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Artificial intelligence (AI)-driven zero-touch network slicing (NS) is a new paradigm enabling the automation of resource management and orchestration (MANO) in multi-tenant beyond 5G (B5G) networks. In this paper, we tackle the problem of cloud-RAN (C-RAN) joint slice admission control and resource allocation by first formulating it as a Markov decision process (MDP). We then invoke an advanced continuous deep reinforcement learning (DRL) method called twin delayed deep deterministic policy gradient (TD3) to solve it. In this intent, we introduce a multi-objective approach to make the central unit (CU) learn how to re-configure computing resources autonomously while minimizing latency, energy consumption and virtual network function (VNF) instantiation cost for each slice. Moreover, we build a complete 5G C-RAN network slicing environment using OpenAI Gym toolkit where, thanks to its standardized interface, it can be easily tested with different DRL schemes. Finally, we present extensive experimental results to showcase the gain of TD3 as well as the adopted multi-objective strategy in terms of achieved slice admission success rate, latency, energy saving and CPU utilization.
", "funding": [ { "award": { "acronym": "5G STEP FWD", "id": "00k4n6c32::722429", "identifiers": [ { "identifier": "https://cordis.europa.eu/projects/722429", "scheme": "url" } ], "number": "722429", "program": "H2020", "title": { "en": "5G System Technological Enhancements Provided by Fiber Wireless Deployments" } }, "funder": { "id": "00k4n6c32", "name": "European Commission" } } ], "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2020-12-07", "publisher": "Zenodo", "resource_type": { "id": "publication-conferencepaper", "title": { "de": "Konferenzbeitrag", "en": "Conference paper" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "Admission control" }, { "subject": "B5G" }, { "subject": "continuous DRL" }, { "subject": "C-RAN" }, { "subject": "network slicing" }, { "subject": "OpenAI Gym" }, { "subject": "resource allocation" }, { "subject": "zerotouch" } ], "title": "Continuous Multi-objective Zero-touch Network Slicing via Twin Delayed DDPG and OpenAI Gym" }, "parent": { "access": { "owned_by": { "user": 19395 } }, "communities": { "default": "a1f17b5e-93df-48ca-b66a-9494100ee009", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2023-07-24T08:40:27.992139+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "a1f17b5e-93df-48ca-b66a-9494100ee009", "links": {}, "metadata": { "curation_policy": "", "page": "The MonB5G represents the joint effort of twelve organizations from eight European countries (Greece, Spain, France, Ireland, Poland, Germany, Cyprus and Finland), during 42 months. The MonB5G project capitalised on artificial intelligence (AI) for smart, flexible and automated management of 5G and 6G network resources. The project used AI-based mechanisms for zero-touch management and orchestration of massive\u0002scale network slices. With the application of AI algorithms, networks acquired self-configuration, self\u0002monitoring, self-healing and self-optimization capabilities, without the need for human intervention. MonB5G presented a hierarchical, fault-tolerant, AI-based, automated network management framework that includes security and energy efficiency techniques for orchestrating many parallel network slices. The concept of “network slicing” referred to the division of a virtual network infrastructure into multiple customized and independent segments and the suitable allocation, per case of use, of network resources to efficiently serve different requirements. Each network slice, assisted by AI, ensured the seamless operation of demanding applications that may require high capacity and low latency. In addition to the provision of reliable and high-quality services, the use of AI enhanced the infrastructure security and improved the energy efficiency of networks. Overall, the MonB5G project's potential impacts can also extend beyond technical advancements and have the potential to generate socio-economic benefits, drive innovation, and positively influence the wider society by improving network efficiency, enhancing service quality, strengthening security, promoting sustainability, and contributing to economic growth for operators and service providers.
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