Publications

Maia F, Paulo J, Coelho F, Neves F, Pereira JO, Oliveira R.  2017.  DDFlasks: Deduplicated Very Large Scale Data Store. Distributed Applications and Interoperable Systems - 17th IFIP WG 6.1 International Conference, DAIS 2017, Held as Part of the 12th International Federated Conference on Distributed Computing Techniques, DisCoTec 2017, Neuchâtel, Switzerland, June 1. :51–66. Abstract

n/a

Pontes R, Burihabwa D, Maia F, Paulo J, Schiavoni V, Felber P, Mercier H, Oliveira R.  2017.  SafeFS: a modular architecture for secure user-space file systems: one {FUSE} to rule them all. Proceedings of the 10th {ACM} International Systems and Storage Conference, {SYSTOR} 2017, Haifa, Israel, May 22-24, 2017. :9:1–9:12. Abstract
n/a
Maia F, Matos M, Coelho F.  2016.  Towards Quantifiable Eventual Consistency. Proceedings of the 6th International Conference on Cloud Computing and Services Science. :368-370. Abstractdatadiversityconvergence_2016_5.pdf

In the pursuit of highly available systems, storage systems began offering eventually consistent data models. These models are suitable for a number of applications but not applicable for all. In this paper we discuss a system that can offer a eventually consistent data model but can also, when needed, offer a strong consistent one.

Cruz F, Maia F, Matos M, Oliveira R, Paulo J, Pereira JO, Vilaça R.  2016.  Resource Usage Prediction in Distributed Key-Value Datastores. Distributed Applications and Interoperable Systems: 16th IFIP WG 6.1 International Conference, DAIS 2016, Held as Part of the 11th International Federated Conference on Distributed Computing Techniques, DisCoTec 2016, Heraklion, Crete, Greece, June 6-9, 2. :144–159. Abstract

n/a

Pontes R, Maia F, Paulo J, Vilaça R.  2016.  SafeRegions: Performance Evaluation of Multi-party Protocols on HBase. 2016 IEEE 35th Symposium on Reliable Distributed Systems Workshops (SRDSW), 2016 . :31-36.13.pdf
Burihabwa D, Pontes R, Felber P, Maia F, Mercier H, Oliveira R, Paulo J, Schiavoni V.  2016.  On the Cost of Safe Storage for Public Clouds: an Experimental Evaluation.. 35th IEEE International Symposium on Reliable Distributed Systems 2016.. 12.pdf
Pontes R, Maia F, Paulo J, Vilaça RMP.  2016.  SafeRegions: Performance Evaluation of Multi-party Protocols on HBase. 35th {IEEE} Symposium on Reliable Distributed Systems Workshops, {SRDS} 2016 Workshop, Budapest, Hungary, September 26, 2016. :31–36. Abstract
n/a
Burihabwa D, Pontes R, Felber P, Maia F, Mercier H, Oliveira R, Paulo J, Schiavoni V.  2016.  On the Cost of Safe Storage for Public Clouds: An Experimental Evaluation. 35th {IEEE} Symposium on Reliable Distributed Systems, {SRDS} 2016, Budapest, Hungary, September 26-29, 2016. :157–166. Abstract
n/a
Jorge T, Maia F, Matos M, Pereira JO, Oliveira R.  2015.  Practical Evaluation of Large Scale Applications. Proceedings of the 15th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS). :124–137. Abstractdais15minhajsr.pdf

Designing and implementing distributed systems is a hard endeavor, both at an abstract level when designing the system, and at a concrete level when implementing, debugging and evaluating it. This stems not only from the inherent complexity of writing and reasoning about distributed software, but also from the lack of tools for testing and evaluating it under realistic conditions. Moreover, the gap between the protocols’ specifications found on research papers and their implementations on real code is huge, leading to inconsistencies that often result in the implementation no longer following the specification. As an example, the specification of the popular Chord DHT comprises a few dozens of lines, while its Java implementation, OpenChord, is close to twenty thousand lines, excluding libraries. This makes it hard and error prone to change the implementation to reflect changes in the specification, regardless of programmers’ skill. Besides, critical behavior due to the unpredictable interleaving of operations and network uncertainty, can only be observed on a realistic setting, limiting the usefulness of simulation tools. We believe that being able to write an algorithm implementation very close to its specification, and evaluating it in a real environment is a big step in the direction of building better distributed systems. Our approach leverages the MINHA platform to offer a set of built in primitives that allows one to program very close to pseudo-code. This high level implementation can interact with off-the-shelf existing middleware and can be gradually replaced by a production-ready Java implementation. In this paper, we present the system design and showcase it using a well-known algorithm from the literature.

