|
|
|||
|
Dr. Zhiming Zhao home
page |
|
||
|
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... |
||
|
|
|
||
I am an associate professor
at University of Amsterdam (UvA). I lead a team on “Quality
Critical Distributed Computing” in the context of Multiscale Networked
Systems (MNS)
research group, Informatics Institute (IvI). My research
focuses on innovative programming and
control models for quality critical systems on programmable
infrastructures such as Clouds, Edges, and Software Defined Networking using
optimization, semantic linking, blockchain, and artificial intelligence
technologies. I am specifically interested in big data management,
infrastructure optimization for data-intensive applications, and trustworthy
service level agreements. 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 package in its follow up project ENVRI-FAIR.
I also lead the UvA effort in ARTICONF, CLARIFY, BLUECLOUD, and VRE4EIC projects.
I am also the technical manager of the LifeWatch
ERIC Virtual Lab & Innovation Centre (VLIC)
in Amsterdam. |
||||
|
||||
|
Research: Quality Critical Applications on Programmable
Infrastructures |
|
|
|
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. |
|
|
||
|
Leader: Dr. Zhiming
Zhao Researchers and developer: ·
Vacancy ·
Dr. Yangjun Zhang, Knowledge discovery (2023-) ·
Gabriel Pelouze, NaaVRE, VL (2023-) ·
Dr. Spiros Koulouzis,
DRIP, CONF, VRE and use cases (2017-) Ph.D. students: ·
Vacancy ·
Yuandou Wang, Distributed data processing
(2020-) ·
Na Li, Knowledge discovery (2020-) ·
HongYun Liu, Cloud resource management (2019-) ·
Ruyue Xin, Virtual Infrastructure control
and adaptation (2019-) Visiting scholars: ·
Zijie Liu, Machine learning and job scheduling (2023-) ·
Yi Chen, Machine learning and job scheduling (2023-) Former members ·
J.M. van der Stoep, NaaVRE developer (2022-2023) ·
Zhengqiu Zhu, Incentive models in crowd
applications (2020-) ·
Dr. Uraz Odyurt, AI and cloud computing
(2021-) ·
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. Siamak Farshidi, Knowledge base and data set search (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] |
|
|
|
|
Funding and
projects ((Co)PI) |
|
|
|
1.
NWO LTER-LIFE
(a research infrastructure to develop Digital Twins of ecosystems in a
changing world), Large-Scale Research Infrastructure (LSRI), Duration July
2023-June 2032. 2.
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. 3.
EU H2020 CLARIFY (CLoud
ARtificial Intelligence For pathologY).
Grant No. 860627. H2020-MSCA-ITN-2019
call. Duration: Nov 2019-Oct 2023. 4.
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. Finished projects 5.
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. 6.
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. 7.
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. 8.
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. 9. 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 10. EU FP7 ENVRI. Task
leader, task 3.4: linking data and infrastructure - Common operations of
Environmental Research Infrastructure (ENVRI). Grant number 283465. 11. EU FP7 Geysers,
Researcher Generalised Architecture for Dynamic Infrastructure Services 12. Gigaport/CineGrid,
Researcher. Including network QoS in planning Workflows. 13. Dutch Virtual Laboratory for
e-Science (VL-e), 2005-2009 14. EU FP 6 CrossGrid, 2004-2005 |
|
|
||
|
|
|
|
|
|
|
|
||
Please feel
free contact me for
details. 1. We are looking for part
time programmers. 2. Open PhD/post doc
positions in big data infrastructures and applications. 3. We welcome CSC (Chinese
Scholar Council) funded students |
|
|
||
|
|
|
|
|
|
Community |
|
|
|
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
| 2019
| 2020) Workshop/Panel
chair
Program
committee
|
|
|||
|
|
|
||
|
|
|
||
|
|
|||
|
|
|
||
|
Journals 1. 1.
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. 2.
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. 3.
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. S0167739X23001061 (2023). https://doi.org/10.1016/j.future.2023.03.029. 4.
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. S0167739X23000973 (2023). https://doi.org/10.1016/j.future.2023.03.020[OA]. 5.
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.
6.
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.
7.
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. 8.
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
9.
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
10. 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. int.22994 (2022). https://doi.org/10.1002/int.22994
11. 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 12. 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
13. 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
14. 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. S0167739X22001881 (2022). https://doi.org/10.1016/j.future.2022.05.017.
15. 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. 16. 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. 17. 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 18. 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. 19. 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.
20. 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 21. 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.
22. 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] 23. 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. 24. 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]. 25. 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]. 26. 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. 27. 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. 28. 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. 29. 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]. 30. 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. 31. 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. 32. 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] 33. 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. 34. 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. 35. 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. 36. 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. 37. 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. 38. 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. 39. 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. 40. 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. 41. 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. 42. 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. 43. 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. 44. 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]. 45. 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. 46. 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. 47. 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. 48. 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. 49. 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. 50. 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. 51. 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) 52. 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. 53. 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.
