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Dr. Zhiming Zhao home
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Multiscale Networked Systems (MNS) University of Amsterdam Email: z.zhao[at]uva.nl Tel: +31 638560996 Office: Room L5.40, Lab 42 Science Park 900, 1098XH Amsterdam the Netherlands |
We are looking for 1. Part time programmers 2. Post doctors 3. PhD
students... |
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I received my Ph.D. in computer science in
2004 from the University
of Amsterdam (UvA).
I am an associate professor and the chair of the Multiscale Network Systems (MNS) research group in the Informatics
Institute (IvI)
at UvA.
I am the technical
manager of the Virtual Lab and Innovation Center (VLIC)
of LifeWatch
ERIC, a European research infrastructure in ecology and biodiversity
science. My research focuses on quality-critical
distributed computing, data-intensive workflow management, virtual research
environments, and digital twins. I am the Co-PI of the Dutch LTER-LIFE project and coordinate the UvA effort in EU projects ENVRI-HUB
next, EVERSE, OSCARS and BlueCloud 2026
to develop Digital Twin Virtual Research Environment, research assets search
engine, and Cloud automation and optimization solutions. I coordinated the
project SWITCH (Software
Workbench for interactive time-critical and highly self-adaptive cloud
applications). I led the Data for Science theme in the
environmental science cluster project ENVRIPLUS, and the technical
development work packages in ENVRI-FAIR, ARTICONF and CLARIFY projects. I am an IEEE Senior member
and the managing editor of the Journal of Cloud
Computing. |
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Research: Quality Critical Applications on Programmable Infrastructures |
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In both scientific and
industrial contexts, there are distributed application systems which 1. have very high business
value (e.g., on-demand business collaboration platforms), or social impact
(e.g., for early warning of disasters); and 2. have very critical
requirements for Quality of Service (QoS) (e.g., tsunami emergency response
time) or quality of experience (QoE) (e.g.,
delivery of ultra-high-definition television or collaborative business
interactions); but 3. are very difficult to
develop and operate because of their distributed nature and the high
requirements for the runtime environment, and in particular, the
sophisticated optimization mechanisms needed for developing and integrating
the system components. Cloud environments provide
virtualized, elastic, controllable, and quality on-demand services for
supporting complex application systems. However, developing, deploying, and
executing quality-critical applications in programmable infrastructure is
still difficult and challenging due to a lack of effective programming and
control mechanisms. My research interests have
revolved around modeling, developing,
controlling, and optimizing such Distributed Quality-Critical
Systems. I specifically focus on novel models for programming, executing,
and optimizing such applications on programmable infrastructures,
such as Cloud- and software-defined networking technologies. My basic
approach is to use autonomous agent technologies to decompose system
complexity, to develop distributed control and optimization intelligence by
combining both application logic and infrastructure programmability, and to
investigate self-adaptable cooperative control models for Cloud-based
quality-critical systems. |
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Leader: Dr. Zhiming
Zhao Postdoc researchers and developers: ·
Vacancy ·
Dr. Nafiseh Soveizi,
Digital twin composition and optimization (2024-) ·
Dr. Siamak Farshidi, Cognitive digital twins (2024-) ·
Dr. Peide Zhu, VRE
search engine (2024-) ·
Dr. Gabriel Pelouze, NaaVRE, VL (2023-) ·
Dr. Spiros Koulouzis,
DRIP, CONF, VRE and use cases (2017-) Ph.D. students: ·
Shaozheng Shi, (2023-) ·
Stefanie Boss, legal anomalies in decentralized
infrastructure (2023-) ·
Yuandou Wang, Distributed data processing
(2020-) ·
Na Li, Knowledge discovery (2020-) ·
HongYun Liu, Cloud resource management (2019-) Former members: ·
Zijie Liu, Machine learning and job scheduling
(2023-) ·
Yi Chen, Machine learning and job scheduling (2023-) ·
Ruyue Xin, Virtual Infrastructure control
and adaptation (2019-)[thesis] ·
Dr. Yangjun Zhang, Knowledge discovery (2023-2023) ·
J.M. van der Stoep, NaaVRE developer (2022-2023) ·
Zhengqiu Zhu, Incentive models in crowd
applications (2020-2022) ·
Dr. Uraz Odyurt, AI and cloud computing
(2021-2022) ·
Zeshun Shi, Trustworthy Virtual
infrastructure (2018-2022) [thesis] ·
Mr. Ning Chen, Sensor network and robustness
(2021-2022) ·
Riccardo Bianchi (M.Sc), DevOps (2020-2022) ·
Dr. Peng Chen, Cloud infrastructure
optimization (2020-2021) ·
Dr. Xiaofeng Liao,
Alignment, annotation (2017-2020) ·
Dr. Paul Martin, Semantic information
linking (2015-2018) ·
Dr. Arie Taal, Time critical
applications (2016-2019) ·
Junchao Wang, Virtual Infrastructure
planning (2015-2020) ·
Hu Yang, Time critical application deployment
(2015-2019) [thesis] ·
Huan Zhou, Virtual infrastructure provisioning
and DevOps (2015-2019) [thesis] |
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Funding and
projects |
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Via University of Amsterdam Via LifeWatch ERIC Virtual Lab and Innovation Center (VLIC) |
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Via UvA 1.
NWO LTER-LIFE
(a research infrastructure to develop Digital Twins of ecosystems in a
changing world), Large-Scale Research Infrastructure (LSRI), Duration August
2023-August 2032. 2.
EU H-Europe ENVRI-Hub
Next (ENVironmental Research
Infrastructures delivering an open access Hub and NEXT-level
interdisciplinary research framework providing services for advancing science
and society). Grant No. 101131141. HORIZON-INFRA-2023-DEV-01
call. Duration 2024-2027. 3.
EU H-Europe EVERSE
(European Virtual Institute for Research Software Excellence). Grant No.
101129744. HORIZON-INFRA-2023-EOSC-01-02
call. Duration 2024-2027. 4.
EU H-Europe OSCARS
(Open Science Clusters’ Action for Research and Society). Grant No.
101129751. HORIZON-INFRA-2023-EOSC-01 call. Duration 2024-2027. (Third party
via LifeWatch ERIC) 5.
EU H-Europe BlueCloud-2026 (A
federated European FAIR and Open Research Ecosystem for Oceans, Seas, and
inland waters). Grant No. 101094227. HORIZON-INFRA-2022-EOSC-01-03
call. Duration: Jan 2023-June 2026. Via LifeWatch 6.
EU H-Europe BioDT (Biodiversity Digital Twin for Advanced
Modelling, Simulation and Prediction Capabilities) Grant No. 101057437. HORIZON-INFRA-2021-TECH-01-01
call. Duration: June 2022- May 2025. (as LifeWatch
VLIC) Finished projects 7.
EU H2020 CLARIFY (CLoud
ARtificial Intelligence For pathologY).
Grant No. 860627. H2020-MSCA-ITN-2019
call. Duration: Nov 2019-Oct 2023. 8.
