Ricardo Vilaça

selected publications

1. Scalable transcriptomics analysis with Dask: applications in data science and machine learning

   Moreno, Marta, Vilaça, Ricardo, and Ferreira, Pedro G.

   BMC Bioinformatics Nov 2022

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   Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary.

2. Detailed Black-Box Monitoring of Distributed Systems

   Neves, Francisco, Vilaça, Ricardo, and Pereira, José

   SIGAPP Appl. Comput. Rev. Jul 2021

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   Modern containerized distributed systems, such as big data storage and processing stacks or micro-service based applications, are inherently hard to monitor and optimize, as resource usage does not directly match hardware resources due to multiple virtualization layers. For instance, interapplication traffic is an important factor in as it directly indicates how components interact, it has not been possible to accurately monitor it in an application independent way and without severe overhead, thus putting it out of reach of cloud platforms.In this paper we present an efficient black-box monitoring approach for gathering detailed structural information of collaborating processes in a distributed system that can be queried for various purposes, as it includes both information about processes, containers, and hosts, as well as resource usage and amount of data exchanged. The key to achieving high detail and low overhead without custom application instrumentation is to use a kernel-aided event driven strategy. We validate a prototype implementation by applying it to multi-platform microservice deployments, evaluate its performance with micro-benchmarks, and demonstrate its usefulness for container placement in a distributed data storage and processing stack (i.e., Cassandra and Spark).

3. Horus: Non-Intrusive Causal Analysis of Distributed Systems Logs

   Neves, Francisco, Machado, Nuno, Vilaça, Ricardo, and Pereira, José

   In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2021

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   Logs are still the primary resource for debugging distributed systems executions. Complexity and heterogeneity of modern distributed systems, however, make log analysis extremely challenging. First, due to the sheer amount of messages, in which the execution paths of distinct system components appear interleaved. Second, due to unsynchronized physical clocks, simply ordering the log messages by timestamp does not suffice to obtain a causal trace of the execution. To address these issues, we present Horus, a system that enables the refinement of distributed system logs in a causallyconsistent and scalable fashion. Horus leverages kernel-level probing to capture events for tracking causality between application-level logs from multiple sources. The events are then encoded as a directed acyclic graph and stored in a graph database, thus allowing the use of rich query languages to reason about runtime behavior. Our case study with TrainTicket, a ticket booking application with 40+ microservices, shows that Horus surpasses current widely-adopted log analysis systems in pinpointing the root cause of anomalies in distributed executions. Also, we show that Horus builds a causally-consistent log of a distributed execution with much higher performance (up to 3 orders of magnitude) and scalability than prior state-of-the-art solutions. Finally, we show that Horus’ approach to query causality is up to 30 times faster than graph database built-in traversal algorithms.

4. CODBS: A cascading oblivious search protocol optimized for real-world relational database indexes

   Pontes, Rogério, Portela, Bernado, Barbosa, Manuel, and Vilaça, Ricardo

   In 40th Symposium on Reliable Distributed Systems, SRDS 2021, online event, [Chicago, USA], September 20-23, 2021 2021

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   Encrypted databases systems and searchable encryption schemes still leak critical information (e.g.: access patterns) and require a choice between privacy and efficiency.We show that using ORAM schemes as a black-box is not a panacea and that optimizations are still possible by improving the data structures. We design an ORAM-based secure database that is built from the ground up: we replicate the typical data structure of a database system using different optimized ORAM constructions and derive a new solution for oblivious searches on databases. Our construction has a lower bandwidth overhead than state-ofthe- art ORAM constructions by moving client-side computations to a proxy with an intermediate (rigorously defined) level of trust, instantiated as a server-side isolated execution environment. We formally prove the security of our construction and show that its access patterns depend only on public information. We also provide an implementation compatible with SQL databases (PostgresSQL). Our system is 1.2 times to 4 times faster than state-of-the-art ORAM-based solutions.

5. Clouder: A Flexible Large Scale Decentralized Object Store

   Vilaça, Ricardo

   2012

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   Large scale data stores have been initially introduced to support a few concrete extreme scale applications, such as social networks. Their scalability and availability requirements often sacrifice richer data and processing models, and even elementary data consistency. In strong contrast with traditional relational databases (RDBMS), large scale data stores present very simple data models and APIs, lacking most of the established relational data management operations, and presenting relaxed consistency guarantees, providing eventual consistency. Nowadays, with a number of available and mature alternatives, there is an increasing willingness to use them in a wider and more diverse spectrum of applications by skewing the current trade-off toward the needs of common business users, and easing the migration from current RDBMS. This is particularly so when used in the context of a Cloud solution, such as in a Platform as a Service (PaaS). This thesis aims at reducing the gap between traditional RDBMS and large scale data stores by seeking mechanisms to provide additional consistency guarantees, and higher-level data processing primitives in large scale data stores. The devised mechanisms should not hinder the scalability and dependability of large scale data stores. Regarding higher-level data processing primitives, this thesis explores two complementary approaches: extending data stores with additional operations such as general multi-item operations; and coupling data stores with other existing processing facilities without hindering scalability. We address these challenges with a new architecture for large scale data stores, efficient multi-item access for large scale data stores, and SQL processing on large scale data stores. The novel architecture allows one to find the right trade-offs among flexible usage, efficiency, and fault tolerance. To efficiently support multi-item access,we extend first generation large scale data store’s data models with tags, and a multituple data placement strategy, allowing to efficiently store and retrieve large sets of related data at once. For efficient SQL support on scalable data stores, we devised design modifications to existing relational SQL query engines, allowing them to be distributed. We demonstrate our approaches with running prototypes and extensive experimental evaluation using proper workloads.

6. MeT: Workload aware elasticity for NoSQL

   Cruz, Francisco, Maia, Francisco, Matos, Miguel, Oliveira, Rui, Paulo, Joao, Pereira, José, and Vilaça, Ricardo

   In EuroSys 2013

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   NoSQL databases manage the bulk of data produced by modern Web applications such as social networks. This stems from their ability to partition and spread data to all available nodes, allowing NoSQL systems to scale. Unfortunately, current solutions’ scale out is oblivious to the underlying data access patterns, resulting in both highly skewed load across nodes and suboptimal node configurations. In this paper, we first show that judicious placement of HBase partitions taking into account data access patterns can improve overall throughput by 35%. Next, we go beyond current state of the art elastic systems limited to uninformed replica addition and removal by: i) reconfiguring existing replicas according to access patterns and ii) adding replicas specifically configured to the expected access pattern. MeT is a prototype for a Cloud-enabled framework that can be used alone or in conjunction with OpenStack for the automatic and heterogeneous reconfiguration of a HBase deployment. Our evaluation, conducted using the YCSB workload generator and a TPC-C workload, shows that MeT is able to i) autonomously achieve the performance of a manual configured cluster and ii) quickly reconfigure the cluster according to unpredicted workload changes.