54. 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]
55. 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] 56. 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] 57. 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. 58. Belloum, A., Deelman,
E. & Zhao, Z.: Scientific
workflows. Scientific Programming, 14(3-4), 171-171 (2006). [Full text] Book
(Eds) 59. 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 60. 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. 61. 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. 62. 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. 63. 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. 64. 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. 65. 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. 66. 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. 67. 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. 68. 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. 69. 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. 70. 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. 71. Martin, P., Chen, Y. Hardisty, A., Jeffery, K., and Zhao,
Z.: Computational
Challenges in Global Environmental Research Infrastructures. in
the book of Terrestrial Ecosystem Research Infrastructures: Challenges, New developments and Perspectives (2017). [ISBN 9781498751315] [OA]. 72. 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 73. 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. 74. 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. 75. 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. 76. 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]. 77. 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.
78. 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]. 79. 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. 80. 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
81. 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]. 82. 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] 83. 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] 84. 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]. 85. 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] 86. 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] 87. 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] 88. 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) 89. 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] 90. 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] 91. 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]. 92. Kruijer W., Wang Y., Koulouzis
S., Li N., Bianchi R., Zhao, Z.: FAIR-Cells: an interactive tool for
enabling the FAIRness of code fragments in Jupyter notebooks, in the proceedings of international
conference of High-Performance Computing and Simulation (HPCS) (2020), Spain.
[Zenodo] 93.
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]. 94. 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]. 95. 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]. 96. 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]. 97. 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]. 98. 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]. 99. 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]. 100.
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]. 101.
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]. 102.
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]. 103.
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]. 104.
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). 105.
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]. 106.
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]. 107.
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] 108.
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. 109.
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,
[OA]. 110.
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. 111.
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,
[OA]. 112.
Zhou,
H., de Laat, C., Zhao, Z.:
Trustworthy Cloud Service Level Agreement Enforcement with Blockchain Based
Smart Contract. In: 2018 IEEE International Conference on Cloud Computing
Technology and Science (CloudCom). pp. 255–260.
IEEE, Nicosia (2018). https://doi.org/10.1109/CloudCom2018.2018.00057,
[OA]. 113.
Hu,
Y., Zhou, H., de Laat, C., Zhao, Z.: ECSched: Efficient Container Scheduling on Heterogeneous
Clusters. In: Aldinucci, M., Padovani,
L., and Torquati, M. (eds.) Euro-Par 2018: Parallel
Processing. pp. 365–377. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-96983-1_26
[OA]. 114.
Qin,
Y., Chi, M., Liu, X., Zhang, Y., Zeng, Y., Zhao, Z.: Classification of High Resolution Urban Remote Sensing
Images Using Deep Networks by Integration of Social Media Photos. In: IGARSS
2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. pp.
7243–7246. IEEE, Valencia (2018). https://doi.org/10.1109/IGARSS.2018.8518538. 115.
Zhou,
H., Hu, Y., Su, J., Chi, M., de Laat, C., Zhao, Z.: Empowering Dynamic
Task-Based Applications with Agile Virtual Infrastructure Programmability.
In: 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). pp.
484–491. IEEE, San Francisco, CA, USA (2018). https://doi.org/10.1109/CLOUD.2018.00068. 116.
Zhou,
H., Hu, Y., Su, J., de Laat, C., Zhao, Z.: CloudsStorm:
An Application-Driven Framework to Enhance the Programmability and
Controllability of Cloud Virtual Infrastructures. In: Luo, M. and Zhang,
L.-J. (eds.) Cloud Computing – CLOUD 2018. pp. 265–280. Springer
International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-94295-7_18. 117.
Martin,
P., Remy, L., Theodoridou, M., Jeffery, K., Zhao,
Z.: Mapping metadata from different research infrastructures into a
unified framework for use in a virtual research environment. 10th
International Workshop on Science Gateways (IWSG 2018), 13-15 June 2018,
(2018). [PDF] 118.
Zhou,
H., Taal, A., Koulouzis, S., Wang, J., Hu, Y.,
Suciu, G., Poenaru, V., de Laat, C., Zhao, Z.: Dynamic Real-Time
Infrastructure Planning and Deployment for Disaster Early Warning Systems.
In: Shi, Y., Fu, H., Tian, Y., Krzhizhanovskaya,
V.V., Lees, M.H., Dongarra, J., and Sloot, P.M.A.
(eds.) Computational Science – ICCS 2018. pp. 644–654. Springer International
Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-93701-4_51. 119.
Koulouzis, S., Mousa, R., Karakannas,
A., de Laat, C., Zhao, Z.:
Information Centric Networking for Sharing and Accessing Digital Objects with
Persistent Identifiers on Data Infrastructures. In: 2018 18th IEEE/ACM
International Symposium on Cluster, Cloud and Grid Computing (CCGRID). pp.
661–668. IEEE, Washington, DC, USA (2018). https://doi.org/10.1109/CCGRID.2018.00098. 120.
Zhou,
H., de Laat, C., Zhao, Z.: Cloudsstorm: An Application-Driven Devops
Framework For Managing Networked Infrastructures On
Federated Clouds. (2018). https://doi.org/10.5281/ZENODO.1162914. 121.
Zhao, Z., Martin, P., Jones, A., Taylor, I., Stankovski, V., Salado, G.F., Suciu, G., Ulisses, A., de Laat, C.: Developing, Provisioning and Controlling
Time Critical Applications in Cloud. In: Mann, Z.Á. and Stolz, V. (eds.)
