Troy: Efficient Service Deployment for Windows Systems

ZHANG Deyu; XIE Yu; XU Mucong; CHENG En; KUI Xiaoyan; HE Bangwen; LI Yunhao

doi:10.23919/cje.2022.00.405

Volume 33 Issue 1

Jan. 2024

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2024 > 33(1): 313-322

Deyu ZHANG, Yu XIE, Mucong XU, et al., “Troy: Efficient Service Deployment for Windows Systems,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 313–322, 2024 doi: 10.23919/cje.2022.00.405

Citation:

Deyu ZHANG, Yu XIE, Mucong XU, et al., “Troy: Efficient Service Deployment for Windows Systems,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 313–322, 2024 doi: 10.23919/cje.2022.00.405

Deyu ZHANG, Yu XIE, Mucong XU, et al., “Troy: Efficient Service Deployment for Windows Systems,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 313–322, 2024 doi: 10.23919/cje.2022.00.405

Citation:

PDF( 3851 KB)

Troy: Efficient Service Deployment for Windows Systems

doi: 10.23919/cje.2022.00.405

1.
School of Computer Science and Engineering, Central South University, Changsha, 410083, China

More Information

Author Bio:
Deyu ZHANG received the B.S. degree in communication engineering from PLA Information Engineering University, Zhengzhou, China, in 2005, and the M.S. degree in communication engineering and Ph.D. degree in computer science from Central South University, Changsha, China, in 2012 and 2016, respectively. He is currently an Associate Professor with the School of Computer Science and Technology. His research interests include mobile system optimization, edge computing, and stochastic optimization. He has published more than 50 papers in prestigious conferences and journals, such as ACM MobiCom, MobiSys, SenSys, CSUR and IEEE TMC, JSAC. He was a Visiting Scholar with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada, from 2014 to 2016. He visited Microsoft Research Asia between 2019 to 2020. Prof. Zhang has served as the Co-Chair of the SECIoT1 workshop and the Guest Editor for a special issue of the IEEE Internet of Things Journal and PPNA. He is an Associate Editor for KSII Transactions on Internet and Information. He is a Member of the ACM, IEEE, and CCF. (Email: zdy876@csu.edu.cn)

Yu XIE received the B.E. degree from University of Electronic Science and Technology of China, in 2017. He is currently pursuing the M.A. degree at Computer Department, Central South University. His research interests include storage system and virtualization. (Email: xie_yu@csu.edu.cn)

Mucong XU received the B.E. degree from Hunan Institute of Technology, in 2016. He is currently pursuing the M.A. degree at Computer Department, Central South University. His current research interests include stream computing and machine learning. (Email: mucongxu@126.com)

En CHENG received the B.E. degree from Hainan University, in 2020. He is currently pursuing the M.A. degree at Computer Department, Central South University. His current research interests include computer vision and stream loading. (Email: chengen@csu.edu.cn)

Xiaoyan KUI is a Professor in the School of Computer Science and Engineering at Central South University, China. She received the B.S., M.S., and Ph.D. degrees, all in computer science, from Central South University, China, in 2003, 2008, and 2012, respectively. Her research interests include information visualization, data visualization, visual analytics, mobile computing and vehicular network. She has published more than 40 technique papers in international journals and conferences. Prof. Kui has served as the Guest Editor for a special issue of the Electronics. She is a Member of the CCF. (Email: xykui@csu.edu.cn)

Bangwen HE received B.E. degree from School of Computer Science and Engineering, Central South University, China, in 2021. He is currently pursuing the M.A. degree at Central South University, Changsha, China. His current research interests include mobile neural network acceleration and edge computing. (Email: hebangwen@csu.edu.cn)

Yunhao LI received the B.E. degree from Central South University of China in 2022. He is currently pursuing the M.E. degree at Computer Department, Central South University. His current research interest is bootstrap procedure of operating systems. (Email: liyunhaocsu@csu.edu.cn)
Corresponding author: Email: xykui@csu.edu.cn
Received Date: 2022-11-25
Accepted Date: 2023-03-20

Available Online: 2023-07-28

Publish Date: 2024-01-05

Abstract

Abstract

The modern university computer lab and kindergarden through 12th grade classrooms require a centralized solution to efficiently manage a large number of desktops. The existing solutions either bring virtualization overhead in runtime or requires loading a large image over 30 GB leading to an unacceptable network latency. In this work, we propose Troy which takes advantage of the differencing virtual hard disk techniques in Windows systems. As such, Troy only loads the modifications made on one machine to all other machines. Troy consists of two modules that are responsible to generate an initial image and merge a differencing image with its parent image, respectively. Specifically, we identify the key fields in the virtual hard disk image that links the differencing image and the parent image and find the modified blocks in the differencing images that should be used to replace the blocks in the parent image. We further design a lazy copy solution to reduce the I/O burden in image merging. We have implemented Troy on bare metal machines. The evaluation results show that the performance of Troy is comparable to the native implementation in Windows, without requiring the Windows environment.
- Service deployment,
- Virtual hard disk,
- File system merging,
- Windows system

FullText(HTML)

References(22)

