A comprehensive survey of micro datacenter: current technologies and future possibilities

Jinyang GUO , Mingxuan ZHANG , Yunwei LI , Chao LI , Minyi GUO

Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (5) : 2105101

PDF (1238KB)
Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (5) :2105101 DOI: 10.1007/s11704-025-50819-w
Architecture
REVIEW ARTICLE
A comprehensive survey of micro datacenter: current technologies and future possibilities
Author information +
History +
PDF (1238KB)

Abstract

Micro datacenters (μDCs) are emerging miniaturized datacenters poised to revolutionize distributed computing in edge environments by seamlessly integrating compact, modular architectures with localized computing, storage, and networking capabilities. This survey provides a complete and up-to-date review of μDC. We introduce a comprehensive three-layer analytical framework essential for understanding and evaluating μDC technologies: 1) The infrastructure layer empowers μDCs to adapt physical resources, including compute units; 2) The platform layer prioritizes middleware-driven resource orchestration, driving improvements in computational efficiency and energy optimization; 3) At the application layer, μDCs are committed to delivering performance optimizations that meet demanding requirements for latency, reliability, and energy efficiency. This work synthesizes leading-edge implementations and technology trends, clearly outlining critical trade-offs, identifying significant challenges, and defining the research frontiers vital for advancing μDC design and deployment across diverse industrial and extreme environments.

Graphical abstract

Keywords

micro datacenter / distributed collaboration / edge computing

Cite this article

Download citation ▾
Jinyang GUO, Mingxuan ZHANG, Yunwei LI, Chao LI, Minyi GUO. A comprehensive survey of micro datacenter: current technologies and future possibilities. Front. Comput. Sci., 2027, 21(5): 2105101 DOI:10.1007/s11704-025-50819-w

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

NVIDIA Corporation. NVIDIA DRIVE Thor. See blogs.nvidia.cn/blog/drive-thor/ website, Accessed: 2025-5

[2]

Liu L, Feng J, Mu X, Pei Q, Lan D, Xiao M . Asynchronous deep reinforcement learning for collaborative task computing and on-demand resource allocation in vehicular edge computing. IEEE Transactions on Intelligent Transportation Systems, 2023, 24( 12): 15513–15526

[3]

Luo Q, Li C, Luan T H, Shi W . Minimizing the delay and cost of computation offloading for vehicular edge computing. IEEE Transactions on Services Computing, 2022, 15( 5): 2897–2909

[4]

Shi J, Du J, Shen Y, Wang J, Yuan J, Han Z . DRL-based V2V computation offloading for blockchain-enabled vehicular networks. IEEE Transactions on Mobile Computing, 2023, 22( 7): 3882–3897

[5]

Sun L, Hou X, Li C, Liu J, Wang X, Chen Q, Guo M . A2: towards accelerator level parallelism for autonomous micromobility systems. ACM Transactions on Architecture and Code Optimization, 2024, 21( 4): 86

[6]

Sun L, Li C, Hou X, Huang T, Xu C, Wang X, Bao G, Sun B, Rui S, Guo M. Jigsaw: Taming BEV-centric perception on dual-SoC for autonomous driving. In: Proceedings of 2024 IEEE Real-Time Systems Symposium (RTSS). 2024, 280−293

[7]

Talpes E, Williams D, Sarma D D. DOJO: The microarchitecture of tesla’s exa-scale computer. In: Proceedings of 2022 IEEE Hot Chips 34 Symposium (HCS). 2022, 1−28

[8]

Saini L M, Ajmani P, Asha V, Pithamber K, Sreevani N. KubeEdge for scalable IoT applications. In: Proceedings of the 7th International Conference on Contemporary Computing and Informatics (IC3I). 2024, 1−7

[9]

Macenski S, Foote T, Gerkey B, Lalancette C, Woodall W . Robot operating system 2: design, architecture, and uses in the wild. Science Robotics, 2022, 7( 66): eabm6074

[10]

Chang W, Wu J. Fog/Edge Computing for Security, Privacy, and Applications. Cham: Springer, 2021

[11]

