In this article, the problem of temporal isolation among containerized software components running in shared cloud infrastructures is tackled, proposing an approach based on hierarchical real-time CPU scheduling. This allows for reserving a precise share of the available computing power for each container deployed in a multi-core server, so to provide it with a stable performance, independently from the load of other co-located containers. The proposed technique enables the use of reliable modeling techniques for end-to-end service chains that are effective in controlling the application-level performance. An implementation of the technique within the well-known OpenStack cloud orchestration software is presented, focusing on a use-case framed in the context of network function virtualization. The modified OpenStack is capable of leveraging the special real-time scheduling features made available in the underlying Linux operating system through a patch to the in-kernel process scheduler. The effectiveness of the technique is validated by gathering performance data from two applications running in a real test-bed with the mentioned modifications to OpenStack and the Linux kernel. A performance model is developed that tightly models the application behavior under a variety of conditions. Extensive experimentation shows that the proposed mechanism is successful in guaranteeing isolation of individual containerized activities on the platform.

Analytics on modern data analytic and data ware-house systems often need to run large complex queries on increasingly complex database schemas. A lot of progress has been made on executing such complex queries using techniques like scale out query processing, hardware accelerators like GPUs and code generation techniques. However, optimization of such queries remains a challenge. Existing optimal solutions either cannot be effectively parallelized, or are inefficient while doing a lot of unnecessary work. In this demonstration, we present our system, GPU-QO, which aims to demonstrate query optimization techniques for large analytical queries using GPUs. We first demonstrate Massively Parallel Dynamic Programming (MPDP) - a novel query optimization technique that can run on GPUs to generate optimal plans in a (massively) parallel and efficient manner. We then showcase IDP2-MPDP and UnionDP - two heuristic techniques, again using GPUs, that can even optimize queries containing 1000s of joins. Furthermore, we compare our techniques with current state-of-the-art solutions, and demonstrate how our techniques can reduce optimization time for optimal solutions by nearly two orders of magnitude and produce much better query plans for heuristics (up to 7x).

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, finding the optimal join order becomes a bottleneck in query optimization. Due to the exponential nature of join order optimization, optimizers resort to heuristic solutions after a threshold number of tables. Our objective is two fold: (a) reduce the optimization time for generating optimal plans; and (b) improve the quality of the heuristic solution.

This paper deals with the problem of performance stability of software running in shared virtualized infrastructures. The focus is on the ability to build an abstract performance model of containerized application components, where real-time scheduling at the CPU level, along with traffic shaping at the networking level, are used to limit the temporal interferences among co-located workloads, so as to obtain a predictable distributed computing platform. A model for a simple client-server application running in containers is used as a case-study, where an extensive experimental validation of the model is conducted over a testbed running a modified OpenStack on top of a custom real-time CPU scheduler in the Linux kernel.

In this paper, we introduce XPySom, a new open-source Python implementation of the well-known Self-Organizing Maps (SOM) technique. It is designed to achieve high performance on a single node, exploiting widely available Python libraries for vector processing on multi-core CPUs and GP-GPUs. We present results from an extensive experimental evaluation of XPySom in comparison to widely used open-source SOM implementations, showing that it outperforms the other available alternatives. Indeed, our experimentation carried out using the Extended MNIST open data set shows a speed-up of about 7x and 100x when compared to the best open-source multi-core implementations we could find with multi-core and GP-GPU acceleration, respectively, achieving the same accuracy levels in terms of quantization error.

In ontology-based data access (OBDA), an ontology is connected to autonomous, and generally pre-existing, data repositories through mappings, so as to provide a high-level, conceptual view over such data. User queries are posed over the ontology, and answers are computed by reasoning both on the ontology and the mappings. Query answering in OBDA systems is typically performed through a query rewriting approach which is divided into two steps: (i) the query is rewritten with respect to the ontology (ontology rewriting of the query); (ii) the query thus obtained is then reformulated over the database schema using the mapping assertions (mapping rewriting of the query). In this paper we present a new approach to the optimization of query rewriting in OBDA. The key ideas of our approach are the usage of inclusion between mapping views and the usage of perfect mappings, which allow us to drastically lower the combinatorial explosion due to mapping rewriting. These ideas are formalized in PerfectMap, an algorithm for OBDA query rewriting. We have experimented PerfectMap in a real-world OBDA scenario: our experimental results clearly show that, in such a scenario, the optimizations of PerfectMap are crucial to effectively perform query answering.

Ontology-based data access (OBDA) is a novel paradigm for accessing large data repositories through an ontology, that is a formal description of a domain of interest. Supporting the management of OBDA applications poses new challenges, as it requires to provide effective tools for (i) allowing both expert and non-expert users to analyze the OBDA specification, (ii) collaboratively documenting the ontology, (iii) exploiting OBDA services, such as query answering and automated reasoning over ontologies, e.g., to support data quality check, and (iv) tuning the OBDA application towards optimized performances. To fulfill these challenges, we have built a novel system, called MASTRO STUDIO, based on a tool for automated reasoning over ontologies, enhanced with a suite of tools and optimization facilities for managing OBDA applications. To show the effectiveness of MASTRO STUDIO, we demonstrate its usage in one OBDA application developed in collaboration with the Italian Ministry of Economy and Finance.