PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis

Debugging performance anomalies in real-world databases is challenging. Causal inference techniques enable qualitative and quantitative root cause analysis of performance downgrade. Nevertheless, causality analysis is practically challenging, particularly due to limited observability. Recently, chaos engineering has been applied to test complex real-world software systems. Chaos frameworks like Chaos Mesh mutate a set of chaos variables to inject catastrophic events (e.g., network slowdowns) to "stress" software systems. The systems under chaos stress are then tested using methods like differential testing to check if they retain their normal functionality (e.g., SQL query output is always correct under stress). Despite its ubiquity in the industry, chaos engineering is now employed mostly to aid software testing rather for performance debugging. This paper identifies novel usage of chaos engineering on helping developers diagnose performance anomalies in databases. Our presented framework, PERFCE, comprises an offline phase and an online phase. The offline phase learns the statistical models of the target database system, whilst the online phase diagnoses the root cause of monitored performance anomalies on the fly. During the offline phase, PERFCE leverages both passive observations and proactive chaos experiments to constitute accurate causal graphs and structural equation models (SEMs). When observing performance anomalies during the online phase, causal graphs enable qualitative root cause identification (e.g., high CPU usage) and SEMs enable quantitative counterfactual analysis (e.g., determining "when CPU usage is reduced to 45\%, performance returns to normal"). PERFCE notably outperforms prior works on common synthetic datasets, and our evaluation on real-world databases, MySQL and TiDB, shows that PERFCE is highly accurate and moderately expensive

Inverse Moment of the \u3cstrong\u3e\u3cem\u3eB\u3c/em\u3e\u3c/strong\u3e Meson Quasidistribution Amplitude

We perform a study on the structure of the inverse moment (IM) of quasidistributions, by taking B-meson quasidistribution amplitude (quasi-DA) as an example. Based on a one-loop calculation, we derive the renormalization group equation and velocity evolution equation for the first IM of quasi-DA. We find that, in the large velocity limit, the first IM of B-meson quasi-DA can be factorized into IM as well as logarithmic moments of light-cone distribution amplitude (LCDA), accompanied by short distance coefficients. Our results can be useful either in understanding the patterns of perturbative matching in large momentum effective theory or evaluating inverse moment of B-meson LCDA on the lattice

Comprehensive research on vibration characteristics, strength and stability of T-tail

T-tail has a more prominent problem of structural vibration, meanwhile, the problems of static strength and stability of its structure can not be ignored. Since T-tail structure is complex, it has multi-variables to optimize and the constraint functions of static strength, stability and vibration are quite different. Therefore it is difficult to get the optimal results through once optimization. This paper firstly set up a wind tunnel experiment of T-tail to test its aerodynamic characteristics. Then, the experimental results were compared with that of the simulation to verify the reliability of the simulation model. Based on the verified model, the influence factors on T-tail vibration characteristic were analyzed which can be set as the constraint of the subsequent optimization problem. At last, this paper considered the multi-disciplinary optimization problems of T-tail based on static strength, stability and vibration, used the idea of multi-level optimization, and designed a rational optimization scheme. And then the structure optimization of T-tail was carried out by the proposed scheme. Finally, the mass of T-tail is greatly reduced, and it also meets the design requirements of static strength, stability and vibration. The comprehensive performance is superior, which can also provide some reference to the multi-disciplinary optimization design of other similar structure