EU H2020 ENVRIFAIR (ENVironmental Research
Infrastructures building Fair services Accessible for society, Innovation and
Research). Grant No 824068. H2020-INFRAEOSC-2018-2
call. Duration: January 2019-December 2022. 9.
EU H2020 BLUECLOUD (Blue-Cloud: Piloting
innovative services for Marine Research & the Blue Economy).H2020-BG-2018-2020.
Grant No. 862409. Duration: Nov 2019-Oct 2022. 10. EU H2020 ARTICONF (smART socIal media eCOsytstem in a blockchaiN Federated
environment). Grant No 825134. H2020-ICT-2018-2
call. Duration: January 2019-December 2021. 11. EU H2020 ENVRIPLUS (ENVironmental Research Infrastructures Providing
shared soLUtions for Science
and society). Leader of the Data for Science Theme. Grant No. 654182 (INFRADEV-4-2014-2015).
Duration: May 2015- April 2019, Project website: www.envriplus.eu. 12. EU H2020 VRE4EIC (A
Europe-wide interoperable Virtual Research Environment to Empower
multidisciplinary research communities and accelerate Innovation and
Collaboration). Grant number 676247. H2020-EINFRA-2015-1.
Duration October 2015- September 2018. 13. EU H2020 SWITCH (Software
Workbench for Interactive, Time Critical and Highly self-adaptive cloud
applications). Grant No 643963. H2020-ICT
9-2014-1 call: Tools and methods for software development. Duration:
February 2015- January 2018. (SWITCH project). Participated projects 14. EU FP7 ENVRI.
Task leader, task 3.4: linking data and infrastructure - Common operations of
Environmental Research Infrastructure (ENVRI). Grant number 283465, |
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Community |
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Editorial board 1.
Managing editor, Journal of cloud computing,
advances, systems and applications 2.
International journal of: Blockchains:
research and applications 3.
Journal of Circuits,
Systems and Computers Organizer/co-organizer
1. Special session: Using heterogeneous environmental data
for system-level science, 2018, Vienna, EGU
1. in Digital
Infrastructure for research 2016, Krakow, Poland 2. in Digital Infrastructure for research 2017,
Brussels, Belgium
Summer school 1. International summer school on data
management in environmental and earth sciences (2018
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| 2020) Workshop/Panel
chair
Program committee
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Journals 1.
Xin,
R., Chen, P., Grosso, P., Zhao, Z.: A fine-grained robust performance
diagnosis framework for run-time cloud applications. Future Generation
Computer Systems. 155, 300–311 (2024). https://doi.org/10.1016/j.future.2024.02.014.
2.
Song,
Y., Xin, R., Chen, P., Zhang, R., Chen, J., Zhao, Z.: Autonomous
selection of the fault classification models for diagnosing microservice applications.
Future Generation Computer Systems. 153, 326–339 (2024). https://doi.org/10.1016/j.future.2023.12.005. 3.
Cheng,
L., Wang, Y., Cheng, F., Liu, C., Zhao, Z., Wang, Y.: A Deep
Reinforcement Learning-Based Preemptive Approach
for Cost-Aware Cloud Job Scheduling. IEEE Trans. Sustain. Comput.
1–12 (2023). https://doi.org/10.1109/TSUSC.2023.3303898.
4.
Jiang, W., Chen, K., Liang, Z., Luo, T.,
Yue, G., Zhao, Z., Song, W., Zhao, L., Wen, J.: HT-RCM: Hashimoto’s Thyroiditis
Ultrasound Image Classification Model based on Res-FCT and Res-CAM. IEEE J.
Biomed. Health Inform. 1–11 (2023). https://doi.org/10.1109/JBHI.2023.3331944.
5.
Tabatabaei,
Z., Wang, Y., Colomer, A., Oliver Moll, J., Zhao,
Z., Naranjo, V.: WWFedCBMIR: World-Wide
Federated Content-Based Medical Image Retrieval. Bioengineering. 10, 1144
(2023). https://doi.org/10.3390/bioengineering10101144
6.
Rito
Lima, I., Filipe, V., Marinho, C., Ulisses, A., Chakravorty, A.,
Hristov, A., Saurabh, N., Zhao, Z., Xin, R., Prodan, R.: ARTICONF
decentralized social media platform for democratic crowd journalism. Soc. Netw. Anal. Min. 13, 116 (2023). https://doi.org/10.1007/s13278-023-01110-y.
7.
Zhang,
J., Cheng, L., Liu, C., Zhao, Z., Mao, Y.: Cost-aware scheduling
systems for real-time workflows in cloud: An approach based on Genetic
Algorithm and Deep Reinforcement Learning. Expert Systems with Applications.
234, 120972 (2023). https://doi.org/10.1016/j.eswa.2023.120972
8.
Li,
J., Li, J., Xie, C., Liang, Y., Qu, K., Cheng, L., Zhao,
Z.: PipCKG-BS: A Method to Build Cybersecurity
Knowledge Graph for Blockchain Systems via the Pipeline Approach. J CIRCUIT
SYST COMP. 2350274 (2023). https://doi.org/10.1142/S0218126623502742
9.
Xin,
R., Chen, P., Zhao, Z.: CausalRCA: Causal inference
based precise fine-grained root cause localization for microservice
applications. Journal of Systems and Software. 111724 (2023). https://doi.org/10.1016/j.jss.2023.111724. 10. Liu, H., Xin, R., Chen, P., Gao, H., Grosso, P., Zhao,
Z.: Robust-PAC time-critical workflow offloading in edge-to-cloud
continuum among heterogeneous resources. J Cloud Comp. 12, 58 (2023). https://doi.org/10.1186/s13677-023-00434-6. 11.
Liu, H., Chen, P.,
Ouyang, X., Gao, H., Yan, B., Grosso, P., Zhao, Z.: Robustness
challenges in Reinforcement Learning based time-critical cloud resource
scheduling: A Meta-Learning based solution. Future Generation Computer
Systems. 146, 18–33 (2023). https://doi.org/10.1016/j.future.2023.03.029. 12.
Song, Y., Xin, R., Chen, P., Zhang, R.,
Chen, J., Zhao, Z.: Identifying performance anomalies in fluctuating
cloud environments: A robust correlative-GNN-based explainable approach.
Future Generation Computer Systems. 145, 77–86 (2023). https://doi.org/10.1016/j.future.2023.03.020.[OA]. 13. Xin, R., Liu, H., Chen, P., Zhao, Z.: Robust
and accurate performance anomaly detection and prediction for cloud
applications: a novel ensemble learning-based framework. J Cloud Comp. 12, 7
(2023). https://doi.org/10.1186/s13677-022-00383-6.
14. Launet, L., Wang, Y., Colomer,
A., Igual, J., Pulgarín-Ospina,
C., Koulouzis, S., Bianchi, R., Mosquera-Zamudio,
A., Monteagudo, C., Naranjo, V., Zhao, Z.:
Federating Medical Deep Learning Models from Private Jupyter
Notebooks to Distributed Institutions. Applied Sciences. 13, 919 (2023). https://doi.org/10.3390/app13020919.
