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Jim Zelenka

dblp:12/4836 · DBLP profile ↗
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5ranked-venue papers
0as first author
0since 2021 · last 2008
—ORCID · none

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

Systems, architecture and hardware · 4Software engineering, systems software and programming languages · 4Databases, data management, data science and information retrieval · 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
5 papers
Storage systems · 85% Memory systems · 8% High-performance computing · 7%
Software engineering, system software, and programming languages
1 paper
Operating systems · 100%

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

TopicWeightPapersLastEvidence papers
Storage systems › file systems › distributed file system
parallel file system
0.112008
Scalable Performance of the Panasas Parallel File System · FAST 2008
Storage systems › file systems
distributed file system
0.021998
A Cost-Effective, High-Bandwidth Storage Architecture · ASPLOS 1998
File Server Scaling with Network-Attached Secure Disks · SIGMETRICS 1997
Storage systems › network-attached storage
network-attached secure disk
0.021998
A Cost-Effective, High-Bandwidth Storage Architecture · ASPLOS 1998
File Server Scaling with Network-Attached Secure Disks · SIGMETRICS 1997
Storage systems
network-attached storage
0.021998
A Cost-Effective, High-Bandwidth Storage Architecture · ASPLOS 1998
File Server Scaling with Network-Attached Secure Disks · SIGMETRICS 1997
High-performance computing
parallel i/o
0.012008
Scalable Performance of the Panasas Parallel File System · FAST 2008
Storage systems › file systems › distributed file system
file server scaling
0.011997
File Server Scaling with Network-Attached Secure Disks · SIGMETRICS 1997
Storage systems
disk array
0.011996
RAIDframe: Rapid Prototyping for Disk Arrays · SIGMETRICS 1996
Storage systems › storage reliability
RAID
0.011996
RAIDframe: Rapid Prototyping for Disk Arrays · SIGMETRICS 1996
Storage systems › buffer management
buffer allocation
0.011995
Informed Prefetching and Caching · SOSP 1995
Memory systems
cache management
0.011995
Informed Prefetching and Caching · SOSP 1995
Storage systems
file systems
0.011995
Informed Prefetching and Caching · SOSP 1995
Memory systems › cache
prefetching
0.011995
Informed Prefetching and Caching · SOSP 1995
Operating systems › i/o › i/o subsystem
i/o scheduling
0.011995
Informed Prefetching and Caching · SOSP 1995

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

cost-benefit analysis · 0.0direct client transfer · 0.0cryptographic secure interface · 0.0trace-driven replay · 0.0analytic modeling · 0.0rapid prototyping · 0.0
YearPublicationVenuePosition
2008 Scalable Performance of the Panasas Parallel File System
Brent Welch, Marc Unangst, Zainul Abbasi, Garth A. Gibson, Brian Mueller, Jason Small, Jim Zelenka
FAST7
1998 A Cost-Effective, High-Bandwidth Storage Architecture
abstract
This paper describes the Network-Attached Secure Disk (NASD) storage architecture, prototype implementations oj NASD drives, array management for our architecture, and three, filesystems built on our prototype. NASD provides scalable storage bandwidth without the cost of servers used primarily, for transferring data from peripheral networks (e.g. SCSI) to client networks (e.g. ethernet). Increasing datuset sizes, new attachment technologies, the convergence of peripheral and interprocessor switched networks, and the increased availability of on-drive transistors motivate and enable this new architecture. NASD is based on four main principles: direct transfer to clients, secure interfaces via cryptographic support, asynchronous non-critical-path oversight, and variably-sized data objects. Measurements of our prototype system show that these services can be cost-effectively integrated into a next generation disk drive ASK. End-to-end measurements of our prototype drive andfilesysterns suggest that NASD cun support conventional distributed filesystems without performance degradation. More importantly, we show scaluble bandwidth for NASD-specialized filesystems. Using a parallel data mining application, NASD drives deliver u linear scaling of 6.2 MB/s per clientdrive pair, tested with up to eight pairs in our lab.
Garth A. Gibson, David Nagle, Khalil Amiri, Jeff Butler, Fay W. Chang, Howard Gobioff, Charles Hardin, Erik Riedel, David Rochberg, Jim Zelenka
ASPLOS10
1997 File Server Scaling with Network-Attached Secure Disks
abstract
By providing direct data transfer between storage and client, network-attached storage devices have the potential to improve scalability for existing distributed file systems (by removing the server as a bottleneck) and bandwidth for new parallel and distributed file systems (through network striping and more efficient data paths). Together, these advantages influence a large enough fraction of the storage market to make commodity network-attached storage feasible. Realizing the technology's full potential requires careful consideration across a wide range of file system, networking and security issues. This paper contrasts two network-attached storage architectures---(1) Networked SCSI disks (NetSCSI) are network-attached storage devices with minimal changes from the familiar SCSI interface, while (2) Network-Attached Secure Disks (NASD) are drives that support independent client access to drive object services. To estimate the potential performance benefits of these architectures, we develop an analytic model and perform trace-driven replay experiments based on AFS and NFS traces. Our results suggest that NetSCSI can reduce file server load during a burst of NFS or AFS activity by about 30%. With the NASD architecture, server load (during burst activity) can be reduced by a factor of up to five for AFS and up to ten for NFS.
Garth A. Gibson, David Nagle, Khalil Amiri, Fay W. Chang, Eugene M. Feinberg, Howard Gobioff, Chen Lee, Berend Ozceri, Erik Riedel, David Rochberg, Jim Zelenka
SIGMETRICS11
1996 RAIDframe: Rapid Prototyping for Disk Arrays
William V. Courtright II, Garth A. Gibson, Mark Holland, Jim Zelenka
SIGMETRICS4
1995 Informed Prefetching and Caching
abstract
In this paper, we present aggressive, proactive mechanisms that tailor file system resource management to the needs of I/O-intensive applications. In particular, we show how to use application-disclosed access patterns (hints) to expose and exploit I/O parallelism, and to dynamically allocate file buffers among three competing demands: prefetching hinted blocks, caching hinted blocks for reuse, and caching recently used data for unhinted accesses. Our approach estimates the impact of alternative buffer allocations on application execution time and applies cost-benefit analysis to allocate buffers where they will have the greatest impact. We have implemented informed prefetching and caching in Digital’s OSF/1 operating system and measured its performance on a 150 MHz Alpha equipped with 15 disks running a range of applications. Informed prefetching reduces the execution time of text search, scientific visualization, relational database queries, speech recognition, and object linking by 20-83%. Informed caching reduces the execution time of computational physics by up to 42 % and contributes to the performance improvement of the object linker and the database. Moreover, applied to multiprogrammed, I/O-intensive workloads, informed prefetching and caching increase overall throughput. 1
R. Hugo Patterson, Garth A. Gibson, Eka Ginting, Daniel Stodolsky, Jim Zelenka
SOSP5