SlicePilot: Demystifying Network Slice Placement in Heterogeneous Cloud Infrastructures
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908701106896896 |
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| author | Panitsas, Ioannis Atalay, Tolga O. Stojadinovic, Dragoslav Stavrou, Angelos Tassiulas, Leandros |
| author_facet | Panitsas, Ioannis Atalay, Tolga O. Stojadinovic, Dragoslav Stavrou, Angelos Tassiulas, Leandros |
| contents | Cellular networks are comprised of software-based entities, with main functions encapsulated as Virtual Network Functions (VNFs) deployed on Commercial-off-the-Shelf (COTS) hardware. As a key enabler of 5G, network slicing offers logically isolated Quality of Service (QoS) for diverse use cases. With the transition to cloud-native infrastructures, optimizing network slice placement across multi-cloud environments remains challenging due to heterogeneous resource capabilities and varying slice-specific demands. This paper presents SlicePilot, a modular framework that enables autonomous and near-optimal VNF placement using a disaggregated Multi-Agent Reinforcement Learning (MARL) approach. SlicePilot collects real-world traffic profiles to estimate resource needs for each slice type. These estimates guide a MARL-based scheduler that minimizes deployment costs while satisfying QoS constraints. We evaluate SlicePilot on a multi-cloud testbed and demonstrate a 19x speed-up over combinatorial optimization methods, while keeping deployment costs within 7.8% of the optimal. Although SlicePilot results in 2.42x more QoS violations under high-load conditions, this trade-off is offset by faster decision-making and reduced computational overhead. Overall, SlicePilot delivers a scalable, cost-efficient solution for network slice placement, making it suitable for real-time deployments where responsiveness and efficiency are critical. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18545 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SlicePilot: Demystifying Network Slice Placement in Heterogeneous Cloud Infrastructures Panitsas, Ioannis Atalay, Tolga O. Stojadinovic, Dragoslav Stavrou, Angelos Tassiulas, Leandros Networking and Internet Architecture Cellular networks are comprised of software-based entities, with main functions encapsulated as Virtual Network Functions (VNFs) deployed on Commercial-off-the-Shelf (COTS) hardware. As a key enabler of 5G, network slicing offers logically isolated Quality of Service (QoS) for diverse use cases. With the transition to cloud-native infrastructures, optimizing network slice placement across multi-cloud environments remains challenging due to heterogeneous resource capabilities and varying slice-specific demands. This paper presents SlicePilot, a modular framework that enables autonomous and near-optimal VNF placement using a disaggregated Multi-Agent Reinforcement Learning (MARL) approach. SlicePilot collects real-world traffic profiles to estimate resource needs for each slice type. These estimates guide a MARL-based scheduler that minimizes deployment costs while satisfying QoS constraints. We evaluate SlicePilot on a multi-cloud testbed and demonstrate a 19x speed-up over combinatorial optimization methods, while keeping deployment costs within 7.8% of the optimal. Although SlicePilot results in 2.42x more QoS violations under high-load conditions, this trade-off is offset by faster decision-making and reduced computational overhead. Overall, SlicePilot delivers a scalable, cost-efficient solution for network slice placement, making it suitable for real-time deployments where responsiveness and efficiency are critical. |
| title | SlicePilot: Demystifying Network Slice Placement in Heterogeneous Cloud Infrastructures |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2509.18545 |