Dar F, Liyanage M, Radeta M, Yin Z, Zuniga A, Kosta S, Tarkoma S, Nurmi P, Flores H . Upscaling fog computing in oceans for underwater pervasive data science using low-cost micro-clouds. ACM Transactions on Internet of Things, 2023, 4( 2): 9

[12]

Periola A A, Alonge A A, Ogudo K A . Heat wave resilient systems architecture for underwater data centers. Scientific Reports, 2022, 12( 1): 17161

[13]

Simon K . Project Natick-Microsoft’s self-sufficient underwater datacenters. IndraStra Global, 2018, 4( 6): 1–4

[14]

Elhoseny M, Lakhan A, Rashid A, Mohammed M, Abdulkareem K . Underwater sensor multi-parameter scheduling for heterogenous computing nodes. ACM Transactions on Sensor Networks, 2022, 18( 3): 35

[15]

Lin C, Han G, Jiang J, Li C, Shah S B H, Liu Q . Underwater pollution tracking based on software-defined multi-tier edge computing in 6G-based underwater wireless networks. IEEE Journal on Selected Areas in Communications, 2023, 41( 2): 491–503

[16]

Bleier N, Mubarik M H, Swenson G R, Kumar R. Space microdatacenters. In: Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture. 2023, 900−915

[17]

Bleier N, Eason R, Lembeck M, Kumar R. Architecting space microdatacenters: a system-level approach. In: Proceedings of 2025 IEEE International Symposium on High Performance Computer Architecture (HPCA). 2025, 1304−1319

[18]

Denby B, Lucia B . Orbital edge computing: machine inference in space. IEEE Computer Architecture Letters, 2019, 18( 1): 59–62

[19]

Denby B, Lucia B. Orbital edge computing: Nanosatellite constellations as a new class of computer system. In: Proceedings of the 25th International Conference on Architectural Support for Programming Languages and Operating Systems. 2020, 939−954

[20]

Wang S, Li Q, Xu M, Ma X, Zhou A, Sun Q. Tiansuan constellation: An open research platform. In: Proceedings of 2021 IEEE International Conference on Edge Computing (EDGE). 2021, 94−101

[21]

Yang C, Yuan J, Wu Y, Sun Q, Zhou A, Wang S, Xu M . Communication-efficient satellite-ground federated learning through progressive weight quantization. IEEE Transactions on Mobile Computing, 2024, 23( 9): 8999–9011

[22]

Li Y, Li H, Liu W, Liu L, Chen Y, Wu J, Wu Q, Liu J, Lai Z. A case for stateless mobile core network functions in space. In: Proceedings of the ACM SIGCOMM 2022 Conference. 2022, 298−313

[23]

Azimi R, Fox T, Gonzalez W, Reda S . Scale-out vs scale-up: A study of ARM-based SoCs on server-class workloads. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 2018, 3( 4): 18

[24]

Xu D, Xu M, Lou C, Zhang L, Huang G, Jin X, Liu X. SoCFlow: efficient and scalable DNN training on SoC-clustered edge servers. In: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 2024, 368−385

[25]

Ichnowski J, Chen K, Dharmarajan K, Adebola S, Danielczuk M, Mayoral-Vilches V, Zhan H, Xu D, Ghassemi R, Kubiatowicz J, Stoica I, Gonzalez J, Goldberg K. FogROS 2: an adaptive and extensible platform for cloud and fog robotics using ROS 2. 2023, arXiv preprint arXiv: 2205.09778

[26]

HPE. Spaceborne Computer-2 returns to the International Space Station. See www.hpe.com/us/en/newsroom/press-release/2024/01/hpe-spaceborne-computer-2-returns-to-the-international-space-station.html website, Accessed: 2025-5

[27]

NVIDIA Corporation. DRIVE AGX Developer Kits. See developer.nvidia.com/drive/agx website, Accessed: 2025-5

[28]

Hao H, Xi W, Kuster A, Gamage A, Xia X. MC-LoRa: Multi-node Concurrent Localization for LoRaWAN Indoors and Outdoors. In: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2025

[29]

Wang S, Zhao Y, Xu J, Yuan J, Hsu C H . Edge server placement in mobile edge computing. Journal of Parallel and Distributed Computing, 2019, 127: 160–168

[30]