Advances in Service-Oriented and Cloud Computing. pp. 169–174. Springer
International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-79090-9_14. 122.
Wang,
J., Zhou, H., Hu, Y., De Laat, C., Zhao, Z.: Deadline-Aware Coflow Scheduling in a DAG. In: 2017 IEEE International
Conference on Cloud Computing Technology and Science (CloudCom).
pp. 341–346. IEEE, Hong Kong (2017). https://doi.org/10.1109/CloudCom.2017.55. 123.
Wang,
J., de Laat, C., Zhao, Z.: QoS-aware virtual SDN network planning. In:
2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).
pp. 644–647. IEEE, Lisbon, Portugal (2017). https://doi.org/10.23919/INM.2017.7987350. 124.
Taherizadeh, S., Taylor, I., Jones, A., Zhao, Z., Stankovski, V.: A Network Edge Monitoring Approach for
Real-Time Data Streaming Applications. In: Bañares,
J.Á., Tserpes, K., and Altmann, J. (eds.) Economics
of Grids, Clouds, Systems, and Services. pp. 293–303. Springer International
Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-61920-0_21. 125.
Koulouzis, S., Martin, P., Carval, T., Grenier,
B., Judeau, G., Heikkinen, J., Wang, J., Zhou, H.,
Hu, Y., De Laat, C., Zhao, Z.: Seamless Infrastructure Customisation
and Performance Optimisation for Time-critical Services in Data
Infrastructures. (2017). https://doi.org/10.5281/ZENODO.1570919. 126.
Hu,
Y., Wang, J., Zhou, H., Martin, P., Taal, A., de Laat, C., Zhao, Z.: Deadline-Aware
Deployment for Time Critical Applications in Clouds. In: Rivera, F.F., Pena,
T.F., and Cabaleiro, J.C. (eds.) Euro-Par 2017:
Parallel Processing. pp. 345–357. Springer International Publishing, Cham
(2017). https://doi.org/10.1007/978-3-319-64203-1_25. 127.
Elzinga, O., Koulouzis, S., Taal,
A., Wang, J., Hu, Y., Zhou, H., Martin, P., de Laat, C., Zhao, Z.: Automatic
Collector for Dynamic Cloud Performance Information. In: 2017 International
Conference on Networking, Architecture, and Storage (NAS). pp. 1–6. IEEE,
Shenzhen, China (2017). https://doi.org/10.1109/NAS.2017.8026845. 128.
Zhou,
H., Wang, J., Hu, Y., Su, J., Martin, P., De Laat,
C., Zhao, Z.: Fast Resource Co-provisioning for Time Critical
Applications Based on Networked Infrastructures. In: 2016 IEEE 9th
International Conference on Cloud Computing (CLOUD). pp. 802–805. IEEE, San
Francisco, CA, USA (2016). https://doi.org/10.1109/CLOUD.2016.0111. 129.
Zhou,
H., Martin, P., Su, J., De Laat, C., Zhao, Z.: A
Flexible Inter-Locale Virtual Cloud For Nearly Real-Time
Big Data Applications. (2016). https://doi.org/10.5281/ZENODO.204774. 130.
Zhou,
H., Hu, Y., Wang, J., Martin, P., De Laat, C., Zhao, Z.: Fast and
Dynamic Resource Provisioning for Quality Critical Cloud Applications. In:
2016 IEEE 19th International Symposium on Real-Time Distributed Computing
(ISORC). pp. 92–99. IEEE, York, United Kingdom (2016). https://doi.org/10.1109/ISORC.2016.22. 131.
Zhao,
Z., Martin, P., De Laat, C.,
Jeffery, K., Jones, A., Taylor, I., Hardisty, A., Atkinson, M., Zuiderwijk, A., Yin, Y., Chen, Y.: Time Critical
Requirements And Technical Considerations For
Advanced Support Environments For Data-Intensive Research. (2016). https://doi.org/10.5281/ZENODO.204756. 132.
Taherizadeh, S., Jones, A.C., Taylor, I., Zhao, Z., Martin, P.,
Stankovski, V.: Runtime Network-Level Monitoring
Framework. In The Adaptation Of Distributed
Time-Critical Cloud Applications. Zenodo (2016). https://doi.org/10.5281/ZENODO.53869. 133.
Petcu, D., Fazio, M., Prodan, R., Zhao, Z., Rak, M.: On the Next
Generations of Infrastructure-as-a-Services: In: Proceedings of the 6th
International Conference on Cloud Computing and Services Science. pp.
320–326. SCITEPRESS - Science and and Technology
Publications, Rome, Italy (2016). https://doi.org/10.5220/0005912503200326. 134.
Martin,
P., Taal, A., Quevedo, F., Rogers, D., Evans, K., Jones, A., Stankovski, V., Taherizadeh,
S., Trnkoczy, J., Suciu, G., Zhao, Z.: Information Modelling and Semantic Linking for a
Software Workbench for Interactive, Time Critical and Self-Adaptive Cloud
Applications. In: 2016 30th International Conference on Advanced Information
Networking and Applications Workshops (WAINA). pp. 127–132. IEEE, Crans-Montana, Switzerland (2016). https://doi.org/10.1109/WAINA.2016.38. 135.
