Dr. Zhiming Zhao home page

 

Multiscale Networked Systems (MNS)

University of Amsterdam

 

Email: z.zhao[at]uva.nl

Tel: +31 638560996

Office:

 C.3.145

 Science park 904, 1098XH Amsterdam

 the Netherlands

We are looking for

1.     Part time programmers

2.     Post doctors

3.     PhD students...

About me

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About me                     

Recent activities

1.     IEEE DAPPS 2022

...

 

 

 

I am an assistant professor and senior researcher at University of Amsterdam (UvA). I lead a team on “Quality Critical Distributed Computing” in the context of Multiscale Networked Systems (MNS) research group, the System and Networking Lab (SNE). 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 agreement. 

Since 2014, I obtained more than 3M euro funding from the EU H2020 research program to support my research activities in UvA. I am the scientific coordinator of the project SWITCH (Software Workbench for interactive time critical and highly self-adaptive cloud applications). I am the leader of the Data for Science theme in the environmental science cluster project ENVRIPLUS and lead 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 was involved in a number of other projects, such as the EU FP7 ENVRI project. I also lead the Virtual Research Environment (VRE) development in the LifeWatch Virtual Lab & Innovation Center.

 

 LifeWatch-ERIC

 

 

 

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.

 

 

 

 

Leader: Dr. Zhiming Zhao 

Researchers:

·       Dr. Uraz Odyurt, AI and cloud computing (2021-)

·       Mr. Riccardo Bianchi (M.Sc), DevOps (2020-)

·       Dr. Siamak Farshidi, Knowledge base and data set search (2020-)

·       Dr. Spiros Koulouzis, DRIP, CONF, VRE and use cases (2017-)

Ph.D. students:

·       Yuandou Wang, Distributed data processing (2020-)

·       Na Li, Knowledge discovery (2020-)

·       HongYun Liu, Cloud resource management (2019-)

·       Zhengqiu Zhu, Incentive models in crowd applications (2020-)

·       Ruyue Xin, Virtual Infrastructure control and adaptation (2019-)

·       Zeshun Shi, Trustworthy Virtual infrastructure (2018-)

Visiting scholars:

·       Mr. Ning Chen, Sensor network and robustness (2021-)

Former members

·       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 (PI)                                        

 

 

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Current projects

1.     EU H2020 CLARIFY (CLoud ARtificial Intelligence For pathologY). Grant No. 860627. H2020-MSCA-ITN-2019 call. [Newsletter in IvI] Duration: Nov 2019-Oct 2023.

2.     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.

3.     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 [Newsletter in IvI].

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 [Newsletter in IvI].

Finished projects

1.     EU H2020 ENVRIPLUS (ENVironmental Research Infrastructures Providing shared soLUtions for Science and society). Leader of the Data for Science Theme.  Grant No. 654182 (News letter in IvI, ENVRIPLUS, EGI). Duration: May 2015- April 2019, Project website: www.envriplus.eu. 

2.     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. (Newsletter in IvI)

3.     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. (Newsletter in IvI) (SWITCH project).

Participated projects

1.     EU FP7 ENVRI. Task leader, task 3.4: linking data and infrastructure - Common operations of Environmental Research Infrastructure (ENVRI). Grant number 283465.

2.     EU FP7 Geysers, Researcher Generalised Architecture for Dynamic Infrastructure Services

3.     Gigaport/CineGrid, Researcher. Including network QoS in planning Workflows. 

4.     Dutch Virtual Laboratory for e-Science (VL-e), 2005-2009

5.     EU FP 6 CrossGrid, 2004-2005

 

 

 

 

 

 

 

Open positions            

 

 

Home

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

 

 

