Brendan Gregg (San Francisco, CA area) is Lead Performance Engineer at Joyent, where he analyzes performance and scalability for small to large cloud computing environments, at any level of the software stack. He is primary author of DTrace and co-author of Solaris Performance and Tools (both from Prentice Hall); as well as numerous articles about systems performance. Previously performance lead and kernel engineer at Sun Microsystems, he has also consulted and trained IT professionals on performance optimization. He developed DTraceToolkit and ZFS L2ARC, and many of his DTrace scripts are shipped by default in Mac OS X and Oracle Solaris 11. His recent work has included performance visualizations.

The accelerating deployment of large-scale web, cloud, Big Data, and virtualized computing systems has introduced serious new challenges in performance optimization. Until now, however, little reliable, practical information has been available to IT professionals who are responsible for running these systems efficiently and cost-effectively.

Systems Performance: Enterprise and the Cloud is the solution. Internationally renowned performance optimization expert Brendan Gregg brings together state-of-the-art techniques and tools for analysis and tuning of large-scale web/cloud computing environments.

Gregg focuses on Linux/Unix/Solaris performance issues, while offering proven methodologies and discussing key issues that apply to all enterprise operating systems. Coverage includes:

Modern performance analysis and capacity planning, including key issues such as latency and dynamic tracing

New performance and reliability challenges associated with cloud computing

Methodology, concepts, terminology, tools, and metrics

Key tradeoffs, including problems of load vs. architecture

Tuning operating systems, CPUs, memory, file systems, disks, networks, and busses

Tuning virtualized systems

Programming language issues related to performance — including application profiling for C, C++, Java, and node.js

Benchmarking strategies and pitfalls, including custom microbenchmarking