在雲端運算時代，虛擬化的容錯利用持續的虛擬機遷移來同步虛擬機及遠端的備份虛擬機是實現高可用性的常用技術。然而傳統熱遷移的目標是最大化的降低系統暫停時間，由於需要預先複製虛擬機的狀態以及記憶體，所以傳統熱遷移的執行時間較長，這增加了故障發生時故障轉移的時間。在本論文中，我們提出一種新的虛擬機遷移方法，稱為快速虛擬機遷移(FVMM)，它利用虛擬機模板(VM templating)技術降低預先複製的成本來加速虛擬機遷移。虛擬機模板(VM templating)能夠利用模板(template)建立多的虛擬機，他通常用於在大型虛擬化環境中部屬許多相似的虛擬機。FVMM使用模板降低預先複製的成本。它使用六種新的技術來優化實做：暫停快速虛擬機遷移(SFVMM)和存活快速虛擬機遷移(LFVMM)、寫入時複製模板(COW templating)、連續模板(continuous templating)、異步傳輸緩衝(asynchronous transmission buffering)、從連續模板中恢復虛擬機、定期製做模板。我們還將FVMM應用在虛擬化容錯上加速故障轉移的過程。實驗結果表明，對16GB的虛擬機，FVMM比傳統的虛擬機遷移速度快56倍以上，與使用一般熱遷移的方法相比可以減少高達32\%的系統暫停時間和容錯重新同步時間。

In the era of cloud computing, virtualization based fault tolerance that utilizes the continuous virtual machine (VM) migration to synchronize a VM and its remote replica is a common technique to achieve high availability. However, traditional live VM migration, whose goal is to minimize the system downtime, has a long duration owing to the expense of the pre-copy for machine status and memory content, which increases the period of failover when failures occur. In this thesis, we proposed a new VM migration method, called Fast VM Migration (FVMM), which utilizes the templating technique to accelerate the VM migration by reducing the cost of pre-copy. The templating technique that creates VMs from a master copy, called a template, is a usually used to deploy many similar VMs in a large virtual environment. FVMM employs VM templating to mitigate the cost of pre-copy. Its implementation is optimized with six new techniques: SFVMM and LFVMM, COW templating, continuous templating, asynchronous transmission buffering, VM recovery from continuous templating, and templating regularly. We also applied FVMM for virtualization based fault tolerance to accelerate the failover process. Experimental results show that FVMM is more than 56 times faster than pre-copy live migration for a VM with 16GB, and can reduce upto 32\% of the system downtime and the fault tolerance resynchronization time comparing to the methods using pre-copy live migration.

[1] Peter Mell and Timothy Grance. The NIST Definition of Cloud Computing. en. 2011. doi: https://doi.org/10.6028/NIST.SP.800-145.
[2] Brendan Cully et al. “Remus: High Availability via Asynchronous Virtual Ma- chine Replication”. Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation. NSDI’08. San Francisco, California: USENIX Association, 2008, 161–174. isbn: 1119995555221.
[3] Chieh-Yu Yu et al. “Efficient Group Fault Tolerance for Multi-tier Services in Cloud Environments”. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). 2020, pp. 1–7. doi: 10.1109/ICC40277.2020.9149253.
[4] VMWare VM Templates. http://www.vmware.com/pdf/vc_2\_templates_
usage_best_practices_wp.pdf.
[5] Oracle VM Templates. http://www.oracle.com/technology/products/vm/
templates/index.html.
[6] Alessandro Randazzo and Ilenia Tinnirello. “Kata Containers: An Emerging Architecture for Enabling MEC Services in Fast and Secure Way”. 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). 2019, pp. 209–214. doi: 10.1109/IOTSMS48152.2019.8939164.
[7] Cuju: An Open Source Project for Virtualization-Based Fault Tolerance. 2019. url: https://cuju-ft.github.io/cuju-web/home.html.
