FITDOC: Fast Virtual Machines Checkpointing with Delta Memory Compression

Abstract

Virtualization provides the function of saving the whole execution environment status of the running virtual machine (VM), which makes check pointing flexible and practical for HPC servers or data center servers. However, the system-level check pointing 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 VMs check pointing approach, named Fast Incremental Check pointing 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 (the number of 8 bytes), instead of the conventional method (the number of pages). FITDOC utilizes dirty page logging mechanism to record the dirty pages, accordingly, a delta memory compression mechanism is implemented to eliminate redundant memory data in check pointing files. To locate the dirty data in dirty pages, FITDOC utilize two mechanisms: by analyzing the distribution characteristics of dirty pages in 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 check pointing algorithm, FITDOC can reduce 70.54% of check pointing time on average with 1GB memory size and achieve better improvement for VMs with larger memory configurations. FITDOC can reduce 52.88% of the check pointing data size on average compared with Remus's incremental solution which is in page granularity. Compared with default dirty bitmap scanning method in Xen, the scanning time of FITDOC is decreased by 91.13% on average.