UC Berkeley

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 problems 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 experience, 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 targeted 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 enterprise network storage systems, two examples of large-scale data-centric systems.

4. Case studies of workload-driven design and evaluation of an energy-efficient MapReduce 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 problems relevant to business, science, and day-to-day consumers.