Demo Data from EDBT 2026 / reviewers in the wild / expert
James Hendricks
dblp:81/4016
· DBLP profile ↗
7ranked-venue papers
3as first author
0since 2021 · last 2010
0000-0002-7793-1634ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 5 · 2 first-authorDatabases, data management, data science and information retrieval · 2Computer networks · 1Security and privacy · 1 · 1 first-authorSoftware engineering, systems software and programming languages · 1 · 1 first-author
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 |
Storage systems · 64% Distributed systems · 26% Performance modeling and evaluation · 9% | |
| Computer networks
1 paper |
Internet architecture and protocols · 77% Network management and operations · 23% |
Topics — the 16 heaviest of 17, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
distributed storage |
0.2 | 2 | 2010 | A Transparently-Scalable Metadata Service for the Ursa Minor Storage System · USENIX ATC 2010 Ursa Minor: Versatile Cluster-based Storage · FAST 2005 |
Storage systems › storage reliability
erasure coding |
0.1 | 2 | 2007 | Low-overhead byzantine fault-tolerant storage · SOSP 2007 Verifying distributed erasure-coded data · PODC 2007 |
Storage systems
storage reliability |
0.1 | 2 | 2007 | Low-overhead byzantine fault-tolerant storage · SOSP 2007 Verifying distributed erasure-coded data · PODC 2007 |
Storage systems › file systems › distributed file system
metadata service |
0.1 | 1 | 2010 | A Transparently-Scalable Metadata Service for the Ursa Minor Storage System · USENIX ATC 2010 |
Distributed systems › fault tolerance
byzantine fault tolerance |
0.1 | 1 | 2007 | Low-overhead byzantine fault-tolerant storage · SOSP 2007 |
Distributed systems
fault tolerance |
0.1 | 1 | 2007 | Low-overhead byzantine fault-tolerant storage · SOSP 2007 |
Performance modeling and evaluation › simulation
trace replay |
0.1 | 1 | 2007 | //TRACE: Parallel Trace Replay with Approximate Causal Events · FAST 2007 |
Storage systems › distributed storage
storage cluster |
0.1 | 1 | 2005 | Ursa Minor: Versatile Cluster-based Storage · FAST 2005 |
Internet architecture and protocols
domain name system |
0.0 | 1 | 2004 | Availability, usage, and deployment characteristics of the domain name system · Internet Measurement Conference 2004 |
Distributed systems
distributed coordination |
0.0 | 1 | 2010 | A Transparently-Scalable Metadata Service for the Ursa Minor Storage System · USENIX ATC 2010 |
Storage systems
metadata management |
0.0 | 1 | 2010 | A Transparently-Scalable Metadata Service for the Ursa Minor Storage System · USENIX ATC 2010 |
Cryptographic primitives and cryptanalysis
homomorphic fingerprinting |
0.0 | 1 | 2007 | Verifying distributed erasure-coded data · PODC 2007 |
Distributed systems › consensus
byzantine agreement |
0.0 | 1 | 2007 | Low-overhead byzantine fault-tolerant storage · SOSP 2007 |
Distributed systems
consensus |
0.0 | 1 | 2007 | Low-overhead byzantine fault-tolerant storage · SOSP 2007 |
Performance modeling and evaluation
workload characterization |
0.0 | 1 | 2007 | //TRACE: Parallel Trace Replay with Approximate Causal Events · FAST 2007 |
Network management and operations
availability analysis |
0.0 | 1 | 2004 | Availability, usage, and deployment characteristics of the domain name system · Internet Measurement Conference 2004 |
Methods — techniques the papers use, named apart from their topics
homomorphic fingerprinting · 0.1erasure coding · 0.1cryptographic hashing · 0.1causal events · 0.1versatility · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Zzyzx: Scalable fault tolerance through Byzantine lockingabstractZzyzx is a Byzantine fault-tolerant replicated state machine protocol that outperforms prior approaches and provides near-linear throughput scaling. Using a new technique called Byzantine Locking, Zzyzx allows a client to extract state from an underlying replicated state machine and access it via a second protocol specialized for use by a single client. This second protocol requires just one round-trip and 2 f + 1 responsive servers-compared to Zyzzyva, this results in 39-43% lower response times and a factor of 2.2-2.9× higher throughput. Furthermore, the extracted state can be transferred to other servers, allowing non-overlapping sets of servers to manage different state. Thus, Zzyzx allows throughput to be scaled by adding servers when concurrent data sharing is not common. When data sharing is common, performance can match that of the underlying replicated state machine protocol. James Hendricks, Shafeeq Sinnamohideen, Gregory R. Ganger, Michael K. Reiter |