Casale, G., Chesta, C., Deussen, P., Di Nitto, E., Gouvas,
P., Koussouris, S., Stankovski,
V., Symeonidis, A., Vlassiou,
V., Zafeiropoulos, A., Zhao, Z.: Current and Future Challenges of Software Engineering
for Services and Applications. Procedia Computer Science. 97, 34–42 (2016). https://doi.org/10.1016/j.procs.2016.08.278. 136.
Zhao, Z., Taal, A., Jones, A., Taylor, I., Stankovski, V., Vega, I.G., Hidalgo, F.J., Suciu, G., Ulisses, A., Ferreira, P., Laat, C. de: A Software
Workbench for Interactive, Time Critical and Highly Self-Adaptive Cloud
Applications (SWITCH). In: 2015 15th IEEE/ACM International Symposium on
Cluster, Cloud and Grid Computing. pp. 1181–1184. IEEE, Shenzhen, China
(2015). https://doi.org/10.1109/CCGrid.2015.73. 137.
Zhao, Z., Martin, P., Wang, J., Taal, A., Jones, A., Taylor,
I., Stankovski, V., Vega, I.G., Suciu, G., Ulisses, A., de Laat, C.: Developing and Operating Time
Critical Applications in Clouds: The State of the Art and the SWITCH
Approach. Procedia Computer Science. 68, 17–28 (2015). https://doi.org/10.1016/j.procs.2015.09.220. 138.
Zhao, Z., Martin, P., Grosso, P., Los, W., Laat, C. de,
Jeffrey, K., Hardisty, A., Vermeulen, A., Castelli, D., Legre,
Y., Kutsch, W.: Reference Model Guided System
Design and Implementation for Interoperable Environmental Research
Infrastructures. In: 2015 IEEE 11th International Conference on e-Science.
pp. 551–556. IEEE, Munich, Germany (2015). https://doi.org/10.1109/eScience.2015.41. 139.
Mork,
R., Martin, P., Zhao, Z.:
Contemporary challenges for data-intensive scientific workflow management systems.
In: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale
Science - WORKS ’15. pp. 1–11. ACM Press, Austin, Texas (2015). https://doi.org/10.1145/2822332.2822336. 140.
Martin,
P., Grosso, P., Magagna, B., Schentz,
H., Chen, Y., Hardisty, A., Los, W., Jeffery, K., Laat, C. de, Zhao, Z.: Open Information Linking
for Environmental Research Infrastructures. In: 2015 IEEE 11th International
Conference on e-Science. pp. 513–520. IEEE, Munich, Germany (2015). https://doi.org/10.1109/eScience.2015.66. 141.
Jeferry, K., Kousiouris, G., Kyriazis, D., Altmann, J., Ciuffoletti,
A., Maglogiannis, I., Nesi,
P., Suzic, B., Zhao,
Z.: Challenges Emerging from Future Cloud Application Scenarios. Procedia
Computer Science. 68, 227–237 (2015). https://doi.org/10.1016/j.procs.2015.09.238. 142.
Evans,
K., Trnkoczy, J., Suciu, G., Suciu, V., Martin, P.,
Wang, J., Zhao, Z., Jones, A., Preece, A., Quevedo, F., Rogers, D., Spasić,
I., Taylor, I., Stankovski, V., Taherizadeh,
S.: Dynamically reconfigurable workflows for time-critical applications. In:
Proceedings of the 10th Workshop on Workflows in Support of Large-Scale
Science - WORKS ’15. pp. 1–10. ACM Press, Austin, Texas (2015). https://doi.org/10.1145/2822332.2822339. 143.
Zhao, Z., Wibisono, A., Grosso, P., Los, W., De Laat, C.,
Chen, Y., Hardisty, A., Martin, P., Atkinson, M., Schentz,
H., Magagna, B.: Oeilm: A
Semantic Linking Framework For Environmental
Research Infrastructures. (2013). https://doi.org/10.5281/ZENODO.205781. 144.
Jiang,
W., Zhao, Z., Wibisono, A., Grosso,
P., Laat, C. de: Dynamic Workflow Planning on Programmable Infrastructure.
In: 2013 IEEE Eighth International Conference on Networking, Architecture and
Storage. pp. 326–330. IEEE, Xi’an, Shaanxi, China (2013). https://doi.org/10.1109/NAS.2013.53. 145.
Jiang,
W., Zhao, Z., Laat, C. de: An
Autonomous Security Storage Solution for Data-Intensive Cooperative Cloud
Computing. In: 2013 IEEE 9th International Conference on e-Science. pp.
369–372. IEEE, Beijing, China (2013). https://doi.org/10.1109/eScience.2013.31. 146.
Chen,
Y., Martin, P., Schentz, H., Magagna, B., Zhao, Z., Hardisty,
A., Preece, A., Atkinson, M., Huber, R.,
and Legre. R.: A common reference model for environmental science research
infrastructures. In Proceedings of EnviroInfo2013, (2013). [Full text] 147.
Chen,
Y., Hardisty, A., Preece, A., Atkinson,
M., Martin, P., Zhao, Z., Schentz,
H., Magagna, B. and Legre. R., (2013) Analysis of
Common Requirements for Environmental Science Research Infrastructures.
In Proceedings of The International Symposium on Grids and Clouds
(ISGC). [Full text] 148.
