EDBT 2026 Demo / reviewers in the wild / expert
Raghav Pisolkar
dblp:24/9511
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2Databases, data management, data science and information retrieval · 2
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
2 papers |
Cloud and datacenter computing · 34% Storage systems · 28% Distributed systems · 26% |
Topics — the 7 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing › cloud storage
multi-tenant cloud storage |
0.3 | 1 | 2018 | CloudKit: Structured Storage for Mobile Applications · Proc. VLDB Endow. 2018 |
Storage systems
flash and SSD |
0.1 | 1 | 2011 | Leveraging Value Locality in Optimizing NAND Flash-based SSDs · FAST 2011 |
Storage systems › flash and SSD › solid-state drive
flash-based SSD |
0.1 | 1 | 2011 | Leveraging Value Locality in Optimizing NAND Flash-based SSDs · FAST 2011 |
Processor architecture and microarchitecture
value locality |
0.1 | 1 | 2011 | Leveraging Value Locality in Optimizing NAND Flash-based SSDs · FAST 2011 |
Cloud and datacenter computing
cloud storage |
0.1 | 1 | 2018 | CloudKit: Structured Storage for Mobile Applications · Proc. VLDB Endow. 2018 |
Storage systems › data management
petabyte-scale data management |
0.1 | 1 | 2018 | CloudKit: Structured Storage for Mobile Applications · Proc. VLDB Endow. 2018 |
Memory systems
non-volatile memory |
0.0 | 1 | 2011 | Leveraging Value Locality in Optimizing NAND Flash-based SSDs · FAST 2011 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | CloudKit: Structured Storage for Mobile ApplicationsabstractCloudKit is Apple's cloud backend service and application development framework that provides strongly-consistent storage for structured data and makes it easy to synchronize data across user devices or share it among multiple users. Launched more than 3 years ago, CloudKit forms the foundation for more than 50 Apple apps, including many of our most important and popular applications such as Photos, iCloud Drive, Notes, Keynote, and News, as well as many third-party apps. To deliver this at large scale, CloudKit explicitly leverages multi-tenancy at the application level as well as at the user level to guide efficient data placement and distribution. By using CloudKit application developers are free to focus on delivering the application front-end and logic while relying on CloudKit for scale, consistency, durability and security. CloudKit manages petabytes of data and handles hundreds of millions of users around the world on a daily basis. Alexander Shraer, Alexandre Aybes, Bryan Davis, Christos Chrysafis, Dave Browning, Eric Krugler, Eric Stone, Harrison Chandler, Jacob Farkas, Jonathan Ruben, Michael Ford, Mike McMahon, Nathan Williams, Nicolas Favre-Felix, Nihar Sharma, Ori Herrnstadt, Paul Seligman, Raghav Pisolkar, Scott Dugas, Scott Gray, Shirley Lu, Sytze Harkema, Valentin Kravtsov, Vanessa Hong, Wan Ling Yih, Yizuo Tian |
Proc. VLDB Endow. | 19 |
| 2012 | When to forget: A system-level perspective on STT-RAMsabstractThe benefits of using STT-RAMs as an alternative to SRAMs are being examined in great detail. However their comparatively higher write latencies and energies continue to be roadblocks for migrating to MRAM based technology in memory hierarchies. In this paper, we present a novel method by which we demonstrate significant energy reduction in writing to the STT-RAM cell by relaxing its non-volatility property. We exploit this characteristic for optimizing system-level properties such as garbage collection. By categorizing the objects based on their lifetimes it is possible to tune the data retention time of the STT-RAM to minimize the write energy. Our scheme yielded 37% reduction in dynamic energy, 88% reduction in leakage and 85% improvement in the Energy-Delay Product over a corresponding SRAM based memory structure. Karthik Swaminathan, Raghav Pisolkar, Cong Xu 0002, Narayanan Vijaykrishnan |
ASP-DAC | 2 |
| 2011 | Leveraging Value Locality in Optimizing NAND Flash-based SSDs
Raghav Pisolkar, Bhuvan Urgaonkar, Anand Sivasubramaniam |
FAST | 2 |