Wu G, Chen X, Gao Z, Zhang H, Yu S, Shen S . Privacy-preserving offloading scheme in multi-access mobile edge computing based on MADRL. Journal of Parallel and Distributed Computing, 2024, 183: 104775

[31]

Cao B, Fan S, Zhao J, Tian S, Zheng Z, Yan Y, Yang P . Large-scale many-objective deployment optimization of edge servers. IEEE Transactions on Intelligent Transportation Systems, 2021, 22( 6): 3841–3849

[32]

Ning Z, Yang Y, Wang X, Guo L, Gao X, Guo S, Wang G . Dynamic computation offloading and server deployment for UAV-enabled multi-access edge computing. IEEE Transactions on Mobile Computing, 2023, 22( 5): 2628–2644

[33]

Ning Z, Yang Y, Wang X, Song Q, Guo L, Jamalipour A . Multi-agent deep reinforcement learning based UAV trajectory optimization for differentiated services. IEEE Transactions on Mobile Computing, 2024, 23( 5): 5818–5834

[34]

Xu X, Shen B, Yin X, Khosravi M R, Wu H, Qi L, Wan S . Edge server quantification and placement for offloading social media services in industrial cognitive IoV. IEEE Transactions on Industrial Informatics, 2021, 17( 4): 2910–2918

[35]

Tong Z, Deng X, Ye F, Basodi S, Xiao X, Pan Y . Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment. Information Sciences, 2020, 537: 116–131

[36]

Bruschi R, Davoli F, Lombardo C, Sanchez O R. Evaluating the impact of micro-data center (µDC) placement in an urban environment. In: Proceedings of 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). 2018, 1−7

[37]

Bruschi R, Davoli F, Lago P, Pajo J F . A multi-clustering approach to scale distributed tenant networks for mobile edge computing. IEEE Journal on Selected Areas in Communications, 2019, 37( 3): 499–514

[38]

Bruschi R, Pajo J F, Davoli F, Lombardo C . Managing 5G network slicing and edge computing with the MATILDA telecom layer platform. Computer Networks, 2021, 194: 108090

[39]

Teng F, Ban Z, Li T, Sun Q, Li Y . A privacy-preserving distributed economic dispatch method for integrated port microgrid and computing power network. IEEE Transactions on Industrial Informatics, 2024, 20( 8): 10103–10112

[40]

Switzer J, Marcano G, Kastner R, Pannuto P. Junkyard computing: Repurposing discarded smartphones to minimize carbon. In: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 2023, 400−412

[41]

Hu S, Qu Z, Tang B, Ye B, Li G, Shi W . Joint service request scheduling and container retention in serverless edge computing for vehicle-infrastructure collaboration. IEEE Transactions on Mobile Computing, 2024, 23( 6): 6508–6521

[42]

Mobileye . Mobileye: creating the autonomous future takes experience and vision. See Mobileye. com/ website, 2024

[43]

Schneider Electric. EcoStruxureTM Micro data center. See Apc. com/us/en/solutions/business-solutions/micro-data-centers/r-series. jsp website, 2024

[44]

Dell. Dell VxRail Spec Sheet. See www.delltechnologies.com/asset/en-us/products/converged-infrastructure/technical-support/h16763-vxrail-spec-sheet.pdf website, Accessed: 2025-5

[45]

Hu Y, Lin X, Wang H, He Z, Yu X, Zhang J, Yang Q, Xu Z, Guan S, Fang J, Shang H, Tang X, Dai X, Wei S, Yin S . Wafer-scale computing: advancements, challenges, and future perspectives [feature]. IEEE Circuits and Systems Magazine, 2024, 24( 1): 52–81

[46]

AWS. AWS wavelength. See aws.amazon.com/cn/wavelength/ website, Accessed: 2025-5

[47]

Verizon Business. 5G edge. See Verizon.com/business/products/5g-edge/ website, Accessed: 2025-5

[48]

Liquidstar . Solar-powered micro data centers. See Liquidstar.io/ website, Accessed: 2025-5

[49]

Subsea Cloud Partners. Underwater edge infrastructure. See www.subseacloudpartners.com/ website, Accessed: 2025-5

[50]

Microsoft. Project Natick. See natick.research.microsoft.com/ websitec, Accessed: 2025-5