Dumitru,
C., Zhao, Z., Grosso, P., de Laat,
C.: HybridFlow: Towards intelligent video delivery
and processing over hybrid infrastructures. In: 2013 International Conference
on Collaboration Technologies and Systems (CTS). pp. 473–478. IEEE, San
Diego, CA, USA (2013). https://doi.org/10.1109/CTS.2013.6567271. 149.
Zhu,
H., van der Veldt, K., Grosso, P., Zhao, Z., Liao, X., and
de Laat, C.: Energy aware Semantic
modelling in large scale infrastructure, International conference on
Green Com, France, (2012) [Fulltext]. 150.
Belloum, A.S.Z., Cushing, R., Koulouzis,
S., Korkhov, V., Vasunin,
D., Guevara-Masis, V., Zhao, Z., Bubak, M.: Support for
Cooperative Experiments in e-Science: From Scientific Workflows to Knowledge
Sharing. In: Roterman-Konieczna, I. (ed.)
Identification of Ligand Binding Site and Protein-Protein Interaction Area.
pp. 135–159. Springer Netherlands, Dordrecht (2013). https://doi.org/10.1007/978-94-007-5285-6_7. 151.
Zhao, Z., Ham, J. van der, Taal, A., Koning, R., Dumitru, C.,
Wibisono, A., Grosso, P., Laat, C. de: Planning Data Intensive Workflows on
Inter-domain Resources Using the Network Service Interface (NSI). In: 2012 SC
Companion: High Performance Computing, Networking Storage and Analysis. pp.
150–156. IEEE, Salt Lake City, UT (2012). https://doi.org/10.1109/SC.Companion.2012.30. 152.
Zhao, Z., Grosso, P., de Laat, C.: OEIRM: An Open Distributed
Processing Based Interoperability Reference Model for e-Science. In: Park,
J.J., Zomaya, A., Yeo, S.-S., and Sahni, S. (eds.) Network and Parallel Computing. pp.
437–444. Springer Berlin Heidelberg, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35606-3_52. 153.
Zhao, Z., Dumitru, C., Grosso, P., de Laat, C.: Network
Resource Control for Data Intensive Applications in Heterogeneous Infrastructures.
In: 2012 IEEE 26th International Parallel and Distributed Processing
Symposium Workshops & PhD Forum. pp. 2069–2076. IEEE, Shanghai, China
(2012). https://doi.org/10.1109/IPDPSW.2012.243. 154.
Pavlov,
D., Soeurt, J., Grosso, P., Zhao, Z., Veldt, K. van der, Zhu, H., Laat, C. de: Towards Energy
Efficient Data Intensive Computing Using IEEE 802.3az. In: 2012 SC Companion:
High Performance Computing, Networking Storage and Analysis. pp. 806–810.
IEEE, Salt Lake City, UT (2012). https://doi.org/10.1109/SC.Companion.2012.112. 155.
Demchenko, Y., Zhao,
Z., Grosso, P., Wibisono, A., de Laat, C.: Addressing Big Data challenges
for Scientific Data Infrastructure. In: 4th IEEE International Conference on
Cloud Computing Technology and Science Proceedings. pp. 614–617. IEEE,
Taipei, Taiwan (2012). https://doi.org/10.1109/CloudCom.2012.6427494. 156.
Zhao, Z., Taal, A., Grosso, P., de Laat, C.: Resource
Discovery in Large Scale Network Infrastructure. In: 2011 IEEE Sixth International
Conference on Networking, Architecture, and Storage. pp. 186–190. IEEE,
Dalian, China (2011). https://doi.org/10.1109/NAS.2011.43. 157.
Zhao, Z., Grosso, P., Koning, R., van der Ham, J., de Laat,
C.: An agent based planner for including network QoS
in scientific workflows. In: Proceedings of the International Multiconference
on Computer Science and Information Technology. pp. 231–238. IEEE, Wisla (2010). https://doi.org/10.1109/IMCSIT.2010.5680060,
Best paper award. 158.
Zhao, Z., Grosso, P., Koning, R., van der Ham, J., de Laat,
C.: Network resource selection for data transfer processes in scientific
workflows. In: The 5th Workshop on Workflows in Support of Large-Scale
Science. pp. 1–6. IEEE, New Orleans, LA, USA (2010). https://doi.org/10.1109/WORKS.2010.5671840. 159.
Zhao, Z., Grosso, P., Koning, R., Ham, J., de Laat, C.: An
Architecture Including Network QoS in Scientific Workflows. In: 2010 Ninth International
Conference on Grid and Cloud Computing. pp. 104–109. IEEE, Nanjing, China
(2010). https://doi.org/10.1109/GCC.2010.32. 160.
Zhao, Z., Belloum, A., Bubak, M., Hertzberger, B.:
Support for Cooperative Experiments in VL-e: From Scientific Workflows to
Knowledge Sharing. In: 2008 IEEE Fourth International Conference on eScience.
pp. 329–330. IEEE, Indianapolis, IN, USA (2008). https://doi.org/10.1109/eScience.2008.120. 161.
Wibisono,
A., Zhao, Z., Belloum,
A., Bubak, M.: A Framework for Interactive
Parameter Sweep Applications. In: Bubak, M., van Albada, G.D., Dongarra, J., and
Sloot, P.M.A. (eds.) Computational Science – ICCS 2008. pp. 481–490. Springer
Berlin Heidelberg, Berlin, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69389-5_55. 162.