DSN | 1 |
| 2010 | A Transparently-Scalable Metadata Service for the Ursa Minor Storage System
Shafeeq Sinnamohideen, Raja R. Sambasivan, James Hendricks, Likun Liu, Gregory R. Ganger |
USENIX ATC | 3 |
| 2007 | //TRACE: Parallel Trace Replay with Approximate Causal Events
Michael P. Mesnier, Matthew Wachs, Raja R. Sambasivan, Julio López 0002, James Hendricks, Gregory R. Ganger, David R. O'Hallaron |
FAST | 5 |
| 2007 | Verifying distributed erasure-coded dataabstractErasure coding can reduce the space and band width overheads of redundancy in fault-tolerant data storage and delivery systems. But it introduces the fundamental difficulty of ensuring that all erasure-coded fragments correspond to the same block of data. Without such assurance, a different block may be reconstructed from different subsets of fragments. This paper develops a technique for providing this assurance without the bandwidth and computational overheads associated with current approaches. The core idea is to distribute with each fragment what we call homomorphic fingerprints. These fingerprints preserve the structure of the erasure code and allow each fragment to be independently verified as corresponding to a specific block. We demonstrate homomorphic fingerprinting functions that are secure, efficient, and compact. James Hendricks, Gregory R. Ganger, Michael K. Reiter |
PODC | 1 |
| 2007 | Low-overhead byzantine fault-tolerant storageabstractThis paper presents an erasure-coded Byzantine fault-tolerant block storage protocol that is nearly as efficient as protocols that tolerate only crashes. Previous Byzantine fault-tolerant block storage protocols have either relied upon replication, which is inefficient for large blocks of data when tolerating multiple faults, or a combination of additional servers, extra computation, and versioned storage. To avoid these expensive techniques, our protocol employs novel mechanisms to optimize for the common case when faults and concurrency are rare. In the common case, a write operation completes in two rounds of communication and a read completes in one round. The protocol requires a short checksum comprised of cryptographic hashes and homomorphic fingerprints. It achieves throughput within 10% of the crash-tolerant protocol for writes and reads in failure-free runs when configured to tolerate up to 6 faulty servers and any number of faulty clients. James Hendricks, Gregory R. Ganger, Michael K. Reiter |
SOSP | 1 |
| 2005 | Ursa Minor: Versatile Cluster-based Storage
Michael Abd-El-Malek, William V. Courtright II, Chuck Cranor, Gregory R. Ganger, James Hendricks, Andrew J. Klosterman, Michael P. Mesnier, Manish Prasad, Brandon Salmon, Raja R. Sambasivan, Shafeeq Sinnamohideen, John D. Strunk, Eno Thereska, Matthew Wachs, Jay J. Wylie |
FAST | 5 |
| 2004 | Availability, usage, and deployment characteristics of the domain name systemabstractThe Domain Name System (DNS) is a critical part of the Internet's infrastructure, and is one of the few examples of a robust, highlyscalable, and operational distributed system. Although a few studies have been devoted to characterizing its properties, such as its workload and the stability of the top-level servers, many key components of DNS have not yet been examined. Based on large-scale measurements taken from servers in a large content distribution network, we present a detailed study of key characteristics of the DNS infrastructure, such as load distribution, availability, and deployment patterns of DNS servers. Our analysis includes both local DNS servers and servers in the authoritative hierarchy. We find that (1) the vast majority of users use a small fraction of deployed name servers, (2) the availability of most name servers is high, and (3) there exists a larger degree of diversity in local DNS server deployment and usage than for authoritative servers. Furthermore, we use our DNS measurements to draw conclusions about federated infrastructures in general. We evaluate and discuss the impact of federated deployment models on future systems, such as Distributed Hash Tables. Jeffrey Pang, James Hendricks, Aditya Akella, Roberto De Prisco, Bruce M. Maggs, Srinivasan Seshan |
Internet Measurement Conference | 2 |