Carbon-Neutral Computing at the Edge: Scalable Greedy - LP Optimization of Green Data Centers

Okazawa, Kazuki; Nishikawa, Hiroki; Zhao, Dafang et al.

Proceedings - 2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2025, 2025, 678-685

Number of Access:141（2026-06-25 17:35 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/104543

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Title
Carbon-Neutral Computing at the Edge: Scalable Greedy - LP Optimization of Green Data Centers
Creator
Scopus Author ID
58512254700 58512254700
CreatorName
Okazawa, Kazuki
NRID
1000090963538 1000090963538
ORCID
0000-0002-9626-0944 0000-0002-9626-0944
researchmap permalink
hiroki_nishikawa hiroki_nishikawa
Scopus Author ID
57205765139 57205765139
CreatorName
Nishikawa, Hiroki
NRID
1000010986962 1000010986962
ORCID
0000-0001-8107-4260 0000-0001-8107-4260
researchmap permalink
zhaodafang zhaodafang
Scopus Author ID
57315020300 57315020300
CreatorName
Zhao, Dafang
NRID
1000040551453 1000040551453
ORCID
0000-0002-7843-5907 0000-0002-7843-5907
researchmap permalink
ittetsu ittetsu
Scopus Author ID
23013657500 23013657500
CreatorName
Taniguchi, Ittetsu
NRID
1000060252590 1000060252590
researchmap permalink
read0096872 read0096872
Scopus Author ID
7007073634 7007073634
CreatorName
Onoye, Takao
CreatorName
De Melo Da Silva, Marcos
CreatorName
Gamatie, Abdoulaye
Subject
carbon neutrality
distributed computing
Edge data center
optimization
renewable energy
resource allocation
Description
Okazawa K., Nishikawa H., Zhao D., et al. Carbon-Neutral Computing at the Edge: Scalable Greedy - LP Optimization of Green Data Centers. Proceedings - 2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2025 , 678 (2025); https://doi.org/10.1109/MCSoC67473.2025.00110.
Abstract
As energy demands in cloud computing surge, efficient task scheduling and carbon-free energy use in mini data centers is essential. Solving scheduling problems with mixed-integer linear programming optimization methods is computationally intensive and less scalable. This paper proposes a two-step approach combining a greedy algorithm with linear programming to optimize virtual machines scheduling and energy management. We show via simulations that our method preserves solution quality, while accelerating the calculation process by 99.6 %. While the literature method requires 6 hours to solve a reference system design, our solution handles 18 times larger designs in 20 minutes only, demonstrating its scalability.
Conference Information
Conference Name
2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)
Conference Sequence
18
Conference Sponsor
IEEE
Conference Date
15-18 December 2025
Conference Place
Singapore
Conference Country
Singapore
Publisher
Institute of Electrical and Electronics Engineers Inc.
Source Title
Proceedings - 2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2025
Page
678-685
Date of Issued
2025-12-31
Language
English
Handle URL
https://hdl.handle.net/11094/104543
PISSN
27713067
eISSN
27713075
Relation.isVersionOf
DOI (Publisher Version)
https://doi.org/10.1109/MCSoC67473.2025.00110
Access Rights
open access
CopyRight
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
oaire:version
Accepted Manuscript
Category
会議発表論文 Conference Paper