Team Placement in Crowd-Resourcing Virtual Laboratory for IT Security e-Learning

ABSTRACT

A crowd-resourcing virtual laboratory is a virtual laboratory in which some of the resources are obtained from the crowd. The virtual laboratory is for IT Security e-Learning, where a trainee needs an isolated laboratory environment to do the practical exercises. The isolated laboratory environment, which is called as a Team, consists of virtual machines (VMs) or containers and virtual network devices. The crowd contributes their resources such as virtual machines or physical machines, to the virtual laboratory. The virtual laboratory automatically occupies the contributed resources and uses them to create a Team. The team that consists of containers, will be run in a VM. Since there could be a lot of VMs available, the system needs to select the best VM to run a Team. We present CTPlace, an approach for Team Placement in crowd-resourcing virtual laboratory.

CTPlace groups the VMs into tree hierarchical clusters based on the Geo-location of the VMs. CTPlace has two steps in the Team placement. First, it selects a nearest cluster to the trainee location to get the highest throughput. Second, it selects a VM inside the selected cluster. To select a VM inside a public cloud cluster, it uses Most-Full-First algorithm to reduce service cost by reducing the number of running VMs. To select a VM inside a private cloud or within contributed resources, it uses Least-Full-First and Tag-Pack to balance the load and try to place the same type of Teams on the same VM. We compare the CTPlace with three other placement algorithms in a simulated environment, to evaluate the performance of the CTPlace.

References

1. Willems, C. and Meinel, C.Practical Network Security Teaching in an Online Virtual Laboratory. In Proceedings of the 2011 International Conference on Security and Management (SAM 2011), p. 65--71, Las Vegas, USA, 2011..
2. Moritz, D., Willems, C., Goderbauer, M., Moeller, P. & Meinel, C. (2013, August). Enhancing a virtual security lab with a private cloud framework. In Teaching, Assessment and Learning for Engineering (TALE), 2013 IEEE International Conference on (pp. 314--320). IEEE..
3. Amorin, K., AlAufi, L.CloudWhip: A Tool for Provisioning Cyber Security Labs in the Amazon Cloud. Security and Management (SAM), 2014. - world-comp.org.
4. Willems, C., Klingbeil, T., Radvilavicius, L., Cenys, A.and Meinel, C.2011. A distributed virtual laboratory architecture for cybersecurity training, In Proc. of the 6th International Conference on Internet Technology and Secured Transactions, 2011, pp. 408--415.
5. Sianipar, J., Willems, C., Meinel, C. (2016). "A Container-based Virtual Laboratory for Internet Security e-Learning". International Journal of Learning and Teaching. IJLT. Vol. 2, No. 2, December 2016.
6. Sianipar, J., Willems, C., Meinel, C. (2016). "Crowdsourcing Virtual Laboratory Architecture on Hybrid Cloud". INTED2016 Proceedings, pp. 2940--2949.
7. Hu, J., Schmitt, Willems, C. and Meinel, C. "A tutoring system for IT-Security", in Proceedings of the 3rd World Conference in Information Security Education, p. 51--60, Monterey, USA, 2003.
8. Hu, J. and Meinel, C. "Tele-Lab IT-Security on CD: Portable, reliable and safe IT security training", Computers & Security, 23:282--289,2004.
9. Hu, J., Cordel, D. and Meinel, C. "A Virtual Machine Architecture for Creating IT-Security Laboratories", Technical report, Hasso-Plattner-Insitut, 2006.
10. Willems, C. and Meinel, C. "Tele-Lab IT-Security: an Architecture for an online virtual IT Security Lab", International Journal of Online Engineering (iJOE), X, 2008.
11. Usmani, Z. & Singh, S. (2016). A survey of virtual machine placement techniques in a cloud data center. Procedia Computer Science, 78, 491--498.
12. Mann, Z. A. (2015). Allocation of virtual machines in cloud data centers---a survey of problem models and optimization algorithms. ACM Computing Surveys (CSUR), 48(1), 11.
13. Challita, S., Paraiso, F. & Merle, P. (2017, April). A Study of Virtual Machine Placement Optimization in Data Centers. In 7th International Conference on Cloud Computing and Services Science, CLOSER 2017.
14. Mills, K., Filliben, J. and Dabrowski, C.Comparing VMPlacement Algorithms for On-Demand Clouds, Proc. IEEE Third Int'l Conf. Cloud Computing Technology and Science (CloudCom), pp. 91--98, 2011
15. Tordsson, J., Montero, R.S., Moreno-Vozmediano, R., Llorente, I. M. Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers, Future Generation Computer Systems 28 (2012), 358--367.
16. Peinl, R., Holzschuher, F., & Pfitzer, F. (2016). Docker cluster management for the cloud-survey results and own solution. Journal of Grid Computing, 14(2), 265--282.
17. Saleh, E., Sianipar, J., Takouna, I., Meinel. C. (2014c, December). SecPlace: A Security-Aware Placement Model for Multi-tenant SaaS Environments. In Proceedings of the 11th IEEE International Conference on Autonomic and Trusted Computing. Bali, Indonesia.
18. Selimi, M., Cerdà-Alabern, L., Wang, L., Sathiaseelan, A., Veiga, L. and Freitag. F. "Bandwidth-aware Service Placement in Community Network Micro-Clouds". In: 41st IEEE Conference on Local Computer Networks (LCN 2016). (CORE Rank A) (Short paper). Dubai, UAE, Nov. 2016, pp. 220--223.
19. Unuvar, M., Steinder, M., Tantawi. A.N. Hybrid cloud placement algorithm. In: Proceedings of the IEEE International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems (2014)

Index Terms

• Applied computing

  • Education

    • E-learning

• Software and its engineering

  • Software organization and properties

    • Software system structures

      • Distributed systems organizing principles

        • Cloud computing

• Theory of computation

  • Design and analysis of algorithms

    • Approximation algorithms analysis

      • Scheduling algorithms