LADIS '13: 7th Workshop on Large-Scale Distributed Systems and Middleware
The Workshop on Large-Scale Distributed Systems and
Middleware (LADIS) brings together researchers and
practitioners to share the challenges of building massive
distributed computing systems and clouds. By raising research
questions in the context of the largest and most-demanding
real-world systems, LADIS serves to catalyze a dialog between
cloud computing engineers and researchers in scalable
distributed systems, to open the veil of secrecy that has
surrounded many cloud computing architectures, and to
increase the potential impact of the best research underway
in the theory and practice of large-scale distributed
systems. LADIS 2013 will be co-located with SOSP 2013 and be
held on Nov. 2nd (afternoon) and Nov. 3rd, 2013.
Checkout the CFP and
Please contact email@example.com
with any questions.
|Paper submission deadline: ||July 15, 2013|
|Acceptance notification: ||August 15, 2013|
|Camera Ready: ||September 30, 2013|
|Workshop: ||November 2-3, 2013|
Suggested Paper Topics
- Consistency, reliability and fault-tolerance models for cloud computing infrastructures and
the technologies to support them, including eventual/convergent consistency, transactions
and state-machine replication.
- Novel storage organizations for large-scale systems (e.g., NoSQL databases or key-value
storage), snapshot and weak isolation models, scalable and elastic transaction approaches
(e.g. mini-transactions), wide-area transactions.
- Large-scale infrastructure technologies, such as massive file system and database designs,
locking and synchronization services, group membership and communication services,
distributed registries, peer-to-peer, autonomic and self-* systems, etc.
- Support and programming models for scalable cloud-hosted applications and services, such
as map-reduce, global file systems, pub-sub, multicast and group communication.
- Novel architectures, paradigms, and application frameworks for parallel data processing,
large-scale analytics, and Big Data.
- Power and other resource management tools, such as virtualization and consolidation,
resource allocation, load balancing, resource placement, routing and scheduling.
- Privacy tools and models, including digital identity management, encrypting private data in
the cloud and information flow in data centers.
- Gregory Chockler, University of London
- Ymir Vigfusson, Reykjavik University
- Lorenzo Alvisi, University of Texas
- Lidong Zhou, Microsoft Research Asia
- Steven Hand, University of Cambridge
- Allen Clement, Max Planck Institute for Software Systems
- Rama Kotla, Microsoft Research Silicon Valley
- Jinyang Li, New York University
- George Porter, University of California, San Diego
- Rodrigo Rodrigues, Universidade Nova de Lisboa
- Jay Wylie, LinkedIn
- Matei Zaharia, University of California, Berkeley