Flexible allocation of resources is one of the main benefits of cloud computing. Virtualization is used to achieve this flexibility: one or more virtual machines run on a single physical machine. These virtual machines can be deployed and destroyed as needed. One obstacle to flexibility in current cloud systems is that deploying multiple virtual machines simultaneously on multiple physical machines is slow due to the inefficient usage of available resources.We implemented and evaluated three methods of transferring virtual machine images for the OpenNebula cloud middleware. One of the implementations was based on BitTorrent and the other two were based on multicast. Our evaluation results showed that the implemented methods were significantly more scalable than the default methods available in OpenNebula when tens of virtual machines were deployed simultaneously. However, the implemented methods were slightly slower than the old ones for deploying only one or a few virtual machines at a time due to overhead related to managing the transfer process.We also evaluated the performance of different virtual machine disk formats, as this choice also affects the deployment time of the machine. Raw images, QCOW2 images and logical volumes were evaluated. Logical volumes were fastest overall in sequential disk I/O performance. With sequential reads and writes, raw images could provide at best approximately 88% of the write performance and 95% of the read performance of logical volumes. The corresponding numbers for QCOW2 were 86% write and 74% read performance. Random access performance between QCOW2 and raw images was nearly identical, but LVM random access performance in our specific benchmark was significantly worse.If the usage pattern of the cloud is such that deploying large batches of virtual machines at once is common, using the new transfer methods will significantly speed up the deployment process and reduce its resource usage. The disk access method should be chosen based on what provides acceptable performance for the task being executed and provides the fastest deployment times.
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