DeepDefrag: A deep reinforcement learning framework for spectrum defragmentation

Etezadi, Ehsan, 1993 (author): Chalmers tekniska högskola,Chalmers University of Technology

Natalino Da Silva, Carlos, 1987 (author): Chalmers tekniska högskola,Chalmers University of Technology

Wosinska, Lena, 1951 (author): Chalmers tekniska högskola,Chalmers University of Technology

Monti, Paolo, 1973 (author): Chalmers tekniska högskola,Chalmers University of Technology

Furdek Prekratic, Marija, 1985 (author): Chalmers tekniska högskola,Chalmers University of Technology

(creator_code:org_t)

2022
2022
English.
In: 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings. ; , s. 3694-3699

Related links:: https://research.cha... (primary) (free); show more...; https://doi.org/10.1...; https://research.cha...; show less...

Abstract Subject headings

Exponential growth of bandwidth demand, spurred by emerging network services with diverse characteristics and stringent performance requirements, drives the need for dynamic operation of optical networks, efficient use of spectral resources, and automation. One of the main challenges of dynamic, resource-efficient Elastic Optical Networks (EONs) is spectrum fragmentation. Fragmented, stranded spectrum slots lead to poor resource utilization and increase the blocking probability of incoming service requests. Conventional approaches for Spectrum Defragmentation (SD) apply various criteria to decide when, and which portion of the spectrum to defragment. However, these polices often address only a subset of tasks related to defragmentation, are not adaptable, and have limited automation potential. To address these issues, we propose DeepDefrag, a novel framework based on reinforcement learning that addresses the 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. DeepDefrag outperforms the well-known Older-First First-Fit (OF-FF) defragmentation heuristic, achieving lower blocking probability under smaller defragmentation overhead.

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)

By the author/editor: Etezadi, Ehsan, ...; Natalino Da Silv ...; Diaz, Renzo; Lindgren, Anders; Melin, Stefan; Wosinska, Lena, ...; show more...; Monti, Paolo, 19 ...; Furdek Prekratic ...; show less...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Enginee ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Telecommunicatio ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Communication Sy ...

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