15. Shi, Z., de Laat, C., Grosso, P., Zhao, Z.:
Integration of Blockchain and Auction Models: A Survey, Some Applications,
and Challenges. IEEE Commun. Surv.
Tutorials. 1–1 (2022). https://doi.org/10.1109/COMST.2022.3222403. 16. Zhu, Z., Ai, C., Chen, H., Chen, B., Duan, W., Qiu, X., Lu, X., He, M., Zhao, Z., Liu, Z.:
Understanding the Necessity and Economic Benefits of Lockdown Measures to
Contain COVID-19. IEEE Trans. Comput. Soc. Syst.
1–13 (2022). https://doi.org/10.1109/TCSS.2022.3194639
17. Xiao, H., Li, P., Zeng, H., Liang, T., Jiang, W., Zhao,
Z.: Metric learning-based whole health indicator model for industrial
robots. Int J of Intelligent Sys. int.23008 (2022). https://doi.org/10.1002/int.23008
18.
Yuan, S., Wang, Y., Liang, T., Jiang,
W., Lin, S., Zhao, Z.: Real‐time recognition and warning of mask
wearing based on improved YOLOv5 R6.1. Int J of Intelligent Sys. 37,
9309–9338 (2022). https://doi.org/10.1002/int.22994. 19. Shi, Z., Ivankovic, V., Farshidi, S., Surbiryala, J.,
Zhou, H., Zhao, Z.: AWESOME: an auction and witness enhanced SLA model
for decentralized cloud marketplaces. J Cloud Comp. 11, 27 (2022). https://doi.org/10.1186/s13677-022-00292-8 20. Zhu, Z., Chen, B., Chen, H., Qiu,
S., Fan, C., Zhao, Y., Guo, R., Ai, C., Liu, Z., Zhao, Z., Fang, L.,
Lu, X.: Strategy evaluation and optimization with an artificial society
toward a Pareto optimum. The Innovation. 3, 100274 (2022). https://doi.org/10.1016/j.xinn.2022.100274
21. Chen, P., Liu, H., Xin, R., Carval, T., Zhao, J.,
Xia, Y., Zhao, Z.: Effectively Detecting Operational Anomalies In Large-Scale IoT Data Infrastructures By Using A
GAN-Based Predictive Model. The Computer Journal. 65, 2909–2925 (2022). https://doi.org/10.1093/comjnl/bxac085
22. Shi, Z., Zhou, H., De Laat, C., Zhao, Z.: A
Bayesian game-enhanced auction model for federated cloud services using
blockchain. Future Generation Computer Systems. 136, 49–66 (2022). https://doi.org/10.1016/j.future.2022.05.017
23. Zhao, Z., Koulouzis, S., Bianchi, R., Farshidi,
S., Shi, Z., Xin, R., Wang, Y., Li, N., Shi, Y., Timmermans, J., Kissling,
W.D.: Notebook-as-a-VRE (NaaVRE): From private
notebooks to a collaborative cloud virtual research environment. Softw Pract Exp. spe.3098
(2022). https://doi.org/10.1002/spe.3098. 24. Wang, Y., Koulouzis, S.,
Bianchi, R., Li, N., Shi, Y., Timmermans, J., Kissling, W.D., Zhao, Z.:
Scaling Notebooks as Re-configurable Cloud Workflows. Data Intelligence. 4,
409–425 (2022). https://doi.org/10.1162/dint_a_00140. 25. Wittenburg, P., Hardisty, A., Franc, Y.L., Mozaffari,
A., Peer, L., Skvortsov, N.A., Zhao, Z., Spinuso, A.: Canonical Workflows to Make Data FAIR. Data
Intelligence. 1–20 (2022). https://doi.org/10.1162/dint_a_00132 26. Jiang, W., Pan, S., Lu, C., Zhao, Z., Lin,
S., Xiong, M., He, Z.: Label entropy-based
cooperative particle swarm optimization algorithm for dynamic overlapping
community detection in complex networks. Int J Intell
Syst. int.22673 (2021). https://doi.org/10.1002/int.22673. 27. Farshidi, S., Liao, X., Li, N., Goldfarb, D., Magagna, B., Stocker, M., Jeffery, K., Thijsse, P., Pichot, C., Petzold, A., Zhao, Z.: Knowledge sharing and
discovery across heterogeneous research infrastructures. Open Res Europe. 1,
68 (2021). https://doi.org/10.12688/openreseurope.13677.1.
28. Karandikar, N., Abhishek, R., Saurabh, N., Zhao, Z., Lercher, A., Marina, N., Prodan, R., Rong, C., Chakravorty, A.: Blockchain-based prosumer incentivization
for peak mitigation through temporal aggregation and contextual clustering.
Blockchain: Research and Applications. 2, 100016 (2021). https://doi.org/10.1016/j.bcra.2021.100016 29. Saurabh, N., Rubia, C., Palanisamy,
A., Koulouzis, S., Sefidanoski,
M., Chakravorty, A., Zhao, Z., Karadimce, A., Prodan, R.: The ARTICONF Approach to
Decentralized Car-Sharing. Blockchain: Research and Applications. 100013
(2021). https://doi.org/10.1016/j.bcra.2021.100013.
30. Zhou, H., Shi, Z., Ouyang, X. Zhao, Z.,
Building a blockchain-based decentralized ecosystem for cloud and edge
computing: an ALLSTAR approach and empirical study. Peer-to-Peer Network
Application. (2021). https://doi.org/10.1007/s12083-021-01198-z
[OA] 31. Calyam, P., Wilkins-Diehr, N.,
Miller, M., Brookes, E.H., Arora, R., Chourasia, A., Jennewein,
D.M., Nandigam, V., Drew LaMar,
M., Cleveland, S.B., Newman, G., Wang, S., Zaslavsky,
I., Cianfrocco, M.A., Ellett,
K., Tarboton, D., Jeffery, K.G., Zhao, Z.,
González - Aranda, J., Perri, M.J., Tucker, G., Candela, L., Kiss, T., Gesing, S.: Measuring success for a future vision:
Defining impact in science gateways/virtual research environments.
Concurrency Computat Pract
Exper. cpe.6099 (2020). https://doi.org/10.1002/cpe.6099. 32. Zhu, Z., Chen, B., Liu, W., Zhao, Y., Liu, Z., and Zhao Z., A Cost-Quality Beneficial
Cell Selection Approach for Sparse Mobile Crowdsensing With Diverse Sensing
Costs, in IEEE Internet of Things Journal, vol. 8, no. 5, pp.
