A dissertation submitted in partial satisfaction of the requirements for the degree of
Doctor of Philosophy in Engineering — Electrical Engineering and Computer Sciences in the
Graduate Division of the
UNIVERSITY OF CALIFORNIA, BERKELEY
Committee in charge:
Professor Randy H. Katz, Chair Professor Vern Paxson Professor Ray R. Larson
Large-scale data-centric systems help organizations store, manipulate, and derive value from large volumes of data. They consist of distributed components spread across a scalable number of connected machines and involve complex software/hardware stacks with multiple semantic layers. These systems help organizations solve established prob- lems involving large amounts of data, while catalyzing new, data-driven businesses such as search engines, social networks, and cloud computing and data storage service providers. The complexity, diversity, scale, and rapid evolution of large-scale data-centric systems make it challenging to develop intuition about these systems, gain operational expe- rience, and improve performance. It is an important research problem to develop a method to design and evaluate such systems based on the empirical behavior of the tar- geted workloads. Using an unprecedented collection of nine industrial workload traces of business-critical large-scale data-centric systems, we develop a workload-driven design and evaluation method for these systems and apply the method to address previously unsolved design problems. Specifically, the dissertation contributes the following:
1. A conceptual framework of breaking down workloads for large-scale data-centric systems into data access patterns, computation patterns, and load arrival patterns.
2. A workload analysis and synthesis method that uses multi-dimensional, non- parametric statistics to extract insights and produce representative behavior.
3. Case studies of workload analysis for industrial deployments of MapReduce and en- terprise network storage systems, two examples of large-scale data-centric systems.
4. Case studies of workload-driven design and evaluation of an energy-efficient MapRe- duce system and Internet datacenter network transport protocol pathologies, two research topics that require workload-specific insights to address.
Overall, the dissertation develops a more objective and systematic understanding of an emerging and important class of computer systems. The work in this dissertation helps further accelerate the adoption of large-scale data-centric systems to solve real life prob- lems relevant to business, science, and day-to-day consumers.