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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. Nishant Saurabh, Zhiming Zhao, Dragi Kimovski, international workshop on intelligent and adaptive edge-cloud operations and services
    1. 1st INTEL4EC, in conjunction with UCC 2022, USA
  2. Kaiwen Zhang, Zhiming Zhao, Vanessa Daza, program chair, IEEE Decentralized Application, and infrastructure
    1. DApps 2022, USA
  3. Chen R., Zhao. Z., International Workshop on Network-Aware Big Data Computing (NEAC)
    1. 1st, in conjunction with CCGrid 2019, Cyprus
    2. 2nd, in conjunction with CCGrid 2020, Virtual
    3. 3rd, in conjunction with CCGrid 2021, Virtual
  4. Zhao, Z., et al., International workshop on interoperable infrastructures for interdisciplinary big data sciences (IT4RIs)
    1. 1st IT4RIs, in conjunction with IEEE e-Science 2015, Munich, Germany
    2. 2nd IT4RIs, in conjunction with IEEE RTSS (Real-time System Symposium) 2016, Porto, Portugal
    3. 3rd IT4RIs, in conjunction with Amsterdam HPC 2018, Amsterdam, the Netherlands
  5. Zhao, Z., Keith, J., Magagana, B., Stocker, M., Martin, P.,

1.     Special session: Using heterogeneous environmental data for system-level science, 2018, Vienna, EGU

  1. Chen, Y., et al., Zhao, Z., Session on Towards Environmental Research Infrastructure as a Service for the Open Science Cloud

1.     in Digital Infrastructure for research 2016, Krakow, Poland

2.     in Digital Infrastructure for research 2017, Brussels, Belgium

  1. Zhao, Z., et al., International workshop on Workflow Systems in E-Sciences (WSES),
    1. 1st WSES, in conjunction with ICCS 2006, Reading, UK
    2. 2nd WSES, in conjunction with ICCS 2007, Beijing, China
    3. 3rd WSES, in conjunction with IEEE CCGrid 2008, Lyon, France
    4. 4th WSES, in conjunction with IEEE CCGrid 2009, Shanghai, China
  2. Belloum, A., Bubak, M., Zhao, Z., International Workshop on Applications of workflows in Computational Science (AWCS) 
    1. AWCS, in conjunction with ICCS, 2008
  1. Belloum, A., Zhao, Z., et al, International Workshop on Scientific Workflows and Business Workflow Standards in e-Science (SWBES),
    1. 1st SWBES, in conjunction with IEEE e-Science 2006, Amsterdam, the Netherlands
    2. 2nd SWBES, in conjunction with IEEE e-Science 2007, Bangalore India
    3. 3rd SWBES, in conjunction with IEEE e-Science 2008, Indianapolis, USA

Summer school

1.    International summer school on data management in environmental and earth sciences (2018 | 2019 | 2020)

Workshop/Panel chair

  1. Panel Co-Chair, International conference on High Performance Computing and Simulation (HPCS 2015, Amsterdam)
  2. Workshop Chair, International conference on Network and Distributed Computing (ICNDC 2010-2013, 2015, 2016)

Program committee

  1. IEEE Int’l conference on CloudNet (2021 | 2022)
  2. Int’l workshop on Science Gateway (IWSG 2018 | 2019 | 2020 | 2021)
  3. IEEE International Conference on Advanced Information Networking and Applications (AINA 2017)
  4. IEEE International Conference on e-Science (2014, 2015, 2016, 2017, 2019)
  5. IEEE International Conference on Ubiquitous Computing and Communication (IUCC, 2011, 2013, 2015, 2016, 2017)
  6. IEEE International Conference on Internet of Things and Cloud (FiCloud, 2016)
  7. International Conference on Smart Data (2016, 2017)
  8. Workshop on Management of resources and services in Cloud and Sky computing (MICAS, 2016)
  9. W3C – VRE4EIC Workshop on Smart Descriptions & Smarter Vocabularies (SDSVoc, 2016)
  10. IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom, 2014)
  11. International Workshop on Workflow Models, Systems, Services and Applications in the Cloud (2012-2014)
  12. The 11th IEEE International Conference on Computer and Information Technology (2011)
  13. The IET International Conference on Frontier Computing– Theory, Technologies and Applications (2010)
  14. International Workshop on Scientific Workflows (2009-2011)