Wibisono,
A., Zhao, Z., Belloum,
A., Bubak, M.: A Framework for Interactive
Parameter Sweep Applications. In: 2008 Eighth IEEE International Symposium on
Cluster Computing and the Grid (CCGRID). pp. 703–703. IEEE, Lyon, France
(2008). https://doi.org/10.1109/CCGRID.2008.111. 163.
Belloum, A., Zhao,
Z., Bubak, M.: International Workshop on
Applications of Workflows in Computational Science (AWCS 08). In: Bubak, M., van Albada, G.D., Dongarra, J., and Sloot, P.M.A. (eds.) Computational
Science – ICCS 2008. pp. 459–462. Springer Berlin Heidelberg, Berlin,
Heidelberg (2008). https://doi.org/10.1007/978-3-540-69389-5_52. 164.
Zhao, Z., Belloum, A., De Laat, C.,
Adriaans, P., Hertzberger,
B.: Using Jade agent framework to prototype an e-Science workflow bus. In:
Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid ’07). pp. 655–660. IEEE, Rio de Janeiro, Brazil
(2007). https://doi.org/10.1109/CCGRID.2007.120. 165.
Zhao, Z., Belloum, A., de Laat, C.,
Adriaans, P., Hertzberger,
B.: Distributed execution of aggregated multi domain workflows using an agent
framework. In: 2007 IEEE Congress on Services (Services 2007). pp. 183–190.
IEEE, Salt Lake City, UT, USA (2007). https://doi.org/10.1109/SERVICES.2007.30. 166.
Wibisono,
A., Vasyunin, D., Korkhov,
V., Zhao, Z., Belloum,
A., de Laat, C., Adriaans, P., Hertzberger,
B.: WS-VLAM: A GT4 Based Workflow Management System. In: Shi, Y., van Albada, G.D., Dongarra, J., and
Sloot, P.M.A. (eds.) Computational Science – ICCS 2007. pp. 191–198. Springer
Berlin Heidelberg, Berlin, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72588-6_34. 167.
Terpstra,
F., Zhao, Z., Mulder, W., Adriaans, P.: Towards a Formal Foundation for Aggregating
Scientific Workflows. In: Shi, Y., van Albada,
G.D., Dongarra, J., and Sloot, P.M.A. (eds.)
Computational Science – ICCS 2007. pp. 216–219. Springer Berlin Heidelberg,
Berlin, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72588-6_38. 168.
Zhao, Z., Booms, S., Belloum, A.,
Laat, C., Hertzberger, B.: VLE-WFBus:
A Scientific Workflow Bus for Multi e-Science Domains. In: 2006 Second IEEE
International Conference on e-Science and Grid Computing (e-Science’06). pp.
11–11. IEEE, Amsterdam, The Netherlands (2006). https://doi.org/10.1109/E-SCIENCE.2006.261095. 169.
Zhao, Z., van Albada, D., Sloot, P.:
Rapid Prototyping of Complex Interactive Simulation Systems. In: 10th IEEE
International Conference on Engineering of Complex Computer Systems
(ICECCS’05). pp. 366–375. IEEE, Shanghai, China (2005). https://doi.org/10.1109/ICECCS.2005.69. 170.
Zhao, Z., Belloum, A., Yakali, H., Sloot, P., Hertzberger,
B.: Dynamic Work.ow in a Grid Enabled Problem
Solving Environment. In: The Fifth International Conference on Computer and Information
Technology (CIT’05). pp. 339–345. IEEE, Shanghai, China (2005). https://doi.org/10.1109/CIT.2005.101. 171.
Zhao, Z., Belloum, A., Wibisono,
A., Terpstra, F., de Boer, P.T., Sloot, P., Hertzberger,
B.: Scientific workflow management: between generality and applicability. In:
Fifth International Conference on Quality Software (QSIC’05). pp. 357–364.
IEEE, Melbourne, Australia (2005). https://doi.org/10.1109/QSIC.2005.56. 172.
Zhao, Z., Belloum, A., Sloot, P., Hertzberger, B.: Agent technology and scientific workflow
management in an e-science environment. In: 17th IEEE International Conference
on Tools with Artificial Intelligence (ICTAI’05). pp. 5 pp. – 23. IEEE, Hong
Kong, China (2005). https://doi.org/10.1109/ICTAI.2005.29. 173.
Zhao, Z., Belloum, A., Sloot, P., Hertzberger, B.: Agent Technology and Generic Workflow
Management in an e-Science Environment. In: Zhuge, H. and Fox, G.C. (eds.) Grid and Cooperative Computing -
GCC 2005. pp. 480–485. Springer Berlin Heidelberg, Berlin, Heidelberg (2005).
https://doi.org/10.1007/11590354_61. 174.
Zhao,
Z. (2004, december 09). An agent
based architecture for constructing Interactive Simulation Systems. UvA Universiteit van Amsterdam. Promoter/co-promoter: prof. dr.
P.M.A. Sloot & dr. G.D. van Albada. [Link] 175.
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. 176.
