Optimizing Reliability and Efficiency of Live Migration Mechanism for Kernel Virtual Machine

• Main Authors: CHAO WEI, 趙緯
• Other Authors: Wei-Jen Wang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/3qn8j3

碩士 === 國立中央大學 === 資訊工程學系 === 106 === As virtualization technology is widely used by enterprises and the general public, many industry services have been deployed on cloud platforms. However, the hardware crash or maintenance Downtime still lead to virtual machine down. At this time how to improve the reliability of cloud services has become an important issue for cloud platform vendors. Now many of cloud platforms have introduced Live migration. This technology can move the virtual machine into other physical machines without stopping the virtual machine. In recent years, fault-tolerance technology has begun to introduce to many cloud platforms. The premise of providing high-reliability services is that any virtual machines memory states must not be lost. Therefore, this research based on the Kvm Pre-Copy Live migration. We find out the virtual machine execute difference application will lead to Live migration fail. In response to this problem, we propose an algorithm MP (Memory Pattern) to correct the failure of the Kvm in Live migration. The MP algorithm checks the memory status of each iteration to determine whether the virtual machine belongs to memory intensive. If virtual machine belongs to memory intensive then enter the stop copy phase earlier, it pays a little more down time to avoid long total migration time, as well as occupying the network bandwidth. After an experimental test of the MP algorithm and Kvm Pre-copy algorithm, Kvm achieved 0% success rate for Live migration in the memory intensive case. Our proposed MP algorithm success rate achieved 100% in the memory intensive case. We have made the Kvm perform live migration by relaxing the down time condition to compare with our proposed MP algorithm. In the total migration time MP algorithm can be reduced by 22%, and the total amount of data transmitted during the migration process is reduced by about 19%. At the same time, it can ensure the reliability of instant migration and can also reduce the consumption of network bandwidth.