Dissertation/Thesis Abstract

Workload-Driven Design and Evaluation of Large-Scale Data-Centric Systems
by Chen, Yanpei, Ph.D., University of California, Berkeley, 2012, 158; 3527081
Abstract (Summary)

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.

Indexing (document details)
Advisor: Katz, Randy H.
Commitee: Larson, Ray R., Paxson, Vern
School: University of California, Berkeley
Department: Electrical Engineering & Computer Sciences
School Location: United States -- California
Source: DAI-B 74/02(E), Dissertation Abstracts International
Subjects: Computer science
Keywords: Datacenters, Design, Evaluation, Large-scale data, Mapreduce, Storage, Workload
Publication Number: 3527081
ISBN: 978-1-267-61201-4
Copyright © 2020 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy