Copo: Joint Cost and Performance Optimization for Task Placement in Geo-Distributed Clouds

To provide a wide range of services for global users, cloud providers tend to build geo-distributed regions all over the world. With the rapid growth of cloud services, massive workloads and inter-region traffic have been introduced to current cloud networks, resulting in huge expenditure. Therefore, it is essential for a cloud provider to carefully place tasks and transfer traffic among regions to minimize the total operating costs. Existing solutions typically focus on optimizing either placement costs (e.g., computing resources, and electricity) or bandwidth costs, and overlook performance metrics, which leads to increased overall operating costs or lower user QoS. To bridge the gap, this paper proposes Copo, a joint cost and performance optimization framework for tenant task placement in geo-distributed clouds. We first formalize the cost optimization problem as an undetermined multi-commodity flow problem which has never been studied before, and propose a graph transformation algorithm to reduce the complexity. Then we combine the cost optimization with the performance optimization as the final framework. The key idea of Copo is leveraging KKT conditions to transfer the bi-level optimization to a single level. To efficiently acquire the joint task placement and traffic transfer decisions, we leverage McCormick Envelope-based relaxation to design a randomized rounding-based approximation algorithm. Extensive experiments based on real-world data show the superior cost-efficiency and performance of Copo compared with state-of-the-art solutions.