Abstract
In the context of the CloudDBAppliance (CDBA) project, fault tolerance and high-availability are provided in layers: within each appliance, within a data centre and between data centres. This paper presents the proposed replication architecture for providing fault tolerance and high availability within a data centre. This layer configuration, along with specific deployment constraints require a custom replication architecture. In particular, replication must be implemented at the middleware-level, to avoid constraining the backing operational database. This paper is focused on the design of the CDBA Replication Manager along with an evaluation, using micro-benchmarking, of components for the replication middleware. Results show the impact, on both throughput and latency, of the replication mechanisms in place.

Abstract
In the context of the CloudDBAppliance (CDBA) project, fault tolerance and high-availability are provided in layers: within each appliance, within a data centre and between datacentres. This paper presents the recovery mechanisms in place to fulfill the provision of high-availability within a datacentre. The recovery mechanism takes advantage of CDBA's in-middleware replication mechanism to bring failed replicas up-to-date. Along with the description of different variants of the recovery mechanism, this paper provides their comparative evaluation, focusing on the time it takes to recover a failed replica and how the recovery process impacts throughput.

Abstract
Data confidentiality in cloud services is commonly ensured by encrypting information before uploading it. However, this approach limits the use of content-aware functionalities, such as deduplication and compression. Although this issue has been addressed individually for some of these functionalities, no unified framework for building secure storage systems exists that can leverage such operations over encrypted data. We present TRUSTFS, a programmable and modular stackable file system framework for implementing secure content-aware storage functionalities over hardware-assisted trusted execution environments. This framework extends the original SAFEFS architecture to provide the isolated execution guarantees of Intel SGX. We demonstrate its usability by implementing an SGX-enabled stackable file system prototype while a preliminary evaluation shows that it incurs reasonable performance overhead when compared to conventional storage systems. Finally, we highlight open research challenges that must be further pursued in order for TRUSTFS to be fully adequate for building production-ready secure storage solutions.

Abstract
Modern storage infrastructures feature long and complicated I/O paths composed of several layers, each employing their own optimizations to serve varied applications with fluctuating requirements. However, as these layers do not have global infrastructure visibility, they are unable to optimally tune their behavior to achieve maximum performance. Background storage tasks, in particular, can rapidly overload shared resources, but are executed either periodically or whenever a certain threshold is hit regardless of the overall load on the system. In this paper, we argue that to achieve optimal holistic performance, these tasks should be dynamically programmable and handled by a controller with global visibility. To support this argument, we evaluate the impact on performance of compaction and checkpointing in the context of HBase and PostgreSQL. We find that these tasks can respectively increase 99th percentile latencies by 955.2% and 61.9%. We also identify future research directions to achieve programmable background tasks.

Abstract

Abstract
Testing large-scale distributed system software is still far from practical as the sheer scale needed and the inherent non-determinism make it very expensive to deploy and use realistically large environments, even with cloud computing and state-of-the-art automation. Moreover, observing global states without disturbing the system under test is itself difficult. This is particularly troubling as the gap between distributed algorithms and their implementations can easily introduce subtle bugs that are disclosed only with suitably large scale tests. We address this challenge with Minha, a framework that virtualizes multiple JVM instances in a single JVM, thus simulating a distributed environment where each host runs on a separate machine, accessing dedicated network and CPU resources. The key contributions are the ability to run off-the-shelf concurrent and distributed JVM bytecode programs while at the same time scaling up to thousands of virtual nodes; and enabling global observation within standard software testing frameworks. Our experiments with two distributed systems show the usefulness of Minha in disclosing errors, evaluating global properties, and in scaling tests orders of magnitude with the same hardware resources. © Nuno Machado, Francisco Maia, Francisco Neves, Fábio Coelho, and José Pereira; licensed under Creative Commons License CC-BY 23rd International Conference on Principles of Distributed Systems (OPODIS 2019).