[51]

Edgex Foundry. The open source edge platform. See Edgexfoundry. org/ website, 2024

[52]

Acun B, Lee B, Kazhamiaka F, Maeng K, Gupta U, Chakkaravarthy M, Brooks D, Wu C J. Carbon explorer: a holistic framework for designing carbon aware datacenters. In: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 2023, 118−132

[53]

Li C, Hu Y, Zhou R, Liu M, Liu L, Yuan J, Li T. Enabling datacenter servers to scale out economically and sustainably. In: Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture. 2013, 322−333

[54]

Kontorinis V, Zhang L E, Aksanli B, Sampson J, Homayoun H, Pettis E, Tullsen D M, Rosing T S . Managing distributed ups energy for effective power capping in data centers. ACM SIGARCH Computer Architecture News, 2012, 40( 3): 488–499

[55]

Pal S, Liu J, Alam I, Cebry N, Suhail H, Bu S, Iyer S S, Pamarti S, Kumar R, Gupta P. Designing a 2048-chiplet, 14336-core waferscale processor. In: Proceedings of 2021 58th ACM/IEEE Design Automation Conference (DAC). 2021, 1183−1188

[56]

Lie S A. Cerebras architecture deep dive: First look inside the HW/SW co-design for deep learning: Cerebras systems. In: Proceedings of 2022 IEEE Hot Chips 34 Symposium (HCS). 2022, 1−34

[57]

Min D, Byun I, Lee G H, Kim J . CoolDC: A cost-effective immersion-cooled datacenter with workload-aware temperature scaling. ACM Transactions on Architecture and Code Optimization, 2024, 21( 3): 51

[58]

Qouneh A, Li C, Li T. A quantitative analysis of cooling power in container-based data centers. In: Proceedings of 2011 IEEE International Symposium on Workload Characterization (IISWC). 2011, 61−71

[59]

Pei Q, Chen S, Zhang Q, Zhu X, Liu F, Jia Z, Wang Y, Yuan Y. CoolEdge: hotspot-relievable warm water cooling for energy-efficient edge datacenters. In: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 2022, 814−829

[60]

Li H, Li Z, Bai Z, Mitra T. ASADI: accelerating sparse attention using diagonal-based in-situ computing. In: Proceedings of 2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA). 2024, 774−787

[61]

Toosi A N, Son J, Buyya R . CLOUDS-Pi: a low-cost raspberry-pi based micro data center for software-defined cloud computing. IEEE Cloud Computing, 2018, 5( 5): 81–91

[62]

IBM and ASTRON, DOME MicroDataCenter, See en.wikipedia.org/wiki/DOME_MicroDataCenter, Accessed: 2025-5

[63]

Yuan G, Behnam P, Li Z, Shafiee A, Lin S, Ma X, Liu H, Qian X, Bojnordi M N, Wang Y, Ding C. FORMS: fine-grained polarized ReRAM-based in-situ computation for mixed-signal DNN accelerator. In: Proceedings of the 48th ACM/IEEE Annual International Symposium on Computer Architecture (ISCA). 2021, 265−278

[64]

Song M, Zhong K, Zhang J, Hu Y, Liu D, Zhang W, Wang J, Li T. In-situ AI: towards autonomous and incremental deep learning for IoT systems. In: Proceedings of 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA). 2018, 92−103

[65]

Zhou J, Cao K, Zhou X, Chen M, Wei T, Hu S . Throughput-conscious energy allocation and reliability-aware task assignment for renewable powered in-situ server systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022, 41( 3): 516–529

[66]

Li C, Hu Y, Liu L, Gu J, Song M, Liang X, Yuan J, Li T . Towards sustainable in-situ server systems in the big data era. ACM Sigarch Computer Architecture News, 2015, 43( 3S): 14–26

[67]

StarlingX . Open source edge cloud platform. See Starlingx. io/ website, Accessed: 2025-5

[68]

Abuibaid M, Ghorab A H, Seguin-Mcpeake A, Yuen O, Yungblut T, St-Hilaire M . Edge workloads monitoring and failover: a starlingX-based testbed implementation and measurement study. IEEE Access, 2022, 10: 97101–97116

[69]

