Scalable Near Real-Time Failure Localization of Data Center Networks
Despite the built-in redundancy in data center networks, performance issues and device or link failures in the network can lead to user-perceived service interruptions. Therefore, determining and localizing user-impacting availability and performance issues in the network in near real time is crucial. Our key idea is to use statistical data mining techniques on large-scale active monitoring data to determine a ranked list of suspect causes, which we refine with passive monitoring signals.
Starfish: A Self-tuning System for Big Data Analytics
The Hadoop MapReduce platform is a popular choice for big data analytics. Unfortunately, Hadoop's performance out of the box leaves much to be desired, causing suboptimal use of resources, time, and money. Starfish is a self-tuning system for big data analytics that builds on Hadoop while adapting to system workloads and user needs to provide good performance automatically; without any need for users to understand and manipulate the many tuning knobs in the Hadoop platform.
Query Optimization Techniques for Partitioned Tables
Table partitioning has evolved into a powerful mechanism but is currently not utilized effectively during query optimization. We have developed new techniques to generate efficient plans for SQL queries involving multiway joins over partitioned tables. The techniques are designed for easy incorporation into bottom-up query optimizers and have been prototyped in PostgreSQL.
Automating the Process of SQL Tuning
zTuned is a new system that automates SQL tuning using an experiment-driven approach. The nontrivial challenge is to plan the best set of experiments to conduct so that a satisfactory (new) plan can be found quickly. A novel feature of zTuned is a SQL-tuning-aware query optimizer, called Xplus, capable of executing plans proactively, collecting monitoring data from the runs, and iterating. Xplus has been prototyped using PostgreSQL.