WSMeter: A performance evaluation methodology for Google’s production warehouse-scale computers

WSMeter: A performance evaluation methodology for Google’s production warehouse-scale computers Lee et al., ASPLOS'18 (The link above is to the ACM Digital Library, if you don’t have membership you should still be able to access the paper pdf by following the link from The Morning Paper blog post directly.) How do you know how well ... Continue Reading

Tail attacks on web applications

Tail attacks on web applications Shan et al., CCS’17 This paper introduces a stealthy DDoS attack on classic n-tier web applications. It is designed to push the tail latency high while simultaneously being very hard to detect using traditional monitoring tools. The attack exploits ‘millibottlenecks’ — caused by buffers in the system that fill up ... Continue Reading

Automatic database management system tuning through large-scale machine learning

Automatic database management system tuning through large-scale machine learning Aken et al. , SIGMOD'17 Achieving good performance in DBMSs is non-trivial as they are complex systems with many tunable options that control nearly all aspects of their runtime operation. OtterTune uses machine learning informed by data gathered from previous tuning sessions to tune new DBMS ... Continue Reading

SyncPerf: Categorizing, detecting, and diagnosing synchronization performance bugs

SyncPerf: Categorizing, detecting, and diagnosing synchronization performance bugs Mejbah ul Alam et al., EuroSys'17 This paper is an investigation into the causes of synchronisation-related performance issues in concurrent systems, together with a pair of tools that can help to detect and diagnose them. The main SyncPerf detection tool is very lightweight (average overhead 2.3%). It ... Continue Reading

Statistical analysis of latency through semantic profiling

Statistical analysis of latency through semantic profiling Huang et al., EuroSys'17 Unlike traditional application profilers that seek to show the 'hottest' functions where an application spends the most time, VProfiler shows you where the sources of variance in latency come from, tied to semantic intervals such as individual requests or transactions. ... an increasing number ... Continue Reading