Li C, Xue Y, Wang J, Zhang W, Li T . Edge-oriented computing paradigms: A survey on architecture design and system management. ACM Computing Surveys (CSUR), 2019, 51( 2): 39

[70]

Linux. Aether open source 5G platform designed for the edge. See aetherproject.org/ website, Accessed: 2025-5

[71]

AWS . AWS snowball. See Aws. amazon. com/snowball-edge/ website, Accessed: 2025-5

[72]

Schlinker B, Kim H, Cui T, et al. Engineering Egress with Edge Fabric: Steering Oceans of Content to the World. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM '17). 2017, 418-431

[73]

Rancher. Lightweight Kubernetes Distribution. See www.rancher.com/categories/distributions, Accessed: 2025-5

[74]

Intel. OpenNESS: Enabling HighPerformance Edge for Telco&Enterprise. See intelsmartedge.github.io/ website, Accessed: 2025-5

[75]

NVIDIA. Nvidia Drive Agx System. See www.nvidia.com/en-us/deep-learning-ai/products/agx-systems/ website, Accessed: 2025-5

[76]

Zhang L, Feng W, Li C, Hou X, Wang P, Wang J, Guo M . Tapping into NFV environment for opportunistic serverless edge function deployment. IEEE Transactions on Computers, 2022, 71( 10): 2698–2704

[77]

Li Z, Lou J, Wu J, Guo J, Tang Z, Shen P, Jia W, Zhao W . Online container scheduling with fast function startup and low memory cost in edge computing. IEEE Transactions on Computers, 2024, 73( 12): 2747–2760

[78]

Xiao K, Yang S, Li F, Zhu L, Chen X, Fu X . Making serverless not so cold in edge clouds: a cost-effective online approach. IEEE Transactions on Mobile Computing, 2024, 23( 9): 8789–8802

[79]

Liu D, Zhang L, Xu Y, Wang X, Sun L, Pu Y, Hou X, Li C, Guo M . Power synchronization: taming massive diversified serverless functions under power constraints. Science China Information Sciences, 2025, 68( 3): 132101

[80]

Xu Z, Zhou L, Liang W, Xia Q, Xu W, Ren W, Ren H, Zhou P . Stateful serverless application placement in MEC with function and state dependencies. IEEE Transactions on Computers, 2023, 72( 9): 2701–2716

[81]

Li Y, Zeng D, Gu L, Ou M, Chen Q. On efficient zygote container planning toward fast function startup in serverless edge cloud. In: Proceedings of IEEE INFOCOM 2023-IEEE Conference on Computer Communications. 2023, 1−9

[82]

Hou X, Xu T, Li C, Xu C, Liu J, Hu Y, Zhao J, Leng J, Cheng K T, Guo M. A tale of two domains: Exploring efficient architecture design for truly autonomous things. In: Proceedings of the 51st ACM/IEEE Annual International Symposium on Computer Architecture (ISCA). 2024, 167−181

[83]

Hong H, Wu Q, Dong F, Song W, Sun R, Han T, Zhou C, Yang H. NetGraph: an intelligent operated digital twin platform for data center networks. In: Proceedings of the ACM SIGCOMM 2021 Workshop on Network-Application Integration. 2021, 26−32

[84]

Sarkar S, Naug A, Guillen A, Luna R, Gundecha V, Babu A R, Mousavi S. Sustainability of data center digital twins with reinforcement learning. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. 2024, 23832−23834

[85]

Chi Y, Yue J, Liao X, Liu H, Jin H . A hybrid memory architecture supporting fine-grained data migration. Frontiers of Computer Science, 2024, 18( 2): 182103

[86]

Tian C, Liu H, Liao X, Jin H . UCat: heterogeneous memory management for unikernels. Frontiers of Computer Science, 2023, 17( 1): 171204

[87]

Jiang C J, Ding Z J, Yu J, Zhang Z H, Yan C G, Zhang Y Y, Wang P W . Cabin computing. SCIENTIA SINICA Informationis, 2021, 51( 8): 1233–1254

RIGHTS & PERMISSIONS

Higher Education Press

PDF (1238KB)

Supplementary files

Highlights

453

Accesses

0

Citation

Detail

Sections
Recommended

/