Abstract
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.
Highlights
N OWADAYS, most cloud service providers (e.g., Google, Microsoft, Amazon) have deployed a geographically distributed infrastructure [1], [2], where datacenters are placed at different regions across the world to enhance application robustness and reduce user access delay at the same time
We study the problem of distributing user requests among geo-distributed datacenters to minimize the total bandwidth cost subject to the heterogeneous latency constraints from end-users
These results directly show that our proposed algorithm can significantly reduce the total bandwidth cost, when user requests are allocated across geographically distributed data centers
Summary
N OWADAYS, most cloud service providers (e.g., Google, Microsoft, Amazon) have deployed a geographically distributed infrastructure [1], [2], where datacenters are placed at different regions across the world to enhance application robustness and reduce user access delay at the same time. In this problem, we take into account the diverse latency requirement of user requests, the heterogeneity on per unit bandwidth cost of different datacenters, as well as the bandwidth capacity of each datacenter’s upstream link. We develop the mathematical model and formulate an integer programming which considers the diverse per unit bandwidth cost across different datacenters To solve this problem efficiently, we further develop an algorithm that seamlessly combines the techniques of convex optimization and random sampling, which has a good competitive ratio in minimizing total bandwidth cost. We conduct extensive simulations to evaluate the performance of our proposed algorithm, in terms of reducing total bandwidth cost for cloud providers and guaranteeing latency requirements of end-users.
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