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Ankita Kejriwal

dblp:144/6273 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Storage systems
key-value storage
0.522016
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
0.212016
SLIK: Scalable Low-Latency Indexes for a Key-Value Store · USENIX ATC 2016
Storage systems
crash recovery
0.212015
The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015
Distributed systems › distributed database
distributed transactions
0.212015
Implementing linearizability at large scale and low latency · SOSP 2015
Storage systems › key-value storage
in-memory key-value store
0.212015
The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015
Distributed systems › consistency models
linearizability
0.212015
Implementing linearizability at large scale and low latency · SOSP 2015
Storage systems
storage reliability
0.212015
The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015
Distributed systems
transaction processing
0.212015
Implementing linearizability at large scale and low latency · SOSP 2015
Storage systems › file systems › write-optimized file system
log-structured file system
0.212014
Log-structured memory for DRAM-based storage · FAST 2014
Cloud and datacenter computing
datacenter storage
0.112015
The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015
Storage systems
distributed storage
0.112015
Implementing linearizability at large scale and low latency · SOSP 2015
Memory systems
DRAM
0.112015
The RAMCloud Storage System · ACM Trans. Comput. Syst. 2015
Memory systems › DRAM
DRAM-based storage
0.112014
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
YearPublicationVenuePosition
2016 SLIK: Scalable Low-Latency Indexes for a Key-Value Store
Ankita Kejriwal, Arjun Gopalan, John K. Ousterhout
USENIX ATC1
2015 Implementing linearizability at large scale and low latency
abstract
Linearizability 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
SOSP3
2015 The RAMCloud Storage System
abstract
RAMCloud 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
FAST2