Pasquet M, Maia F, Rivière E, Schiavoni V.  2014.  Autonomous Multi-Dimensional Slicing for Large-Scale Distributed Systems. Proceedings of the 14th International Conference on Distributed Applications and Interoperable Systems - DAIS. Abstractautonomous-multi-dimensional.pdf

Slicing is a distributed systems primitive that allows to autonomously partition a large set of nodes based on node-local attributes. Slicing is decisive for automatically provisioning system resources for different services, based on their requirements or importance. One of the main limitations of existing slicing protocols is that only single dimension attributes are considered for partitioning. In practical settings, it is often necessary to consider best compromises for an ensemble of metrics.
In this paper we propose an extension of the slicing primitive that allows multi-attribute distributed systems slicing. Our protocol employs a gossip-based approach that does not require centralized knowledge and allows self-organization. It leverages the notion of domination between nodes, forming a partial order between multi-dimensional points, in a similar way to SkyLine queries for databases. We evaluate and demonstrate the interest of our approach using large-scale simulations.

Maia F, Matos M, Vilaça R, Pereira JO, Oliveira R, Rivière E.  2014.  DATAFLASKS: epidemic store for massive scale systems. The 33rd IEEE Symposium on Reliable Distributed Systems (SRDS 2014) . Abstractmain.pdf

Very large scale distributed systems provide some of the most interesting research challenges while at the same time being increasingly required by nowadays applications. The escalation in the amount of connected devices and data being produced and exchanged, demands new data management systems. Although new data stores are continuously being proposed, they are not suitable for very large scale environments. The high levels of churn and constant dynamics found in very large scale systems demand robust, proactive and unstructured approaches to data management. In this paper we propose a novel data store solely based on epidemic (or gossip-based) protocols. It leverages the capacity of these protocols to provide data persistence guarantees even in highly dynamic, massive scale systems. We provide an open source prototype of the data store and correspondent evaluation.

Cruz F, Maia F, Oliveira R, Vilaça R.  2014.  Workload-aware table splitting for NoSQL. SAC - Proceedings of the ACM Symposium on Applied Computing. Abstractdads14.pdf

Massive scale data stores, which exhibit highly desirable scalability and availability properties are becoming pivotal systems in nowadays infrastructures. Scalability achieved by these data stores is anchored on data independence; there is no clear relationship between data, and atomic inter-node operations are not a concern. Such assumption over data allows aggressive data partitioning. In particular, data tables are horizontally partitioned and spread across nodes for load balancing. However, in current versions of these data stores, partitioning is either a manual process or automated but simply based on table size. We argue that size based partitioning does not lead to acceptable load balancing as it ignores data access patterns, namely data hotspots. Moreover, manual data partitioning is cumbersome and typically infeasible in large scale scenarios. In this paper we propose an automated table splitting mechanism that takes into account the system workload. We evaluate such mechanism showing that it simple, non-intrusive and e fective.

Cruz F, Maia F, Matos M, Oliveira R, Paulo J, Pereira JO, Vilaça R.  2013.  MeT: Workload aware elasticity for NoSQL. Proceedings of the 8th European Conference on Computer Systems - EuroSys. :183–196. Abstractmet.pdf

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.

Maia F, Matos M, Vilaça R, Pereira JO, Oliveira R, Rivière E.  2013.  DataFlasks: an epidemic dependable key-value substrate. Proceedings of the 3rd International Workshop on Dependability of Clouds, Data Centers and Virtual Computing Environments (with DSN 2013). Abstractdataflasks_dcdv13.pdf

Recently, tuple-stores have become pivotal struc- tures in many information systems. Their ability to handle large datasets makes them important in an era with unprecedented amounts of data being produced and exchanged. However, these tuple-stores typically rely on structured peer-to-peer protocols which assume moderately stable environments. Such assumption does not always hold for very large scale systems sized in the scale of thousands of machines. In this paper we present a novel approach to the design of a tuple-store. Our approach follows a stratified design based on an unstructured substrate. We focus on this substrate and how the use of epidemic protocols allow reaching high dependability and scalability.

Maia F, Matos M, Rivière E, Oliveira R.  2013.  Slicing as a distributed systems primitive. Proceedings of the 6th Latin-American Symposium on Dependable Computing (LADC). Abstractslicing_primitive.pdf

Large-scale distributed systems appear as the major in- frastructures for supporting planet-scale services. These systems call for appropriate management mechanisms and protocols. Slicing is an example of an autonomous, fully decentral- ized protocol suitable for large-scale environments. It aims at organizing the system into groups of nodes, called slices, according to an application-specific criteria where the size of each slice is relative to the size of the full system. This al- lows assigning a certain fraction of nodes to different task, according to their capabilities. Although useful, current slicing techniques lack some features of considerable practical importance. This pa- per proposes a slicing protocol, that builds on existing so- lutions, and addresses some of their frailties. We present novel solutions to deal with non-uniform slices and to per- form online and dynamic slices schema reconfiguration. Moreover, we describe how to provision a slice-local Peer Sampling Service for upper protocol layers and how to en- hance slicing protocols with the capability of slicing over more than one attribute. Slicing is presented as a complete, dependable and inte- grated distributed systems primitive for large-scale systems.