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Deep reinforcement ...
Deep reinforcement learning for proactive spectrum defragmentation in elastic optical networks [Invited]
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- Etezadi, Ehsan, 1993 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Natalino Da Silva, Carlos, 1987 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Diaz, Renzo (författare)
- Telia Company
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- Lindgren, Anders (författare)
- Telia Company
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- Melin, Stefan (författare)
- Telia Company
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- Wosinska, Lena, 1951 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Monti, Paolo, 1973 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Furdek Prekratic, Marija, 1985 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
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Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 15:10, s. E86-E96
- Relaterad länk:
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https://research.cha... (primary) (free)
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https://research.cha...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- The immense growth of Internet traffic calls for advanced techniques to enable the dynamic operation of optical networks, efficient use of spectral resources, and automation. In this paper, we investigate the proactive spectrum defragmentation (SD ) problem in elastic optical networks and propose a novel deep reinforcement learning-based framework DeepDefrag to increase spectral usage efficiency. Unlike the conventional, often threshold-based heuristic algorithms that address a subset of the defragmentation related tasks and have limited automation capabilities, DeepDefrag jointly addresses the three main aspects of the SD process: determining when to perform defragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. By considering services attributes, spectrum occupancy state expressed by several different fragmentation metrics, as well as reconfiguration cost, DeepDefragmis able to consistently select appropriate reconfiguration actions over the network lifetime and adapt to changing conditions. Extensive simulation results reveal superior performance of the proposed scheme over a scenario with exhaustive defragmentation and a well-known benchmark heuristic from the literature, achieving lower blocking probability at a smaller defragmentation overhead.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
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- ref (ämneskategori)
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