[8] Yifeng Sun. “Protection Mechanisms for Virtual Machines on Virtualized Servers”. PhD thesis. Stony Brook University, 2017.
[9] Mihai Caraman et al. “Romulus: Disaster tolerant system based on kernel virtual machines”. 20th International DAAAM Symposium : Intelligent Manufacturing and Automation: Theory, Practice and Education 1 (2009).
[10] Yoshiaki Tamura et al. “Kemari: Virtual Machine Synchronization for Fault Tolerance” (2008).
[11] Timothy Wood et al. “PipeCloud: Using Causality to Overcome Speed-of-light Delays in Cloud-based Disaster Recovery”. Proceedings of the 2Nd ACM Symposium on Cloud Computing. SOCC ’11. Cascais, Portugal: ACM, 2011, 17:1–17:13. isbn: 978-1-4503-0976-9. doi: 10.1145/2038916.2038933. url: http: //doi.acm.org/10.1145/2038916.2038933.
[12] YaoZu Dong et al. “COLO: COarse-grained LOck-stepping Virtual Machines for Non-stop Service”. Proceedings of the 4th Annual Symposium on Cloud Computing. SOCC ’13. Santa Clara, California: ACM, 2013, 3:1–3:16. isbn: 978-1-4503-2428-1. doi: 10.1145/2523616.2523630. url: http://doi.acm.org/10.1145/2523616.2523630.36
[13] P. Tsao et al. “Efficient Virtualization-Based Fault Tolerance”. 2016 International Computer Symposium (ICS). 2016, pp. 114–119. doi: 10 . 1109 / ICS .2016.0031.
[14] Marvin M. Theimer, Keith A. Lantz, and David R. Cheriton. “Preemptable Remote Execution Facilities for the V-System”. Proceedings of the Tenth ACM Symposium on Operating Systems Principles. SOSP ’85. Orcas Island, Washington, USA: Association for Computing Machinery, 1985, 2–12. isbn: 0897911741. doi: 10.1145/323647.323629. url: https://doi.org/10.1145/323647.
323629.
[15] Michael R. Hines, Umesh Deshpande, and Kartik Gopalan. “Post-Copy Live Migration of Virtual Machines”. SIGOPS Oper. Syst. Rev. 43.3 (2009), 14–26. issn: 0163-5980. doi: 10.1145/1618525.1618528. url: https://doi.org/10.1145/1618525.1618528.
[16] osnetsvn. qemu-4.1.0. https://github.com/osnetsvn/qemu-4.1.0. 2021.
[17] Po-Jen Chuang and Yen-Chia Huang. “Efficient Snapshot Mechanisms for Xen Virtual Machines”. 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA). 2017, pp. 112–115. doi: 10.1109/SOCA.2017.23.
[18] Zhiyu Hao et al. “iConSnap: An Incremental Continuous Snapshots System for Virtual Machines”. IEEE Transactions on Services Computing 15.1 (2022), pp. 539–550. doi: 10.1109/TSC.2019.2955700.
[19] Denis Plotnikov. qemu. https://github.com/qemu/qemu/tree/stable-6.0.2021.
[20] R. Gioiosa et al. “Transparent, Incremental Checkpointing at Kernel Level: a Foundation for Fault Tolerance for Parallel Computers”. SC ’05: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing. 2005, pp. 9–9. doi: 10.1109/SC.2005.76.
[21] Long Wang et al. “VM-Checkpoint: Design, Modeling, and Assessment of Lightweight In-Memory VM Checkpointing”. IEEE Transactions on Dependable and Secure Computing 12.2 (2015), pp. 243–255. doi: 10.1109/TDSC.2014.2327967.
[22] Bernhard Egger et al. “Efficient Checkpointing of Live Virtual Machines”. IEEE Transactions on Computers 65.10 (2016), pp. 3041–3054. doi: 10.1109/TC.2016.2519890.
[23] Colin Ian King. stress-ng. https://github.com/ColinIanKing/stress- ng. 2022.