FITDOC: fast virtual machines checkpointing with delta memory compression

Abstract

Virtualization provides the function of saving the entire status of the execution environment of a running virtual machine (VM), which makes checkpointing flexible and practical for HPC servers or data center servers. However, the system-level checkpointing needs to save a large number of data to the disk. Moreover, the overhead grows linearly with the increasing size of virtual machine memory, which leads to disk I/O consumption disaster along with poor system scalability. To target this, we propose a novel fast VM checkpointing approach, named Fast Incremental checkpoinTing with Delta memOry Compression (FITDOC). By studying the run-time memory characteristics of different workloads, FITDOC counts the dirty pages in a fine-granularity manner (i.e., the number of 8 bytes), instead of in the conventional method (i.e., the number of pages). FITDOC utilises a dirty page logging mechanism to record the dirty pages. Accordingly, a delta memory compression mechanism is implemented to eliminate redundant memory data in checkpointing files. To locate the dirty data in dirty pages, FITDOC utilizes two mechanisms: by analyzing the distribution characteristics of dirty pages in the dirty bitmap, we propose a fast dirty bitmap scanning method to locate the dirty pages, and take a multi-threading data comparison mechanism to locate the real dirty data in one page. The experimental results show that compared with Xen's default system-level checkpointing algorithm, FITDOC can on average reduce checkpointing time 70.54 % with a 1 GB memory size and achieve better improvement for VMs with larger memory configurations. FITDOC can reduce the size of checkpointing data 52.88 % on average compared with Remus's incremental solution, which is in page granularity. Compared with the default dirty bitmap scanning method in Xen, the scanning time of FITDOC is decreased by 91.13 % on average.