 

 

 

 

 

 

 

Home

 

 

 

Publications

 

 

 

Journals

1.     Zeshun Shi, Veno Ivankovic, Siamak Farshidi, Jayachander Surbiryala, Huan Zhou and Zhiming Zhao: AWESOME: an auction and witness enhanced SLA model for decentralized cloud marketplaces, Journal of Cloud Computing: Advances, Systems (2022), https://doi.org/10.1186/s13677-022-00292-8

2.     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

3.     Peng Chen, Hongyun Liu, Ruyue Xin, Thierry Carval, Jiale Zhao, Yunni Xia  and Zhiming Zhao: Effectively Detecting Operational Anomalies in Large-scale IoT Data Infrastructures by using a GAN-based Predictive Model, The Computer Journal, to appear.

4.     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.

5.     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.

6.     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.

7.     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

8.     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.

9.     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.

10.  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 

11.  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.

12.  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]

13.  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.

14.  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].

15.  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].

16.  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.

17.  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.

18.  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.

19.  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].

20.  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.

21.  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.

22.  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]

23.  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 Computat Pract Exper. (2019). https://doi.org/10.1002/cpe.5511.

24.  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.

25.  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.

26.  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.

27.  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.

28.  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.

29.  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.

30.  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.

31.  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.

32.  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.

33.  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.

34.  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].

35.  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.

36.  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.

37.  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.

38.  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.

39.  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.

40.  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.

41.  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)

42.  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.

43.  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.

44.  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]

45.  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]

46.  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]

47. 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.

48.  Belloum, A., Deelman, E. & Zhao, Z.: Scientific workflows. Scientific Programming, 14(3-4), 171-171 (2006). [Full text]

 

Book (Eds)

49.  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] 

https://media.springernature.com/w306/springer-static/cover-hires/book/978-3-030-52829-4

Book chapters

50.  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.

51.  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.

52.  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.

53.   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.

54.  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.

55.  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.

56.  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.

57.  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.

58.  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.

59.  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.

60.  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.

61.  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].

62.  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

1.

63.  Veno Ivankovic, Zeshun Shi, and Zhiming Zhao, A Customizable dApp Framework for User Interactions in Decentralized Service Marketplaces, IEEE SmartIoT 2022, to appear.

64.  Liu H., Xin R., Chen P., Zhao, Z.,: Multi-Objective Robust Workflow Offloading in Edge-to-Cloud Continuum, IEEE Cloud (2022), Barcelona, Spain. [10.1109/CLOUD55607.2022.00070][OA]

65.  Boyko, Alex, Farshidi, Siamak, & Zhao, Zhiming. (2022, June 17). An Adaptable Framework for Entity Matching Model Selection in Business Enterprises. 2022 IEEE 24th Conference on Business Informatics (CBI), Amsterdam. https://doi.org/10.1109/CBI54897.2022.00017 [OA]

66.  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].

67.  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]

68.  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]

69.  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]

70.  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)

71.  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]

72.  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]

73.  Spiros Koulouzis, Riccardo Bianchi, Robin van der Linde, Yuandou Wang and Zhiming Zhao, SPIRIT: A microservice-based framework for interactive Cloud infrastructure planning, Euro-Par 2021 Workshops (2021), Online [10.1007/978-3-031-06156-1_32][OA].

74.  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]

75.  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].

76.  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].

77.  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].

78.  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].

79.  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].

80.  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].

81.  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].

82.  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].

83.  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].

84.  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].

85.  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].

86.  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).

87.  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].

88.  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].

89.  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]

90.  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.

91.  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].

92.  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.

93.  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].

94.  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].

95.  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].

96.  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.

97.  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.

98.  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.

99.  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]

100.                 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.

101.                 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.

102.                 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.

103.                 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.

104.                 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.

105.                 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.

106.                 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.

107.                 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.

108.                 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.

109.                 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.

110.                 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.

111.                 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.

112.                 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.

113.                 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.

114.                 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.

115.                 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.

