Cloud Computing is a paradigm which provides resources to users from its pool based on demand to satisfy their requirements. During this process, many servers are overloaded and underloaded in the cloud environment. Thus, power consumption and load balancing are the major problems and are resolved by live virtual machine (VM) migration. Load balancing is addressed by moving virtual machines from overloaded host to under loaded host and from under loaded host to any other host which is not overloaded called VM migration. If this process is done without power off (Live) the virtual machines then it is called live VM migration. By this process, the issue of power consumption by physical hosts is also resolved. Migrating virtual machines involves virtualized components like storage disks, memory, CPU and networking, the entire state of VM is captured as a collection of data files. These data files are virtual disk files, configuration files, and log files. The virtual disk files take larger memory and take more migration time and hence the downtime. These disk files include many zero pages, similar and redundant pages. Many techniques such as compression, deduplication is used reduce the size of VM disk image file. Compression techniques are not widely used, due to the disadvantage of compression ratio and decompression time. Many researchers hence used deduplication techniques for reducing the VM disk image file in the live migration process. The significance of the research work is to design an adaptive deduplication mechanism for reducing VM disk image file size by performing fixed length and variable length block-level deduplication processes. The Rabin-Karp rolling hash algorithm is used in variable length block-level deduplication. Akka stream is used for streaming the VM disk image files as it is the bulk volume of live data transfer. To reduce the time of the deduplication process, many researchers used multithreading and multi-core technologies. We use multithreading in Akka framework to run the deduplication process concurrently without OutofMemory errors. The experiment results show that we achieved a maximum of 83% overall reduction in image storage space and 89.76% reduction in total migration time are achieved by adaptive deduplication method. 3% improvement in deduplication rate when compared with the existing image management system. The results are significant because when we apply this in the storage of data centres, there are much space savings. The reduction in size is dependent on the dataset was taken and the applications running on the VM.

中文翻译：

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使用AKKA流对虚拟机映像进行自适应重复数据删除，以加快云环境中的实时迁移过程

云计算是一种范式，它可以根据需求从其池中为用户提供资源，以满足他们的需求。在此过程中，许多服务器在云环境中过载和负载不足。因此，功耗和负载平衡是主要问题，可以通过实时虚拟机（VM）迁移解决。通过将虚拟机从过载的主机移动到负载不足的主机，以及从负载不足的主机移动到其他任何未过载的主机（称为VM迁移），可以解决负载平衡问题。如果在不关闭虚拟机电源的情况下完成了此过程，则称为实时VM迁移。通过此过程，还可以解决物理主机的功耗问题。迁移虚拟机涉及虚拟化的组件，例如存储磁盘，内存，CPU和网络，VM的整个状态被捕获为数据文件的集合。这些数据文件是虚拟磁盘文件，配置文件和日志文件。虚拟磁盘文件占用更大的内存，并需要更多的迁移时间，因此需要停机时间。这些磁盘文件包括许多零页，相似和冗余页。使用压缩，重复数据删除等许多技术可以减小VM磁盘映像文件的大小。由于压缩率和解压缩时间的缺点，压缩技术没有被广泛使用。因此，许多研究人员使用重复数据删除技术来减少实时迁移过程中的VM磁盘映像文件。研究工作的意义在于设计一种自适应重复数据删除机制，通过执行固定长度和可变长度的块级重复数据删除过程来减少VM磁盘映像文件的大小。Rabin-Karp滚动哈希算法用于可变长度块级重复数据删除。Akka流用于传输VM磁盘映像文件，因为它是实时数据传输的大容量。为了减少重复数据删除过程的时间，许多研究人员使用了多线程和多核技术。我们在Akka框架中使用多线程来同时运行重复数据删除过程，而不会出现OutofMemory错误。实验结果表明，通过自适应重复数据删除方法，图像存储空间最多可减少83％，总迁移时间最多可减少89.76％。与现有的图像管理系统相比，重复数据删除率提高了3％。结果非常重要，因为当我们将其应用于数据中心存储时，可节省大量空间。