SRDS 2020 : Symposium on Reliable Distributed Systems

The 39th International Symposium on Reliable Distributed Systems (SRDS 2020) is a forum for researchers and practitioners interested in distributed systems design, development and evaluation, with emphasis on reliability, availability, safety, dependability, security, and real-time. We welcome Research Papers describing original research as well as design, development and experimental results of operational systems, Practical Experience Reports describing ongoing industrial projects, prototype systems and exploratory or emerging applications, and Tool Papers describing architecture, implementation and usage of substantive tools to aid the research and practice of reliable distributed systems. Papers will be assessed with criteria appropriate to each category.

The authors of the best ranked papers of SRDS will be invited to submit an extended version of their papers to IEEE Transactions on Dependable and Secure Computing.
Topics of Interest

The major areas of interest include the following topics:

Dependability, security and privacy of distributed systems including, but not limited to, cloud, high-performance, fog, and edge computing; distributed data storage and processing; distributed machine learning and AI; safety-critical distributed systems; Internet of Things, vehicular, robotic, cyber-physical and mobile systems.
Techniques and algorithms advancing the state-of-the-art in fault tolerance, fault recovery, robustness, self-stabilization, self-healing, scalability, and real-time for distributed systems. These include, but are not limited to, coordination, replication, failure prediction and detection, microservices, transactions, and blockchains.
Methods and tools for the design, implementation, verification, validation, and operation of dependable and secure distributed applications, middleware, operating systems and hardware.
Analytical, simulative and experimental assessment of dependable and secure distributed systems, in particular, when in real settings or with real data and in large scale and complex environments.