116.                 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.

117.                 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.

118.                 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.

119.                 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.

120.                 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.

121.                 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.

122.                 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.

123.                 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.

124.                 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.

125.                 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.

126.                 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.

127.                 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.

128.                 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]

129.                 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]

130.                 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.

131.                 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].

132.                 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.

133.                 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.

134.                 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.

135.                 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.

136.                 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.

137.                 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.

138.                 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.

139.                 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. 

140.                 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.

141.                 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.

142.                 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.

143.                 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.

144.                 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.

145.                 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.

146.                 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.

147.                 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.

148.                 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.

149.                 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.

150.                 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.

151.                 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.

152.                 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.

153.                 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.

154.                 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.

155.                 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.

156.                 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]

157.                 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.

158.                 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.

159.                 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.

160.                 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.

161.                 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.

162.                 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]

163.                 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]

164.                 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]

165.                 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

166.                 Andreas Petzold, Ari Asmi, Rita Gomes, Katrin Seemeyer, Angeliki Adamaki, Alexander T Vermeulen, Daniele Bailo, Keith G Jeffery, Helen Glaves, Zhiming Zhao, Markus Stocker, Margareta Hellström (2021) Creating ENVRI-hub, the Open-Access Platform of the Environmental Sciences Community in Europe, AGU [2021AGUFMIN53A..04P]

167.                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]

168.                 Spiros Koulouzis, Yifang Shi, Yuandou Wan, Riccardo Bianchi, Daniel Kissling, Zhiming Zhao (2021) Enabling LiDAR data processing as a service in a Jupyter environment, EGU 2021 [10.5194/egusphere-egu21-8294]

169.                 Andreas Petzold, Ari Asmi, Katrin Seemeyer, Angeliki Adamaki, Alex Vermeulen, Daniele Bailo, Keith Jeffery, Helen Glaves, Zhiming Zhao, Markus Stocker, Margareta Hellström (2021) Advancing the FAIRness and Openness of Earth system science in Europe, EGU [10.5194/egusphere-egu21-8052]

170.                 Xiaofeng Liao, Doron Goldfarb, Barbara Magagna, Markus Stocker, Peter Thijsse, Dick Schaap, Zhiming Zhao (2020) ENVRI knowledge base: A community knowledge base for research, innovation and society,  EGU, April 2020 [10.5194/egusphere-egu2020-20708]

171.                 Zhao, Z., Martin, P., Koulouzis, S.: (2019) Optimizing environmental data services on federated Cloud and e-Infrastructures, EGU, April 2019 [2019EGUGA..21.3683Z]

172.                 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]

173.                 Abraham Nieva de la Hidalga, Alex R Hardisty, Barbara Magagna, Paul W Martin, Zhiming Zhao (2018) Use of the ENVRI Reference Model to Support the Design of Environmental Research Infrastructures, 20th EGU General Assembly, EGU2018, Proceedings from the conference held 4-13 April, 2018 in Vienna, Austria, p.18552 [2018EGUGA..2018552N]

174.                 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

175.                Zhao, Z., Martin, P. Jeffery K., (2017) VRE in the Data for Science Approach to Common Challenges in ENVRIPLUS, ERCIM, Newsletter, April 2017

176.                Zhao, Z. (2016) Data for science: software and solutions for the environmental sciences, EGI Newsletter, January 2016.

177.                Zhao, Z., (2015) Data for Science theme: software and solutions to address common challenges facing environmental research infrastructures, 1st ENVRIPLUS Newsletter, November 2015.

178.                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]

179.                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

180.                 Yang Hu (2019) Resource Scheduling for Quality-Critical Applications on Cloud Infrastructure, University of Amsterdam, [ISBN: 978-94-028-1713-3].

181.                 Huan Zhou (2019) Seamless Infrastructure Programming and Control for Quality-critical Cloud Applications, [ISBN: 978-94-028-1727-0]

 

In Chinese

182.                 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]

183.                 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]

 

 

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Update date: July 12, 2022