3831-3850, 1 March1, 2021, https://doi.org/10.1109/JIOT.2020.3024833.[OA]. 33. Zhang, L., Jiang, W., Zhao, Z.: Short -text
feature expansion and classification based on nonnegative matrix
factorization. Int Journal of Intelligent Systems. int.22290 (2020)https://doi.org/10.1002/int.22290[OA]. 34. Uriarte, R.B., Zhou, H., Kritikos,
K., Shi, Z., Zhao, Z., De Nicola,
R.: Distributed service- level agreement management with smart contracts and
blockchain. Concurrency Computat Pract Exper. (2020). https://doi.org/10.1002/cpe.5800. 35. Hu, Y., de Laat, C., Zhao, Z.: Optimizing Service Placement for Microservice
Architecture in Clouds. Applied Sciences. 9, 4663 (2019). https://doi.org/10.3390/app9214663. 36. Hu, Y., Zhou, H., de Laat, C., Zhao, Z.: Concurrent container scheduling on heterogeneous
clusters with multi-resource constraints. Future Generation Computer Systems.
102, 562–573 (2020). https://doi.org/10.1016/j.future.2019.08.025. 37. Remy, L., Ivanovic, D., Theodoridou,
M., Kritsotaki, A., Martin, P., Bailo,
D., Sbarra, M., Zhao, Z., Jeffery, K.: Building an Integrated Enhanced Virtual
Research Environment Metadata Catalogue. The Electronic Library. (2019) https://doi.org/10.1108/EL-09-2018-0183
[OA]. 38. Zhou, H., Hu, Y., Ouyang, X., Su,
J., Koulouzis, S., Laat, C., Zhao, Z.: CloudsStorm: A framework for
seamlessly programming and controlling virtual infrastructure functions
during the DevOps lifecycle of cloud applications. Softw:
Pract Exper. 49,
1421–1447 (2019). https://doi.org/10.1002/spe.2741. 39. Tanhua, T., Pouliquen, S.,
Hausman, J., O’Brien, K., Bricher, P., de Bruin,
T., Buck, J.J.H., Burger, E.F., Carval, T., Casey, K.S., Diggs, S., Giorgetti, A., Glaves, H., Harscoat, V., Kinkade, D., Muelbert,
J.H., Novellino, A., Pfeil, B., Pulsifer,
P.L., Van de Putte, A., Robinson, E., Schaap, D.,
Smirnov, A., Smith, N., Snowden, D., Spears, T., Stall, S., Tacoma, M., Thijsse, P., Tronstad, S., Vandenberghe, T., Wengren, M., Wyborn, L., Zhao,
Z.: Ocean FAIR Data Services. Front. Mar. Sci. 6, 440 (2019). https://doi.org/10.3389/fmars.2019.00440. 40. Martin, P., Remy, L., Theodoridou,
M., Jeffery, K., Zhao, Z.: Mapping
heterogeneous research infrastructure metadata into a unified catalogue for use
in a generic virtual research environment. Future Generation Computer
Systems. 101, 1–13 (2019). https://doi.org/10.1016/j.future.2019.05.076.
[OA] 41. Zhou, H., Ouyang, X., Su,
J., Laat, C., Zhao, Z.: Enforcing
trustworthy cloud SLA with witnesses: A game theory–based model using smart
contracts. Concurrency Computation: Practice Experience (2019). https://doi.org/10.1002/cpe.5511. 42. Taal, A., Wang, J., de Laat, C., Zhao, Z.: Profiling the scheduling
decisions for handling critical paths in deadline-constrained cloud
workflows. Future Generation Computer Systems. 100, 237–249 (2019). https://doi.org/10.1016/j.future.2019.05.002. 43. Štefanič, P., Cigale, M., Jones,
A.C., Knight, L., Taylor, I., Istrate, C., Suciu,
G., Ulisses, A., Stankovski,
V., Taherizadeh, S., Salado, G.F., Koulouzis, S., Martin, P., Zhao, Z.: SWITCH workbench: A novel approach for the development
and deployment of time-critical microservice-based cloud-native applications.
Future Generation Computer Systems. 99, 197–212 (2019). https://doi.org/10.1016/j.future.2019.04.008. 44. Liao, X., Zhao,
Z.: Unsupervised Approaches for Textual Semantic Annotation, A Survey.
ACM Comput. Surv. 52,
1–45 (2019). https://doi.org/10.1145/3324473. 45. Liao, X., Bottelier, J., Zhao, Z.: A Column Styled Composable
Schema Matcher for Semantic Data-Types. Data Science
Journal. 18-25 (2019). https://doi.org/10.5334/dsj-2019-025. 46. Koulouzis, S., Martin, P., Zhou, H., Hu, Y., Wang, J.,
Carval, T., Grenier, B., Heikkinen, J., Laat, C., Zhao, Z.: Time-critical data
management in clouds: Challenges and a Dynamic Real-Time Infrastructure
Planner (DRIP) solution. Concurrency Computat Pract Exper. e5269 (2019). https://doi.org/10.1002/cpe.5269. 47. Taherizadeh, S., Jones, A.C., Taylor, I., Zhao, Z., Stankovski, V.: Monitoring
self-adaptive applications within edge computing frameworks: A
state-of-the-art review. Journal of Systems and Software. 136, 19–38 (2018). https://doi.org/10.1016/j.jss.2017.10.033. 48. Li, J., Yang, Y., Wang, X., Zhao, Z., Li, T.: A novel parallel distance metric-based approach
for diversified ranking on large graphs. Future Generation Computer Systems.
88, 79–91 (2018). https://doi.org/10.1016/j.future.2018.05.031. 49. Jiang, W., Zhai, Y.,
Zhuang, Z., Martin, P., Zhao, Z.,
Liu, J.-B.: Vertex Labelling and Routing for Farey-Type Symmetrically-Structured
Graphs. Symmetry. 10, 407 (2018). https://doi.org/10.3390/sym10090407. 50. Jiang, W., Zhai, Y.,
Zhuang, Z., Martin, P., Zhao, Z., Liu,
J.-B.: An Efficient Method of Generating Deterministic Small-World and
Scale-Free Graphs for Simulating Real-World Networks. IEEE Access. 6,
59833–59842 (2018). https://doi.org/10.1109/ACCESS.2018.2875928. 51. Jiang, W., Zhai, Y.,
Martin, P., Zhao, Z.: Structure
Properties of Generalized Farey graphs based on Dynamical Systems for
Networks. Sci Rep. 8, 12194 (2018). https://doi.org/10.1038/s41598-018-30712-2. 52. Wang, J., Taal, A., Martin, P., Hu, Y., Zhou, H.,
Pang, J., de Laat, C., Zhao, Z.:
Planning virtual infrastructures for time critical applications with multiple
deadline constraints. Future Generation Computer Systems. 75, 365–375 (2017).
https://doi.org/10.1016/j.future.2017.02.001,
[Zenodo]. 53. Koulouzis, S., Belloum, A.S.Z., Bubak, M.T., Zhao,
Z., Živković, M., de Laat, C.T.A.M.: SDN-aware
federation of distributed data. Future Generation Computer Systems. 56, 64–76
(2016). https://doi.org/10.1016/j.future.2015.09.032. 54. Zhu, H., van der Veldt, K., Zhao, Z., Grosso, P., Pavlov, D., Soeurt,
J., Liao, X., de Laat, C.: A semantic enhanced Power Budget Calculator for
distributed computing using IEEE 802.3az. Cluster Comput.