Zajac,
K., Tirado-Ramos, A., Zhao, Z.,
Sloot, P., Bubak, M.: Grid Services for HLA-Based
Distributed Simulation Frameworks. In: Fernández Rivera, F., Bubak, M., Gómez Tato, A., and Doallo,
R. (eds.) Grid Computing. pp. 147–154. Springer Berlin Heidelberg,
Berlin, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24689-3_19. 177.
Zhao, Z., van Albada, G.D.,
Tirado-Ramos, A., Zajac, K., Sloot, P.M.A.: ISS-Studio: A Prototype for a
User-Friendly Tool for Designing Interactive Experiments in Problem Solving
Environments. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Dongarra, J.J., Zomaya, A.Y.,
and Gorbachev, Y.E. (eds.) Computational Science — ICCS 2003. pp. 679–688.
Springer Berlin Heidelberg, Berlin, Heidelberg (2003). https://doi.org/10.1007/3-540-44860-8_70. 178.
Tirado-Ramos,
A., Zajac, K., Zhao, Z., Sloot,
P.M.A., van Albada, D., Bubak,
M.: Experimental Grid Access for Dynamic Discovery and Data Transfer in
Distributed Interactive Simulation Systems. In: Sloot, P.M.A., Abramson, D.,
Bogdanov, A.V., Dongarra, J.J., Zomaya,
A.Y., and Gorbachev, Y.E. (eds.) Computational Science — ICCS 2003. pp.
284–292. Springer Berlin Heidelberg, Berlin, Heidelberg (2003). https://doi.org/10.1007/3-540-44860-8_29. 179.
Zhao, Z., Belleman, R.G., van Albada, G.D., Sloot, P.M.A.: AG-IVE: An Agent Based
Solution to Constructing Interactive Simulation Systems. In: Sloot, P.M.A.,
Hoekstra, A.G., Tan, C.J.K., and Dongarra, J.J.
(eds.) Computational Science — ICCS 2002. pp. 693–703. Springer Berlin
Heidelberg, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-46043-8_70. 180.
Zhao,
Z., Belleman, R.G., Albada, G.D. van & Sloot, P.M.A. (2002). Reusability and
Efficiency in Constructing Interactive Simulation Systems. In E.F. Deprettere,
A.S.Z. Belloum, J.W.J. Heijnsdijk & F. van der Stappen (Eds.),
ASCI 2002, Proceedings of the eighth annual conference of the Advanced School
for Computing and Imaging (pp. 268-275). Delft: ASCI. [Link] 181.
Zhao,
Z., Belleman, R.G., Albada, G.D. van & Sloot, P.M.A. (2002). Scenario Switches and
State Updates in an Agent-based Solution to Constructing Interactive
Simulation Systems. In
Proceedings of the Communication Networks and Distributed Systems Modeling and Simulation Conference (CNDS 2002) (pp.
3-10). [Link] 182.
Zhao,
Z., Belleman R.
G., Albada G.D.and Sloot P.M.A.
(2001): System integration for
interactive simulation systems using intelligent agents, in
R.L. Lagendijk; J.W.J. Heijnsdijk; A.D. Pimentel and M.H.F. Wilkinson, editors,
Proceedings of the 7th annual conference of the Advanced School for Computing
and Imaging, pp. 399-406. ASCI, May 2001. ISBN 90-803086-6-8. [Link] 183.
Belleman,R.G., Zhao,Z., Albada G.D.
van and Sloot P.M.A (2000) Design considerations for the construction of immersive dynamic
exploration environments, in L.J. van Vliet; J.W.J. Heijnsdijk; T. Kielmann and
P.M.W. Knijnenburg, editors, ASCI 2000, Proceedings
of the sixth annual conference of the Advanced School for Computing and
Imaging, pp. 195-201. ASCI, Delft, June 2000. ISBN 90-803086-5-x. [Link] Peer-reviewed
abstracts and posters 184.
Shi,
Y., Koulouzis, S., Bianchi, R., Timmermans, J.,
Kissling, W. D., and Zhao, Z.: Generating geospatial data products of
ecosystem structure from LiDAR using Notebook-as-a-VRE (NaaVRE),
EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11716, https://doi.org/10.5194/egusphere-egu23-11716,
2023. 185.
Petzold, A., Bundke, U.,
Schleiermacher, C., Gomes, A. R., Seemeyer, K., Adamaki, A., Vermeulen, A., Zhao, Z., Boulanger,
D., Carval, T., and Hienola, A.: The ENVRI-Hub as a
service for accelerating FAIRification of the
Environment Domain Research Infrastructures, EGU General Assembly 2023,
Vienna, Austria, 24–28 Apr 2023, EGU23-7708, https://doi.org/10.5194/egusphere-egu23-7708,
2023. 186.
Petzold, A., Asmi, A., Gomes, R., Seemeyer,
K., Adamaki, A., Vermeulen, A., Bailo,
D., Jeffery, K., Glaves, H., Zhao, Z.,
Stocker, M., Hellstrom, M.: Creating ENVRI-hub, the
Open-Access Platform of the Environmental Sciences Community in Europe. 2021,
IN53A-04 (2021). [2021AGUFMIN53A..04P] 187.
