Distributed storage systems are constrained by the finite speed of propagation of information. The CAP (which stands for consistency, availability, and partition tolerance) theorem states that in the presence of network partitions, a choice has to be made in between availability and consistency. However, even in the absence of failures, a trade-off between consistency and latency of operations (reads and writes) exists. Eventually consistent storage systems often sacrifice consistency for high availability and low latencies. One way to achieve fine-tuning in the consistency-latency trade-off space is to inject artificial delays to each storage operation. This thesis describes an adaptive tuning framework that is able to calculate the values of artificial delay to be injected to each storage operation to meet a specific target consistency. The framework is able to adapt nimbly to environmental changes in the storage system to maintain target consistency levels. It consists of a feedback loop which uses a technique called spectral shifting at each iteration to calculate the target value of artificial delay from a history of operations. The tuning framework is able to converge to the target value of artificial delay much faster than the state-of-art solution. This thesis also presents a probabilistic analysis of inconsistencies in eventually consistent distributed storage systems operating under weak (read one, write one) consistency settings. The analysis takes into account symmetrical (same for reads and writes) artificial delays which enable consistency-latency tuning. A mathematical formula for the percentage of inconsistent operations is derived from other environmental parameters pertaining to the storage system. The formula's predictions for the proportion of inconsistent operations match observations of the same from a stochastic simulator of the storage system running 10^6 operations (per experiment), and from a widely used key-value store (Apache Cassandra) closely.

Cloud storage services and NoSQL systems typically offer only "Eventual Consistency", a rather weak guarantee covering a broad range of potential data consistency behavior. The degree of actual (in-)consistency, however, is unknown. This work presents novel solutions for determining the degree of (in-)consistency via simulation and benchmarking, as well as the necessary means to resolve inconsistencies leveraging this information.

Providing a shared memory abstraction in distributed systems is a powerful tool that can simplify the design and implementation of software systems for networked platforms. This enables the system designers to work with abstract readable and writable objects without the need to deal with the complexity and dynamism of the underlying platform. The key property of shared memory implementations is the consistency guarantee that it provides under concurrent access to the shared objects. The most intuitive memory consistency model is atomicity because of its equivalence with a memory system where accesses occur serially, one at a time. Emulations of shared atomic memory in distributed systems is an active area of research and development. The problem proves to be challenging, and especially so in distributed message passing settings with unreliable components, as is often the case in networked systems. We present several approaches to implementing shared memory services with the help of replication on top of message-passing distributed platforms subject to a variety of perturbations in the computing medium.

Cloud storage services and NoSQL systems typically offer only ""Eventual Consistency"", a rather weak guarantee covering a broad range of potential data consistency behavior. The degree of actual (in-)consistency, however, is unknown. This work presents novel solutions for determining the degree of (in-)consistency via simulation and benchmarking, as well as the necessary means to resolve inconsistencies leveraging this information. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

Distributed Systems: Concurrency and Consistency explores the gray area of distributed systems and draws a map of weak consistency criteria, identifying several families and demonstrating how these may be implemented into a programming language. Unlike their sequential counterparts, distributed systems are much more difficult to design, and are therefore prone to problems. On a large scale, usability reminiscent of sequential consistency, which would provide the same global view to all users, is very expensive or impossible to achieve. This book investigates the best ways to specify the objects that are still possible to implement in these systems. Explores the gray area of distributed systems and draws a map of weak consistency criteria Investigates the best ways to specify the objects that are still possible to implement in these systems Presents a description of existing memory models and consistency criteria

This book constitutes the refereed proceedings of the 19th International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2017, held in Boston, MA, USA, in November 2017. The 29 revised full papers presented together with 8 revised short papers were carefully reviewed and selected from 68 initial submissions. This year the Symposium was organized into three tracks reflecting major trends related to self-* systems: Stabilizing Systems: Theory and Practice: Distributed Computing and Communication Networks; and Computer Security and Information Privacy.