18, 61–77 (2015). https://doi.org/10.1007/s10586-014-0395-7. 55. Ghijsen, M., van der Ham, J., Grosso, P., Dumitru, C., Zhu,
H., Zhao, Z., de Laat, C.: A
semantic-web approach for modelling computing infrastructures. Computers
& Electrical Engineering. 39, 2553–2565 (2013). https://doi.org/10.1016/j.compeleceng.2013.08.011. 56. Zhao,
Z., Grosso, P., van der Ham, J.,
Koning, R., de Laat, C.: An agent-based network resource planner for workflow
applications. MGS. 7, 187–202 (2011). https://doi.org/10.3233/MGS-2011-0180. 57. Belloum, A., Inda, M.A., Vasunin, D., Korkhov, V., Zhao, Z., Rauwerda,
H., Breit, T.M., Bubak,
M., Hertzberger, L.O.: Collaborative e-Science
Experiments and Scientific Workflows. IEEE Internet Comput.
15, 39–47 (2011). https://doi.org/10.1109/MIC.2011.87. 58. Zhao,
Z., van Albada,
D., Sloot, P.: Agent-Based Flow Control for HLA Components. SIMULATION. 81,
487–501 (2005). https://doi.org/10.1177/0037549705058060. 59. Kommers, P. and Zhao, Z.: Conceptual
Support with Virtual Reality in Web-based Learning, International Journal
of Continuing Engineering Education and Life-Long Learning, vol. 8, nr 1
1998. ISSN 0957-4344 (1998). https://www.inderscienceonline.com/doi/abs/10.1504/IJCEELL.1998.030134. Editorial (special issues
and proceedings) 60. Wittenburg, P., Hardisty, A., Mozzafari,
A., Peer, L., Skvortsov, N., Spinuso,
A., Zhao, Z.: Editors’ Note: Special Issue on Canonical Workflow
Frameworks for Research. Data Intelligence. 4, 149–154 (2022). https://doi.org/10.1162/dint_e_00122. 61. Rong, C., Zhao, Z.: Welcome to the
new Journal of Cloud Computing by Springer. J Cloud Comp. 10, 49,
s13677-021-00263–5 (2021). https://doi.org/10.1186/s13677-021-00263-5.
62. Zhao, Z., Taylor, I., Prodan, R.: Editorial for
FGCS Special issue on “Time-critical Applications on Software-defined
Infrastructures.” Future Generation Computer Systems. 112, 1170–1171 (2020). https://doi.org/10.1016/j.future.2020.07.056.[OA] [Content]
63. Zhao, Z., Altmeyer,
S., Keith J., Atkinson, M. and Ulisses A.: Nearly
real time data processing and time critical cloud applications. Proceedings
of the 2nd International workshop on Interoperable
infrastructures for interdisciplinary big data sciences (IT4RIs 16), in the
context of IEEE Real-time System Symposium (RTSS), Porto, Portugal, November
29-December 2, (2016). [doi:10.5281/zenodo.204685] 64. Lu, S., Deelman,
E. & Zhao, Z.: Scientific
workflows special issue. International journal of business process
integration and management (pp. 1-2). Inder science
Enterprises Ltd. (2010). [Full text] 65. Zhao, Z., Belloum, A., Bubak, M.: Special section on workflow systems and
applications in e-Science. Future Generation Computer Systems. 25, 525–527
(2009). https://doi.org/10.1016/j.future.2008.10.011. 66. Belloum, A., Deelman,
E. & Zhao, Z.: Scientific
workflows. Scientific Programming, 14(3-4), 171-171 (2006). [Full text] Book (Eds) 67. Zhao, Z., Hellström,
M. eds: Towards Interoperable Research Infrastructures for Environmental and
Earth Sciences: A Reference Model Guided Approach for Common Challenges.
Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4.
[ENVRI archive][Book interview] Book chapters 68. Jeffery, K., Pursula, A., Zhao, Z.: ICT Infrastructures for
Environmental and Earth Sciences. In: Zhao,
Z. and Hellström, M. (eds.) Towards Interoperable
Research Infrastructures for Environmental and Earth Sciences. pp. 17–29.
Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_2. 69. Magagna, B., Martin, P., de la Hidalga,
A.N., Atkinson, M., Zhao, Z.:
Common Challenges and Requirements. In: Zhao,
Z. and Hellström, M. (eds.) Towards
Interoperable Research Infrastructures for Environmental and Earth Sciences.
pp. 30–57. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_3. 70. de la Hidalga, A.N.,
Hardisty, A., Martin, P., Magagna, B., Zhao, Z.: The ENVRI Reference Model.
In: Zhao, Z. and Hellström, M. (eds.) Towards Interoperable Research
Infrastructures for Environmental and Earth Sciences. pp. 61–81. Springer
International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_4. 71. Zhao, Z., Jeffery, K.: Reference
Model Guided Engineering. In: Zhao, Z. and Hellström,
M. (eds.) Towards Interoperable Research Infrastructures for Environmental
and Earth Sciences. pp. 82–99. Springer International Publishing, Cham
(2020). https://doi.org/10.1007/978-3-030-52829-4_5. 72. Martin, P., Liao, X., Magagna,
B., Stocker, M., Zhao, Z.:
Semantic and Knowledge Engineering Using ENVRI RM. In: Zhao, Z. and Hellström, M. (eds.)
Towards Interoperable Research Infrastructures for Environmental and Earth
Sciences. pp. 100–119. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_6. 73. Quimbert, E., Jeffery, K., Martens, C., Martin, P., Zhao, Z.: Data Cataloguing. In: Zhao, Z. and Hellström,
M. (eds.) Towards Interoperable Research Infrastructures for Environmental
and Earth Sciences. pp. 140–161. Springer International Publishing, Cham
(2020). https://doi.org/10.1007/978-3-030-52829-4_8. 74. Koulouzis, S., Martin, P., Zhao, Z.: Virtual Infrastructure Optimisation. In: Zhao, Z. and Hellström,
M. (eds.) Towards Interoperable Research Infrastructures for Environmental
and Earth Sciences. pp. 192–207. Springer International Publishing, Cham
(2020). https://doi.org/10.1007/978-3-030-52829-4_11. 75. Magagna, B., Goldfarb, D., Martin, P., Atkinson, M., Koulouzis, S., Zhao,
Z.: Data Provenance. In: Zhao, Z.
and Hellström, M. (eds.) Towards Interoperable
Research Infrastructures for Environmental and Earth Sciences. pp. 208–225.
Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_12. 76. Martin, P., Magagna, B.,
Liao, X., Zhao, Z.: Semantic
Linking of Research Infrastructure Metadata. In: Zhao, Z. and Hellström, M. (eds.)
Towards Interoperable Research Infrastructures for Environmental and Earth
Sciences. pp. 226–246. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_13. 77. Koulouzis, S., Carval, T., Heikkinen, J., Pursula,
A., Zhao, Z.: Case Study: Data Subscriptions
Using Elastic Cloud Services. In: Zhao,
Z. and Hellström, M. (eds.) Towards
Interoperable Research Infrastructures for Environmental and Earth Sciences.
pp. 293–306. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-52829-4_16. 78. Zhao,
Z., Jeffery, K., Stocker, M.,
Atkinson, M., Petzold, A.: Towards Operational
Research Infrastructures with FAIR Data and Services. In: Zhao, Z. and Hellström,
M. (eds.) Towards Interoperable Research Infrastructures for Environmental
and Earth Sciences. pp. 360–372. Springer International Publishing, Cham
(2020). https://doi.org/10.1007/978-3-030-52829-4_20. 79. Martin, P., Chen, Y. Hardisty, A., Jeffery, K., and Zhao,
Z.: Computational
Challenges in Global Environmental Research Infrastructures. in
the book Terrestrial Ecosystem Research Infrastructures: Challenges, New
Developments and Perspectives (2017). [ISBN 9781498751315] [OA]. 80. Zhao, Z., Grosso, P., Ham, J. van der, Koning, R.
& de Laat, C.: Quality guaranteed media delivery over
advanced network. Chapter in book Next Generation Content Delivery
Infrastructure: Emerging Paradigms and Technologies, IGI, (2012). ISBN
978-1-4666-1794-0 [doi: 10.4018/978-1-4666-1794-0.ch006]. Conferences 81. Van De Kamp, R., Bakker, K., Zhao, Z.: Paving the
Path Towards Platform Engineering Using a Comprehensive Reference Model. In: Sales, T.P., De Kinderen, S., Proper, H.A., Pufahl, L., Karastoyanova, D., and Van Sinderen, M. (eds.) Enterprise Design, Operations, and Computing. EDOC
2023 Workshops. pp. 177–193. Springer Nature Switzerland, Cham (2024). https://doi.org/10.1007/978-3-031-54712-6_11
[OA] 82. Ashraf, A., Belleman,
R.G., Zhao, Z.: Visualization Techniques and Tools for Developing Digital
Twins of Ecosystems: State-of-the-Art and Selection. In: 2023 IEEE Smart
World Congress (SWC). pp. 1–8. IEEE, Portsmouth, United Kingdom (2023). https://doi.org/10.1109/SWC57546.2023.10448753
[OA] 83. Li, N., Qi, Y., Xin, R., Zhao, Z.: Ocean Data
Quality Assessment through Outlier Detection-enhanced Active Learning. In:
2023 IEEE International Conference on Big Data (BigData).
pp. 102–107. IEEE, Sorrento, Italy (2023). https://doi.org/10.1109/BigData59044.2023.10386969
[OA]. 84. Li, N., Zhang, Y., Zhao, Z.: A Dense
Retrieval System and Evaluation Dataset for Scientific Computational
Notebooks. In: 2023 IEEE 19th International Conference on e-Science
(e-Science). pp. 1–10. IEEE, Limassol, Cyprus (2023). https://doi.org/10.1109/e-Science58273.2023.10254859[OA]. 85. Christou, V., Wang, Y., Zhao, Z.: Towards a
Knowledge Graph Enhanced Automation and Collaboration Framework for Digital
Twins. In: 2023 IEEE 19th International Conference on e-Science (e-Science).
pp. 1–2. IEEE, Limassol, Cyprus (2023). https://doi.org/10.1109/e-Science58273.2023.10254845
[OA]. 86. Kontomaris, C., Wang, Y., Zhao, Z.: CWL-FLOps: A Novel Method for Federated Learning Operations
at Scale. In: 2023 IEEE 19th International Conference on e-Science
(e-Science). pp. 1–2. IEEE, Limassol, Cyprus (2023). https://doi.org/10.1109/e-Science58273.2023.10254788[OA]. 87. Marra, M.L., Henkemans, D.B., Titocci, J., Koulouzis, S.,
Rosati, I., Zhao, Z.: Integrating R in a Distributed Scientific
Workflow via a Jupyter-Based Environment. In: 2023
IEEE 19th International Conference on e-Science (e-Science). pp. 1–2. IEEE,
Limassol, Cyprus (2023). https://doi.org/10.1109/e-Science58273.2023.10254945[OA]. 88. Blanco, A.F., Shi, Z., Roy, D., Zhao, Z.:
Improving the Resiliency of Decentralized Crowdsourced Blockchain Oracles.
In: Mikyška, J., De Mulatier,
C., Paszynski, M., Krzhizhanovskaya,
V.V., Dongarra, J.J., and Sloot, P.M.A. (eds.)
Computational Science – ICCS 2023. pp. 3–17. Springer Nature Switzerland,
Cham (2023). https://doi.org/10.1007/978-3-031-35995-8_1
89. Wang, Y., Kanwal, N., Engan,
K., Rong, C., Zhao, Z.: Towards a Privacy-Preserving Distributed Cloud
Service for Preprocessing Very Large Medical
Images. In: 2023 IEEE International Conference on Digital Health (ICDH). pp.
325–327. IEEE, Chicago, IL, USA (2023). https://doi.org/10.1109/ICDH60066.2023.00055
[OA] 90. Wang, Y., Janse, N.,
Bianchi, R., Koulouzis, S., Zhao, Z.:
Towards a Service-based Adaptable Data Layer for Cloud Workflows. In: 2023
IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC).
pp. 904–911. IEEE, Torino, Italy (2023). https://doi.org/10.1109/COMPSAC57700.2023.00121
[OA] 91. Li, N., Zhang, Y., Zhao, Z.: CNSVRE: A Query
Reformulated Search System with Explainable Summarization for Virtual
Research Environment. In: Companion Proceedings of the ACM Web Conference
2023. pp. 254–257. ACM, Austin TX USA (2023). https://doi.org/10.1145/3543873.3587360
[OA]. 92. Song, Y., Xin, R., Zhang, R., Chen, J., Zhao, Z.:
A Robust and Accurate Multivariate Time Series Anomaly Detection in
Fluctuating Cloud-Edge Computing Systems. In: 2022 IEEE 24th Int Conf on
High-Performance Computing & Communications; 8th Int Conf on Data Science
& Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in
Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys). pp.
357–365. IEEE, Hainan, China (2022). https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00077
93. Lima, I.R., Marinho, C.,
Filipe, V., Ulisses, A., Saurabh, N., Chakravorty, A., Zhao, Z., Hristov, A., Prodan,
R.: MOGPlay: A Decentralized Crowd Journalism
Application for Democratic News Production. In: 2022 IEEE/ACM International
Conference on Advances in Social Networks Analysis and Mining (ASONAM). pp.
462–469. IEEE, Istanbul, Turkey (2022). https://doi.org/10.1109/ASONAM55673.2022.10068697. 94. Xin, R., Stallinga, S.,
Liu, H., Chen, P., Zhao, Z.: Provenance-enhanced Root Cause Analysis
for Jupyter Notebooks. In: 2022 IEEE/ACM 15th
International Conference on Utility and Cloud Computing (UCC). pp. 327–333. IEEE, Vancouver, WA, USA (2022). https://doi.org/10.1109/UCC56403.2022.00058[OA]. 95. Chen, S., Huang, G., Lin, S., Jiang, W., Zhao,
Z.: Overlapping Community Discovery Algorithm Based on Three-Level Neighbor Node Influence. In: Xu, Y., Yan, H., Teng, H.,
Cai, J., and Li, J. (eds.) Machine Learning for Cyber Security. pp. 335–344.
Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-20099-1_28.
96. Li, N., Farshidi, S.,
Bianchi, R., Koulouzis, S., Zhao, Z.:
Context-Aware Notebook Search in a Jupyter-Based
Virtual Research Environment. In: 2022 IEEE 18th International Conference on
e-Science (e-Science). pp. 393–394. IEEE, Salt Lake City, UT, USA (2022). https://doi.org/10.1109/eScience55777.2022.00054[OA]. 97. Li, M., Su, J., Liu, H., Zhao,
Z., Ouyang, X., Zhou, H.: The Extreme Counts: Modeling
the Performance Uncertainty of Cloud Resources with Extreme Value Theory. In:
Troya, J., Medjahed, B., Piattini,
M., Yao, L., Fernández, P., and Ruiz-Cortés, A. (eds.) Service-Oriented
Computing. pp. 498–512. Springer Nature Switzerland, Cham (2022). https://doi.org/10.1007/978-3-031-20984-0_35. 98. Launet, L., Amor, R. del, Colomer,
A., Mosquera-Zamudio, A., Moscardó, A., Monteagudo, C., Zhao, Z., Naranjo, V.: Federating Unlabeled Samples: A Semi-supervised Collaborative
Framework for Whole Slide Image Analysis. In: Yin, H., Camacho, D., and Tino,
P. (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2022.
pp. 64–72. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-21753-1_7
99. Ivankovic, V., Shi, Z., Zhao, Z.: A Customizable dApp Framework for User Interactions in Decentralized
Service Marketplaces. In: 2022 IEEE International Conference on Smart
Internet of Things (SmartIoT). pp. 224–231. IEEE,
Suzhou, China (2022). https://doi.org/10.1109/SmartIoT55134.2022.00043.[OA][Best paper]. 100.
Liu,
H., Xin, R., Chen, P., Zhao, Z.: Multi-Objective Robust Workflow
Offloading in Edge-to-Cloud Continuum. In: 2022 IEEE 15th International
Conference on Cloud Computing (CLOUD). pp. 469–478. IEEE, Barcelona, Spain
(2022). https://doi.org/10.1109/CLOUD55607.2022.00070.[OA] 101.
Boyko,
A., Farshidi, S., Zhao, Z.: An Adaptable
Framework for Entity Matching Model Selection in Business Enterprises. In:
2022 IEEE 24th Conference on Business Informatics (CBI). pp. 90–99. IEEE,
Amsterdam, Netherlands (2022). https://doi.org/10.1109/CBI54897.2022.00017 [OA] 102.
Farshidi, S., Zhao, Z.: An Adaptable Indexing
Pipeline for Enriching Meta Information of Datasets from Heterogeneous
Repositories. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., and Teng, F.
(eds.) Advances in Knowledge Discovery and Data Mining. pp. 472–484. Springer
International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-05936-0_37
[OA]. 103.
Hoogenkamp, B., Farshidi, S., Xin,
R., Shi, Z., Chen, P., Zhao, Z.: A Decentralized Service Control
Framework for Decentralized Applications in Cloud Environments. In: Montesi, F., Papadopoulos, G.A., and Zimmermann, W.
(eds.) Service-Oriented and Cloud Computing. pp. 65–73. Springer
International Publishing, Cham (2022). [https://doi.org/10.1007/978-3-031-04718-3_4][OA] 104.
Bergers, J., Shi, Z., Korsmit,
K., Zhao, Z.: DWH-DIM: A Blockchain Based Decentralized Integrity
Verification Model for Data Warehouses. In: 2021 IEEE International
Conference on Blockchain (Blockchain). pp. 221–228. IEEE, Melbourne,
Australia (2021). https://doi.org/10.1109/Blockchain53845.2021.00037
[OA] 105.
Poon,
L., Farshidi, S., Li, N., Zhao, Z.:
Unsupervised Anomaly Detection in Data Quality Control. In: 2021 IEEE
International Conference on Big Data (Big Data). pp. 2327–2336. IEEE,
Orlando, FL, USA (2021). https://doi.org/10.1109/BigData52589.2021.9671672
[OA] 106.
Liu
H., Chen P., Zhao, Z.,: Towards A Robust Meta-Reinforcement
Learning-Based Scheduling Framework for Time Critical Tasks in Cloud
Environments, IEEE Cloud (2021), Online, [10.1109/CLOUD53861.2021.00082]
[OA](Best student paper) 107.
Shi
Ze., Farshidi S., Zhou H., Zhao Z., An
Auction and Witness Enhanced Trustworthy SLA Model for Decentralized Cloud Marketplaces, The
2021 ACM International Conference on Information Technology for Social Good (GoodIT 2021), Rome Italy [https://doi.org/10.1145/3462203.3475876]
[OA] 108.
Xin
R., Mohazzab J., Shi Z., and Zhao Z.: CBProf: Customisable Blockchain-as-a-service Performance
Profiler in Cloud Environments, International Conf. on Blockchain (2021),
Online [https://doi.org/10.1007/978-3-030-96527-3_9][OA] 109.
Koulouzis, S., Bianchi, R., der Linde, R. van, Wang, Y., Zhao,
Z.: SPIRIT: A Microservice-Based Framework for Interactive Cloud
Infrastructure Planning. In: Chaves, R., B. Heras,
D., Ilic, A., Unat, D., Badia,
R.M., Bracciali, A., Diehl, P., Dubey, A., Sangyoon, O., L. Scott, S., and Ricci, L. (eds.) Euro-Par
2021: Parallel Processing Workshops. pp. 405–416. Springer International
Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-06156-1_32
[OA]. 110.
Zhao, Z., Rong, C., Jaatun, M.G.: A Trustworthy Blockchain-based
Decentralised Resource Management System in the Cloud. In: 2020 IEEE 26th International
Conference on Parallel and Distributed Systems (ICPADS). pp. 617–624. IEEE,
Hong Kong (2020). https://doi.org/10.1109/ICPADS51040.2020.00086
[OA]. 111.
Wang,
Y., Zhao, Z.: Decentralized
workflow management on software defined infrastructure, Workshop on The 1st
Workshop On Data-Centric Workflows On Heterogeneous Infrastructures:
Challenges And Directions (DAWHI), in the context of IEEE Service Congress,
(2020), [https://doi.org/10.1109/SERVICES48979.2020.00059][OA]. 112.
de
Jong, K., Fahrenfort, C., Younis, A., Zhao, Z.: Sharing digital object across
data infrastructures using Named Data Networking (NDN). 2nd
workshop on Network Aware Big Data Computing (NEAC), In: 2020 20th IEEE/ACM
International Symposium on Cluster, Cloud and Internet Computing (CCGRID).
pp. 873–880. IEEE, Melbourne, Australia (2020). https://doi.org/10.1109/CCGrid49817.2020.00013.