References

[1]	Y. J. Tang and X. X. Ding, “Application research of desktop virtualization technology based on VOI in computer room management of colleges and universities,” Journal of Physics: Conference Series, vol. 1345, article no. 062055, 2019. doi: 10.1088/1742-6596/1345/6/062055
[2]	X. Q. Shi, “The construction of language laboratory based on VOI technology,” Journal of Physics: Conference Series, vol. 1952, article no. 042030, 2021. doi: 10.1088/1742-6596/1952/4/042030
[3]	D. Y. Zhang, L. Tan, J. Ren, et al., “Near-optimal and truthful online auction for computation offloading in green edge-computing systems,” IEEE Transactions on Mobile Computing, vol. 19, no. 4, pp. 880–893, 2020. doi: 10.1109/TMC.2019.2901474
[4]	J. R. Zhang, H. Yang, J. Ren, et al., “MobiDepth: Real-time depth estimation using on-device dual cameras,” in Proceedings of the 28th Annual International Conference on Mobile Computing And Networking, Sydney, Australia, pp. 528–541, 2022.
[5]	C. T. Yang, J. C. Liu, J. Y. Lee, et al., “The implementation of a virtual desktop infrastructure with GPU accelerated on OpenStack,” in 2018 15th International Symposium on Pervasive Systems, Algorithms and Networks, Yichang, China, pp. 366–370, 2018.
[6]	H. Xia, “Research and Application of Cloud Computing and Big Data Technology in Intelligent Desktop Virtualization System,” 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA), Dalian, China, pp. 517-521, 2022
[7]	J. T. Lim and J. Nieh, “Optimizing nested virtualization performance using direct virtual hardware,” in Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, Lausanne, Switzerland, pp. 557–574, 2020.
[8]	Y. Sun, J. X. Lei, S. Shin, et al., “Baoverlay: A block-accessible overlay file system for fast and efficient container storage,” in Proceedings of the 11th ACM Symposium on Cloud Computing, Virtual Event, New York, USA, pp. 90–104, 2020.
[9]	H. B. Li, Y. F. Yuan, R. Du, et al., “DADI: Block-Level image service for agile and elastic application deployment,” in Proceedings of 2020 USENIX Annual Technical Conference, pp. 727–740, 2020.
[10]	P. Olivier, A. K. M. F. Mehrab, S. Lankes, et al., “HEXO: Offloading HPC compute-intensive workloads on low-cost, low-power embedded systems,” in Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing, Phoenix, AZ, USA, pp. 85–96, 2019.
[11]	L. L. Ma, S. H. Yi, N. Carter, et al., “Efficient live migration of edge services leveraging container layered storage,” IEEE Transactions on Mobile Computing, vol. 18, no. 9, pp. 2020–2033, 2019. doi: 10.1109/TMC.2018.2871842
[12]	S. Gotanda and T. Shinagawa, “Short paper: Highly compatible fast container startup with lazy layer pull,” in Proceedings of 2021 IEEE International Conference on Cloud Engineering (IC2E), San Francisco, CA, USA, pp. 53–59, 2021.
[13]	X. B. Wu, Z. L. Shao, and S. Jiang, “Selfie: Co-locating metadata and data to enable fast virtual block devices,” in Proceedings of the 8th ACM International Systems and Storage Conference, Haifa, Israel, article no.2, 2015.
[14]	M. Wu, L. Zhou, and F. J. Huang, “EVCS: An edge-assisted virtual computing and storage approach for heterogeneous desktop deployment,” in Proceedings of the 2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), Jinan, China, pp. 107–112, 2022.
[15]	S. G. Wang, Y. Guo, N. Zhang, et al., “Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach,” IEEE Transactions on Mobile Computing, vol. 20, no. 3, pp. 939–951, 2021. doi: 10.1109/TMC.2019.2957804
[16]	K. Akahoshi, F. J. He, and E. Oki, “Service deployment model based on virtual network function resizing,” IEEE Transactions on Network and Service Management, vol. 20, no. 1, pp. 547–562, 2023. doi: 10.1109/TNSM.2022.3198664
[17]	A. Hazra, M. Adhikari, T. Amgoth, et al., “Stackelberg game for service deployment of IoT-enabled applications in 6g-aware fog networks,” IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5185–5193, 2021. doi: 10.1109/JIOT.2020.3041102
[18]	Z. Z. Xiang, Y. H. Zheng, M. Z. He, et al., “Energy-effective artificial internet-of-things application deployment in edge-cloud systems,” Peer-to-Peer Networking and Applications, vol. 15, no. 2, pp. 1029–1044, 2022. doi: 10.1007/s12083-021-01273-5
[19]	A. Bozorgchenani, D. Tarchi, and W. Cerroni, “On-demand service deployment strategies for fog-as-a-service scenarios,” IEEE Communications Letters, vol. 25, no. 5, pp. 1500–1504, 2021. doi: 10.1109/LCOMM.2021.3055535
[20]	Y. M. Zhang, X. L. Lan, J. Ren, et al., “Efficient computing resource sharing for mobile edge-cloud computing networks,” IEEE/ACM Transactions on Networking, vol. 28, no. 3, pp. 1227–1240, 2020. doi: 10.1109/TNET.2020.2979807
[21]	F. C. Jia, D. Y. Zhang, T. Cao, et al., “CODL: Efficient CPU-GPU co-execution for deep learning inference on mobile devices,” in Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services, Portland, Oregon, pp. 209–221, 2022.
[22]	S. Yue, J. Ren, N. Qiao, et al., “TODG: Distributed task offloading with delay guarantees for edge computing,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 7, pp. 1650–1665, 2022. doi: 10.1109/TPDS.2021.3123535