Lattice: An Efficient Task Placement Framework for Geographically Distributed Clouds

Yuehao Xu

doi:10.1109/tcc.2026.3663568

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Lattice: An Efficient Task Placement Framework for Geographically Distributed Clouds

2026-02-11

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Yuehao Xu

Zhijun Ding

DOI：10.1109/tcc.2026.3663568

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Abstract

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Cloud requests and infrastructures are gradually showing a trend of geographical distribution, and the scale is getting bigger and bigger. Due to a lack of information about the network, resources, and other tenants, tenant-made resource region decisions will incur more unnecessary costs than those made by the cloud provider. For such a complex scenario, public cloud providers even put stringent efficiency requirements, requiring decision-making within 1 s for 100 K requests. In summary, there are three significant challenges for task placement in large-scale geographically distributed clouds: complex topology, large scale, and lagging information. Existing studies either focus on intra-regional task placement only or ignore the complex request topology with large-scale and strict decision delay constraints, and most of them need to consider the impact of information lag on decision-making. To address these challenges, we design a large-scale task placement framework called Lattice. Despite the tough challenges posed on a large scale, we find that there are many similar relationships among complex topologies. Therefore, combining offline characterization and online placement, we design a data structure and algorithm based on pattern matching topology compression and interval retrieval, a multi-dimensional parallel placement architecture, and a competition-aware state representation strategy to achieve efficient and high-quality task deployment. Simulation results based on real-world datasets show that our algorithm can achieve an efficiency of 1.74x-15.01x that of existing methods while maintaining similar decision quality. We have open-sourced our algorithm, experimental environment, and experimental data.

Keywords:

Large scale

geo-distributed cloud

task placement

efficiency optimization