Demo Data from EDBT 2026 / reviewers in the wild / expert
Ankita Kejriwal
dblp:144/6273
· DBLP profile ↗
4ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3 · 1 first-authorSoftware engineering, systems software and programming languages · 1Databases, 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
4 papers |
Storage systems · 66% Distributed systems · 26% Memory systems · 5% |
Topics — the 13 heaviest of 13, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
key-value storage |
0.5 | 2 | 2016 | SLIK: Scalable Low-Latency Indexes for a Key-Value Store · USENIX ATC 2016 The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015 |
Storage systems › indexing
index structure |
0.2 | 1 | 2016 | SLIK: Scalable Low-Latency Indexes for a Key-Value Store · USENIX ATC 2016 |
Storage systems
crash recovery |
0.2 | 1 | 2015 | The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015 |
Distributed systems › distributed database
distributed transactions |
0.2 | 1 | 2015 | Implementing linearizability at large scale and low latency · SOSP 2015 |
Storage systems › key-value storage
in-memory key-value store |
0.2 | 1 | 2015 | The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015 |
Distributed systems › consistency models
linearizability |
0.2 | 1 | 2015 | Implementing linearizability at large scale and low latency · SOSP 2015 |
Storage systems
storage reliability |
0.2 | 1 | 2015 | The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015 |
Distributed systems
transaction processing |
0.2 | 1 | 2015 | Implementing linearizability at large scale and low latency · SOSP 2015 |
Storage systems › file systems › write-optimized file system
log-structured file system |
0.2 | 1 | 2014 | Log-structured memory for DRAM-based storage · FAST 2014 |
Cloud and datacenter computing
datacenter storage |
0.1 | 1 | 2015 | The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015 |
Storage systems
distributed storage |
0.1 | 1 | 2015 | Implementing linearizability at large scale and low latency · SOSP 2015 |
Memory systems
DRAM |
0.1 | 1 | 2015 | The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015 |
Memory systems › DRAM
DRAM-based storage |
0.1 | 1 | 2014 | Log-structured memory for DRAM-based storage · FAST 2014 |
Methods — techniques the papers use, named apart from their topics
polling-based communication · 0.2log-structured storage · 0.2kernel bypass · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | SLIK: Scalable Low-Latency Indexes for a Key-Value Store
Ankita Kejriwal, Arjun Gopalan, John K. Ousterhout |
USENIX ATC | 1 |
| 2015 | Implementing linearizability at large scale and low latencyabstractLinearizability is the strongest form of consistency for concurrent systems, but most large-scale storage systems settle for weaker forms of consistency. RIFL provides a general-purpose mechanism for converting at-least-once RPC semantics to exactly-once semantics, thereby making it easy to turn non-linearizable operations into linearizable ones. RIFL is designed for large-scale systems and is lightweight enough to be used in low-latency environments. RIFL handles data migration by associating linearizability metadata with objects in the underlying store and migrating metadata with the corresponding objects. It uses a lease mechanism to implement garbage collection for metadata. We have implemented RIFL in the RAMCloud storage system and used it to make basic operations such as writes and atomic increments linearizable; RIFL adds only 530 ns to the 13.5 μs base latency for durable writes. We also used RIFL to construct a new multi-object transaction mechanism in RAMCloud; RIFL's facilities significantly simplified the transaction implementation. The transaction mechanism can commit simple distributed transactions in about 20 μs and it outperforms the H-Store main-memory database system for the TPC-C benchmark. Collin Lee, Seo Jin Park, Ankita Kejriwal, Satoshi Matsushita, John K. Ousterhout |
SOSP | 3 |
| 2015 | The RAMCloud Storage SystemabstractRAMCloud is a storage system that provides low-latency access to large-scale datasets. To achieve low latency, RAMCloud stores all data in DRAM at all times. To support large capacities (1PB or more), it aggregates the memories of thousands of servers into a single coherent key-value store. RAMCloud ensures the durability of DRAM-based data by keeping backup copies on secondary storage. It uses a uniform log-structured mechanism to manage both DRAM and secondary storage, which results in high performance and efficient memory usage. RAMCloud uses a polling-based approach to communication, bypassing the kernel to communicate directly with NICs; with this approach, client applications can read small objects from any RAMCloud storage server in less than 5μs, durable writes of small objects take about 13.5μs. RAMCloud does not keep multiple copies of data online; instead, it provides high availability by recovering from crashes very quickly (1 to 2 seconds). RAMCloud’s crash recovery mechanism harnesses the resources of the entire cluster working concurrently so that recovery performance scales with cluster size. John K. Ousterhout, Arjun Gopalan, Ankita Kejriwal, Collin Lee, Behnam Montazeri, Diego Ongaro, Seo Jin Park, Henry Qin, Mendel Rosenblum, Stephen M. Rumble, Ryan Stutsman |
ACM Trans. Comput. Syst. | 4 |
| 2014 | Log-structured memory for DRAM-based storage
Stephen M. Rumble, Ankita Kejriwal, John K. Ousterhout |
FAST | 2 |