的基础上制定了整数规划。为了有效而实际地解决此问题，我们首先将整数规划放宽为连续凸优化，然后利用随机采样的优势将解决方案强制为原始整数规划的可行方案。我们进行了严格的理论分析，以证明我们的算法可以提供相当好的竞争比。大量的仿真表明，我们提出的算法可以在保证所有请求的延迟要求的同时将总带宽成本降低30％，