Geographically Distributed Database Management at the Cloud's Edge

146 Pages·2017·2.14 MB·English

Geographically Distributed Database
Management at the Cloud’s Edge
by
Ca˘t˘alin-Alexandru Avram
A thesis
presented to the University of Waterloo
in fulfillment of the
thesis requirement for the degree of
Doctor of Philosophy
in
Computer Science
Waterloo, Ontario, Canada, 2017
(cid:13)c Ca˘t˘alin-Alexandru Avram 2017

Examining Committee Membership
The following served on the Examining Committee for this thesis. The decision of the
Examining Committee is by majority vote.
External Examiner: Patrick Martin
Professor, School of Computing,
Queen’s University
Supervisor: Ken Salem
Professor, Cheriton School of Computer Science,
University of Waterloo
Internal Members: Bernard Wong
Associate Professor, Cheriton School of Computer Science,
University of Waterloo
¨
Tamer Ozsu
Professor, Cheriton School of Computer Science,
University of Waterloo
Internal-External Member: Wojciech Golab
Assistant Professor, Electrical and Computer Engineering,
University of Waterloo
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I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis,
including any required final revisions, as accepted by my examiners.
I understand that my thesis may be made electronically available to the public.
v

Abstract
Request latency resulting from the geographic separation between clients and remote ap-
plication servers is a challenge for cloud-hosted web and mobile applications. Numerous
studies have shown the importance of low latency to the end user experience. Small re-
sponse time increases on the order of a few hundred milliseconds directly translate to
reduced user satisfaction and loss of revenue that persist even after a low latency environ-
ment is restored. One way to address this challenge in geo-distributed settings is to push
all or part of the application, along with the data it requires, to the edge of the cloud -
closer to application clients. This thesis explores the idea of taking advantage of clients’
proximity to the edge of the network in order to reduce request latencies.
SpearDB is a prototype replicated distributed database system which operates in a
star network topology, with a core site and a large number of edge sites that are close to
clients. Clients access the nearest edge, which holds replicas of locally relevant portions of
the database. SpearDB’s edge sites coordinate through the core to provide a global trans-
actional consistency guarantee (parallel snapshot isolation or PSI), while handling as much
work locally as possible. SpearDB provides full general purpose transactional semantics
with ACID guarantees. Experiments show that SpearDB is effective at reducing workload
latencies for applications whose access patterns are geographically localizable. Many appli-
cations fit this criteria: bulletin boards (e.g., Craigslist, Kijiji), local commerce or services
(e.g., Groupon, Uber), booking and ticketing (e.g., OpenTable, StubHub), location based
services (mapping, directions, augmented reality), local news outlets and client-centric ser-
vices (e-mail, rss feeds, gaming). SpearDB introduces protocols for executing application
transactions in a geo-distributed setting under strong consistency guarantees. These pro-
tocols automatically hide the complexity as well as much of the latency introduced by
geo-distribution from applications.
The effectiveness of SpearDB depends on the placement of primary and secondary
replicas at core and edge sites. The secondary replica placement problem is shown to be
NP-hard. Several algorithms for automatic data partitioning and replication are presented
to provide approximate solutions. These algorithms work in a geo-distributed core-edge
setting under partial replication. Their goal is to bring data closer to clients in order to
lower request latencies. Experimental comparisons of the resulting placements’ latency
impact show good results. Surprisingly however, the placements produced by the simplest
of the proposed algorithms are comparable in quality to those produced by more complex
approaches.
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Acknowledgments
I would like to express my gratitude towards my supervisor, Dr. Ken Salem who has
been incredibly helpful throughout the long process of developing all the work that is
described in this document and instrumental to its completion. I have learned a lot from
him both personally and professionally.
A special thanks to Dr. Bernard Wong who also took part in the projects that have
lead to this thesis and provided me with much needed guidance along the way.
¨
I would like to thank the other members of my committee as well: Dr. Tamer Ozsu,
Dr. Patrick Martin and Dr. Wojciech Golab; for taking the time to read and review this
thesis.
And finally I would like to thank my family for all the support and understanding they
have provided throughout my studies.
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