Contrary to CPU, memory, and network, disk virtualization is peculiar, for which virtualization through direct access is impossible. We study virtual disk utilization in a large-scale public cloud and observe the presence of long snapshot chains, sometimes composed of up to 1,000 files. We then demonstrate, through experimental measurements, that such long chains lead to virtualized storage performance and memory footprint scalability issues. To address these problems, we presentSVD, a new virtual disk format. We implementedSVDby extending Qcow2, a popular format, and its Qemu driver. We evaluated our prototype, demonstrating that it brings significant performance enhancements and memory footprint reduction. For example,SVDimproves the throughput of RocksDB by about 48% on a snapshot chain of length 500.SVDalso reduces the memory footprint by 15×.

Logs record valuable system information at runtime. They are widely used by data-driven approaches for development and monitoring purposes. Parsing log messages to structure their format is a classic preliminary step for log-mining tasks. As they appear upstream, parsing operations can become a processing time bottleneck for downstream applications. The quality of parsing also has a direct influence on their efficiency. Previous approaches toward online log parsing focused on stateful methods. But an increasing number of tasks ask for real time monitoring. Regarding this problem, we propose USTEP, an online log parsing method based on an evolving tree structure. Evaluation results on a panel of 13 datasets coming from different real-world systems demonstrate USTEP superiority in terms of both effectiveness and robustness when compared to other online methods. We also introduce USTEP-UP, a way of running multiple decentralized instances of USTEP in parallel.

The improvement of automated resource management techniques for cloud computing platforms requires a deep understanding of the workload. Previous works focused on virtual machines (VMs), and neglected complementary virtual resources such as images, volumes, snapshots and security groups. Besides, most attention went to public hyperscale platforms with more than ten thousand servers, or small on-premise platforms. To fill the gap, we perform a holistic workload characterization of a non-hyperscale platform. We have collected a three-month-long trace allowing us to characterize the correlated utilization of virtual resources; the consumption of CPU, memory and disk by VMs; and the CPU interferences between VMs.