EDBT 2026 Demo / reviewers in the wild / expert
Raju Rangaswami
dblp:13/4977
· DBLP profile ↗
11ranked-venue papers in the field
1as first author
1since 2021 · last 2021
0009-0000-5243-9451ORCID · verified
Domains — venue-derived; a paper can count in several
Big Data, Cloud & Distributed Data Systems · 8Database Systems & Data Management · 3 (1 first)
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Learning Cache Replacement with CACHEUS
Liana V. Rodriguez, Farzana Beente Yusuf, Steven Lyons, Eysler Paz, Raju Rangaswami, Jason Liu 0001, Ming Zhao 0002, Giri Narasimhan |
FAST | 5 |
| 2015 | Non-blocking Writes to Files
Daniel Campello, Hector López, Ricardo Koller, Raju Rangaswami, Luis Useche |
FAST | 4 |
| 2013 | Write policies for host-side flash caches
Ricardo Koller, Leonardo Mármol, Raju Rangaswami, Swaminathan Sundararaman, Nisha Talagala, Ming Zhao 0002 |
FAST | 3 |
| 2011 | Cost Effective Storage using Extent Based Dynamic Tiering
Jorge Guerra, Himabindu Pucha, Joseph S. Glider, Wendy Belluomini, Raju Rangaswami |
FAST | 5 |
| 2010 | I/O Deduplication: Utilizing Content Similarity to Improve I/O Performance
Ricardo Koller, Raju Rangaswami |
FAST | 2 |
| 2010 | SRCMap: Energy Proportional Storage Using Dynamic Consolidation
Akshat Verma, Ricardo Koller, Luis Useche, Raju Rangaswami |
FAST | 4 |
| 2009 | BORG: Block-reORGanization for Self-optimizing Storage Systems
Medha Bhadkamkar, Jorge Guerra, Luis Useche, Sam Burnett, Jason Liptak, Raju Rangaswami, Vagelis Hristidis |
FAST | 6 |
| 2009 | 2LP: A double-lazy XML parser
Fernando Farfán, Vagelis Hristidis, Raju Rangaswami |
Inf. Syst. | 3 |
| 2007 | Beyond Lazy XML Parsing
Fernando Farfán, Vagelis Hristidis, Raju Rangaswami |
DEXA | 3 |
| 2003 | Design and Implementation of Semi-preemptible IO
Zoran Dimitrijevic, Raju Rangaswami, Edward Y. Chang |
FAST | 2 |
| 2003 | MEMS-based Disk Buffer for Streaming Media ServersabstractThe performance of streaming media servers has been limited due to the dual requirements of high throughput and low memory use. Although disk throughput has been enjoying a 40% annual increase, slower improvements in disk access times necessitate the use of large DRAM buffers to improve the overall streaming throughput. MEMS-based storage is an exciting new technology that promises to bridge the widening performance gap between DRAM and disk-drives in the memory hierarchy. We explore the impact of integrating these devices into the memory hierarchy on the class of streaming media applications. We evaluate the use of MEMS-based storage for buffering and caching streaming data. We also show how a bank of k MEMS devices can be managed in either configuration and that they can provide a k-fold improvement in both throughput and access latency. An extensive analytical study shows that using MEMS storage can reduce the buffering cost and improve the throughput of streaming servers significantly. Raju Rangaswami, Zoran Dimitrijevic, Edward Y. Chang, Klaus E. Schauser |
ICDE | 1 |