[OA]. 113.
Zhou,
H., Ouyang, X., Zhao, Z.: ALLSTAR:
A Blockchain Based Decentralized Ecosystem for Cloud and Edge Computing. In:
2020 IEEE International Conference on Joint Cloud Computing. pp. 55–62. IEEE,
Oxford, United Kingdom (2020). https://doi.org/10.1109/JCC49151.2020.00018
[OA]. 114.
Shi,
Z., Zhou, H., Surbiryala, J., Hu, Y., de Laat, C., Zhao, Z.: An Automated Customization
and Performance Profiling Framework for Permissioned Blockchains in a
Virtualized Environment. In: 2019 IEEE International Conference on Cloud
Computing Technology and Science (CloudCom). pp. 404–410.
IEEE, Sydney, Australia (2019). https://doi.org/10.1109/CloudCom.2019.00069,
[OA]. 115.
Petzold, A., Asmi, A., Vermeulen, A., Pappalardo,
G., Bailo, D., Schaap,
D., Glaves, H.M., Bundke,
U., Zhao, Z.: ENVRI-FAIR -
Interoperable Environmental FAIR Data and Services for Society, Innovation
and Research. In: 2019 15th International Conference on eScience (eScience).
pp. 277–280. IEEE, San Diego, CA, USA (2019). https://doi.org/10.1109/eScience.2019.00038,
[OA]. 116.
Fahrenfort, C., Zhao,
Z.: Effective Digital Object Access and Sharing Over a Networked
Environment using DOIP and NDN. In: 2019 15th International Conference on
eScience (eScience). pp. 632–633. IEEE, San Diego, CA, USA (2019). https://doi.org/10.1109/eScience.2019.00092,
[OA]. 117.
Demchenko, Y., Zhao,
Z., Surbiryala, J., Koulouzis,
S., Shi, Z., Liao, X., Gordiyenko, J.: Teaching DevOps
and Cloud Based Software Engineering in University Curricula. In: 2019 15th
International Conference on eScience (eScience). pp. 548–552. IEEE, San
Diego, CA, USA (2019). https://doi.org/10.1109/eScience.2019.00075,
[OA]. 118.
Ahanach, E. el K., Koulouzis, S., Zhao,
Z.: Contextual Linking between Workflow Provenance and System Performance
Logs. In: 2019 15th International Conference on eScience (eScience). pp.
634–635. IEEE, San Diego, CA, USA (2019). https://doi.org/10.1109/eScience.2019.00093,
[OA]. 119.
Prodan,
R., Saurabh, N., Zhao, Z., Orton-Johnson, K., Chakravorty, A., Karadimce, A.,
and Ulisses, A.: ARTICONF: Towards a Smart Social
Media Ecosystem in a Blockchain Federated Environment, in the 7th Workshop on
Large Scale Distributed Virtual Environments, in the context of Euro-Par
conference 2019, Gottingen, Germany (2019) https://doi.org/10.1007/978-3-030-48340-1_32
[OA]. 120.
Shi,
Z., Zhou, H., Hu, Y., Koulouzis, S., Rubia, C., and
Zhao, Z.: Co-located and
Orchestrated Network Fabric (CONF): An Automated Cloud Virtual Infrastructure
for Social Network Applications. in the 7th Workshop on Large Scale
Distributed Virtual Environments, in the context of Euro-Par conference 2019,
Gottingen, Germany (2019) https://doi.org/10.1007/978-3-030-48340-1_36,
[OA]. 121.
Zhou,
H., Shi, Z., Hu, Y., Donkers, P., Afanasyev, A., Koulouzis, S.,
Taal, A., Ulisses, A., Zhao, Z.: Large Distributed Virtual Infrastructure Partitioning
and Provisioning Across Providers. In: 2019 IEEE International Conference on
Smart Internet of Things (SmartIoT). pp. 56–63.
IEEE, Tianjin, China (2019). https://doi.org/10.1109/SmartIoT.2019.00018,
[OA] (Best student paper candidate). 122.
Zhao, Z., Liao, X., Martin, P., Maduro, J., Thijsse, P., Schaap, D., Stocker,
M., Goldfarb, D., Magagna, B.:
Knowledge-as-a-Service: A Community Knowledge Base for Research
Infrastructures in Environmental and Earth Sciences. In: 2019 IEEE World
Congress on Services (SERVICES). pp. 127–132. IEEE, Milan, Italy (2019). https://doi.org/10.1109/SERVICES.2019.00041,
[OA]. 123.
Hu,
Y., de Laat, C., Zhao, Z.:
Learning Workflow Scheduling on Multi-Resource Clusters. In: 2019 IEEE
International Conference on Networking, Architecture and Storage (NAS). pp.
1–8. IEEE, EnShi, China (2019). https://doi.org/10.1109/NAS.2019.8834720,
[OA]. 124.
Ahanach, E. el K., Koulouzis, S., Zhao,
Z.: Linking provenance with system logs: a context aware information
integration and exploration framework for analyzing
workflow execution. 10th International Workshop on Science Gateways (IWSG
2019), pp. 13-15 June 2019 (2019). [OA] 125.
Zhou,
H., Ouyang, X., Ren, Z., Su, J., de Laat, C., Zhao, Z.: A Blockchain based Witness Model
for Trustworthy Cloud Service Level Agreement Enforcement. In: IEEE INFOCOM
2019 - IEEE Conference on Computer Communications. pp. 1567–1575. IEEE,
Paris, France (2019). https://doi.org/10.1109/INFOCOM.2019.8737580,
[OA] Best presentation in the session. 126.
Shi,
Z., Zhou, H., Hu, Y., Jayachander, S., de Laat, C.,
Zhao, Z.: Operating Permissioned
Blockchain in Clouds: A Performance Study of Hyperledger Sawtooth. In: 2019
18th International Symposium on Parallel and Distributed Computing (ISPDC).
pp. 50–57. IEEE, Amsterdam, Netherlands (2019). https://doi.org/10.1109/ISPDC.2019.00010,
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Hu,
Y., De Laat, C., Zhao, Z.:
Multi-objective Container Deployment on Heterogeneous Clusters. In: 2019 19th
IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
(CCGRID). pp. 592–599. IEEE, Larnaca, Cyprus
(2019). https://doi.org/10.1109/CCGRID.2019.00076,
[OA] Best paper award. 128.
Zhou,
H., Koulouzis, S., Hu, Y., Wang, J., de Laat, C., Ulisses, A., Zhao,
Z.: Migrating Live Streaming Applications onto Clouds: Challenges and a CloudStorm Solution. In: 2018 IEEE/ACM International
Conference on Utility and Cloud Computing Companion (UCC Companion). pp.
321–326. IEEE, Zurich (2018). https://doi.org/10.1109/UCC-Companion.2018.00075,
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Zhou,
H., de Laat, C., Zhao, Z.:
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Update date: March 2, 2024