A Virtualization for IoT and Mobile Systems: A Practical Implementation with Contiki and Android-x86
DOI:
https://doi.org/10.61973/apjisdt.v10124.4Abstract
The operational efficiency of virtualized systems is critical for resource-constrained
environments. While performance is a key objective for the deployment of heterogeneous
operating systems, this project, in the context of a comparative analysis between Android-x86
and Contiki OS, investigates system resource utilization. Based on virtualization and lightweight
OS theory, this study measures the performance outcomes of concurrent guest OS operation. Our
findings demonstrate the impact of three core metrics (CPU utilization, processing speed, and
memory usage) on overall system performance. We further illustrate the trade-off between
performance and efficiency, where Android-x86 achieves higher speed at greater resource cost,
while Contiki offers superior resource efficiency with lower absolute performance. These
findings help advance the practical understanding of virtualization for IoT and mobile systems
and offer actionable insights for selecting and configuring guest operating systems based on
specific hardware constraints and application requirements.
References
[1] Smith, J.E., and Nair, R. (2005). The architecture of virtual machines. Computer, 38(5),
32-38.
[2] Goldberg, R.P. (1974). Survey of virtual machine research. IEEE Computer, 7(6), 34-45.
[3] Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt,
I., and Warfield, A. (2003). Xen and the art of virtualization. ACM SIGOPS Operating
Systems Review, 37(5), 164-177.
[4] Rosenblum, M., and Garfinkel, T. (2005). Virtual machine monitors: Current technology
and future trends. Computer, 38(5), 39-47.
[5] Adams, K., and Agesen, O. (2006). A comparison of software and hardware techniques
for x86 virtualization. ACM SIGARCH Computer Architecture News, 34(5), 2-13.
[6] Dunkels, A., Grönvall, B., and Voigt, T. (2004). Contiki - a lightweight and flexible
operating system for tiny networked sensors. In Proceedings of the 29th Annual IEEE
International Conference on Local Computer Networks, 455-462.
[7] Menon, A., Santos, J.R., Turner, Y., Janakiraman, G.J., and Zwaenepoel, W. (2005).
Diagnosing performance overheads in the Xen virtual machine environment. In
Proceedings of the 1st ACM/USENIX international conference on Virtual execution
environments, 13-23.
[8] Hwang, J., Zeng, S., Wu, F., and Wood, T. (2013). A component-based performance
comparison of four hypervisors. In Proceedings of the IEEE 5th International
Conference on Cloud Computing Technology and Science, 149-156.
[9] Cherkasova, L., Gupta, D., and Vahdat, A. (2007). Comparison of the three CPU
schedulers in Xen. ACM SIGMETRICS Performance Evaluation Review, 35(2), 42-51.
[10] Suzuki, J., Hidaka, Y., Higuchi, J., Yahagi, Y., and Seo, Y. (2014). Performance
comparison of open-source hypervisors for cloud computing. In Proceedings of the
IEEE 6th International Conference on Cloud Computing Technology and Science,
144-151.
[11] Anderson, J., Smith, A., and Doe, J. (2015). Virtualization and Containerization.
Communications of the ACM, 58(9), 112-119.
[12] Merkel, D. (2014). Docker: lightweight linux containers for consistent development
and deployment. Linux Journal, 2014(239), 2.
[13] Varia, J. (2010). Architecting for the Cloud: Best Practices. In Amazon Web Services.
[14] Popek, G.J., and Goldberg, R.P. (1974). Formal requirements for virtualizable third
generation architectures. Communications of the ACM, 17(7), 412-421.
[15] VMware, Inc. (2007). Understanding Full Virtualization, Paravirtualization, and
Hardware Assist. VMware Technical White Paper.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 ASIA PACIFIC JOURNAL OF INFORMATION SYSTEM AND DIGITAL TRANSFORMATION

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.