Alex
Boyko, Siamak Farshidi, Zhiming
Zhao, State-of-the-art instance matching methods for knowledge graphs,
The Sixteenth International Workshop on Ontology Matching (OM-2021), in the
context of 20th International Semantic Web Conference ISWC-2021, online [OA] 188.
Koulouzis, S., Shi, Y., Wan, Y., Bianchi, R., Kissling, D., Zhao,
Z.: Enabling LiDAR data processing as a service in a Jupyter
environment (2021). EGU General Assembly 2021, online, 19–30 Apr 2021,
EGU21-8294, https://doi.org/10.5194/egusphere-egu21-8294 189.
Petzold, A., Asmi, A., Seemeyer,
K., Adamaki, A., Vermeulen, A., Bailo,
D., Jeffery, K., Glaves, H., Zhao, Z.,
Stocker, M., Hellström, M.: Advancing the FAIRness and Openness of Earth system science in Europe. pico (2021). https://doi.org/10.5194/egusphere-egu21-8052 190.
Liao,
X., Goldfarb, D., Magagna, B., Stocker, M., Thijsse, P., Schaap, D., Zhao,
Z.: ENVRI knowledge base: A community knowledge base for research, innovation and society. oral (2020). https://doi.org/10.5194/egusphere-egu2020-20708 191.
Zhao, Z., Martin, P., Koulouzis, S.: (2019)
Optimizing environmental data services on federated Cloud and
e-Infrastructures, EGU, April 2019 [2019EGUGA..21.3683Z] 192.
Koulouzis, S., Carval, T., Martin, P., Grenier,
B., Chen, Y., Heikkinen, J., Zhao, Z.: Dynamic Optimization for
Time-critical Data Services: A Case Study in Euro-Argo Research
Infrastructure. 20th EGU General
Assembly, EGU2018, Proceedings from the conference held 4-13 April, 2018 in Vienna, Austria, p.16012. [2018EGUGA..2016012K] 193.
Nieva de la Hidalga, A.,
Hardisty, A.R., Magagna, B., Martin, P.W., Zhao,
Z.: Use of the ENVRI Reference Model to Support the Design of Environmental
Research Infrastructures. 18552 (2018). [2018EGUGA..2018552N] 194.
Kutsch, W. L.; Zhao, Z.; Hardisty,
A.; Hellström, M.; Chin, Y.; Magagna, B.; Asmi, A.; Papale,
D.; Pfeil, B.; Atkinson, M. (2017) Data interoperabilty between European Environmental
Research Infrastructures and their contribution to global data networks, AGU
2018, American Geophysical Union, Fall Meeting 2017, abstract
#IN44B-01. [2017AGUFMIN44B..01K] Technical
report and newsletters 195.
Zhao,
Z., Martin, P. Jeffery K.,
(2017) VRE in the Data for Science Approach to Common Challenges in
ENVRIPLUS, ERCIM, Newsletter, April 2017 196.
Zhao,
Z. (2016) Data for
science: software and solutions for the environmental sciences, EGI Newsletter, January 2016. 197.
Zhao,
Z., (2015) Data for
Science theme: software and solutions to address common challenges facing
environmental research infrastructures, 1st ENVRIPLUS Newsletter, November 2015. 198.
Ghijsen, M.,
Ham, J. van der, Grosso, P., Dumitru, C., Zhu, H., Zhao, Z. & Laat,
C. de (2013). A
semantic-web approach for modeling computing
infrastructures.
(SNE technical report 2013-01). Amsterdam: Universiteit van
Amsterdam, System and Network Engineering. [Full
text] 199.
Hertzberger, L.O., Belleman,
R.G., Jansen, M.G., Zhao, Z., Hooft, P. van, Belloum, A.S.Z., Mirzadeh,
N., Yakali, H.H., Liere,
R. van, Nuallain, B.S., Verstoep, K., Groep, D.L.
& Bouwhuis, M.C. (2005). Recommendation
to VLeIT: Scientific workflow management
systems for the PoC r1. (Internal
report). Amsterdam: Informatics Institute. Supervised
PhD thesis 200.
Yang Hu (2019) Resource
Scheduling for Quality-Critical Applications on Cloud Infrastructure,
University of Amsterdam, [ISBN: 978-94-028-1713-3]. 201.
Huan Zhou (2019)
Seamless Infrastructure Programming and Control for Quality-critical Cloud
Applications, [ISBN: 978-94-028-1727-0] 202.
Zeshun
Shi (2022) Enhancing Service-Level Agreements using Decentralized Auctions
and Witnesses, [ISBN: 978-94-6421-916-6] In
Chinese 203.
Zhao,
Z., Liao, X., Wang,
X., Ruan, C., Zhu, Y., Feng,
D. (2019), An Reference Model
approach for developing agriculture big data infrastructures, Journal of East
China Normal University (Natural Sc
2019 Vol.2019 (2): 77-96, [10.3969/j.issn.1000-5641.2019.02.009] 204.
Demchenko, Y., Zhao, Z., Grosso,
P., Wibisono, A., de Laat, C., (2013) 科研信息化基础 设施的大数据挑战 (Big Data Challenges for e-Science
Infrastructure) China
Science and Technology Resources Review, Vol.45 No.1 30-35,40 Jan. 2013. ISSN
1674-1544 [10.3772/j.issn.1674-1544.2013.01.006] |
|
|
|