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Daniel Peek

dblp:71/1924 · DBLP profile ↗
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7ranked-venue papers
4as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 4 · 3 first-authorSoftware engineering, systems software and programming languages · 3 · 1 first-authorComputer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
6 papers
Performance modeling and evaluation · 36% Memory systems · 15% Cloud and datacenter computing · 15%
Network and information security
2 papers
Systems and software security · 100%

Topics — the 13 heaviest of 15, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation › performance evaluation methodology
end-to-end performance analysis
0.212014
The Mystery Machine: End-to-end Performance Analysis of Large-scale Internet Services · OSDI 2014
Performance modeling and evaluation
workload characterization
0.212014
The Mystery Machine: End-to-end Performance Analysis of Large-scale Internet Services · OSDI 2014
Memory systems
cache
0.212013
Scaling Memcache at Facebook · NSDI 2013
Processor architecture and microarchitecture
speculative execution
0.112008
Parallelizing security checks on commodity hardware · ASPLOS 2008
Parallel and multicore computing
speculative parallelization
0.112008
Parallelizing security checks on commodity hardware · ASPLOS 2008
Storage systems › file systems
distributed file system
0.112007
Sprockets: Safe Extensions for Distributed File Systems · USENIX ATC 2007
Storage systems
distributed storage
0.112006
EnsemBlue: Integrating Distributed Storage and Consumer Electronics · OSDI 2006
Cloud and datacenter computing
datacenter services
0.112014
The Mystery Machine: End-to-end Performance Analysis of Large-scale Internet Services · OSDI 2014
Cloud and datacenter computing › datacenter services › online service systems › internet services
internet-scale services
0.112014
The Mystery Machine: End-to-end Performance Analysis of Large-scale Internet Services · OSDI 2014
Energy-efficient computing
storage power management
0.112005
Drive-Thru: Fast, Accurate Evaluation of Storage Power Management · USENIX ATC, General Track 2005
Systems and software security › information flow tracking
taint analysis
0.012008
Parallelizing security checks on commodity hardware · ASPLOS 2008
Energy-efficient computing
power management
0.012005
Drive-Thru: Fast, Accurate Evaluation of Storage Power Management · USENIX ATC, General Track 2005
Performance modeling and evaluation
storage performance evaluation
0.012005
Drive-Thru: Fast, Accurate Evaluation of Storage Power Management · USENIX ATC, General Track 2005

Methods — techniques the papers use, named apart from their topics

speculative execution · 0.2process-level replay · 0.2
YearPublicationVenuePosition
2014 The Mystery Machine: End-to-end Performance Analysis of Large-scale Internet Services
Michael Chow, David Meisner, Jason Flinn, Daniel Peek, Thomas F. Wenisch
OSDI4
2013 Scaling Memcache at Facebook
Rajesh Nishtala, Hans Fugal, Steven Grimm, Marc Kwiatkowski, Herman Lee, Harry C. Li, Ryan McElroy, Mike Paleczny, Daniel Peek, Paul Saab, David Stafford, Tony Tung, Venkateshwaran Venkataramani
NSDI9
2010 TrapperKeeper: The Case for Using Virtualization to Add Type Awareness to File Systems
Daniel Peek
HotStorage1
2008 Parallelizing security checks on commodity hardware
abstract
Speck (Speculative Parallel Check) is a system thataccelerates powerful security checks on commodity hardware by executing them in parallel on multiple cores. Speck provides an infrastructure that allows sequential invocations of a particular security check to run in parallel without sacrificing the safety of the system. Speck creates parallelism in two ways. First, Speck decouples a security check from an application by continuing the application, using speculative execution, while the security check executes in parallel on another core. Second, Speck creates parallelism between sequential invocations of a security check by running later checks in parallel with earlier ones. Speck provides a process-level replay system to deterministically and efficiently synchronize state between a security check and the original process.We use Speck to parallelize three security checks: sensitive data analysis, on-access virus scanning, and taint propagation. Running on a 4-core and an 8-core computer, Speck improves performance 4x and 7.5x for the sensitive data analysis check, 3.3x and 2.8x for theon-access virus scanning check, and 1.6x and 2x for the taint propagation check.
Ed Nightingale, Daniel Peek, Peter M. Chen, Jason Flinn
ASPLOS2
2007 Sprockets: Safe Extensions for Distributed File Systems
Daniel Peek, Ed Nightingale, Brett D. Higgins, Puspesh Kumar, Jason Flinn
USENIX ATC1
2006 EnsemBlue: Integrating Distributed Storage and Consumer Electronics
Daniel Peek, Jason Flinn
OSDI1
2005 Drive-Thru: Fast, Accurate Evaluation of Storage Power Management
Daniel Peek, Jason Flinn
USENIX ATC, General Track1