Automated Cluster-Based Web Service Performance Tuning

In this paper, we apply the Active Harmony system to improve the performance of a cluster-based web service system. The performance improvement cannot easily be achieved by tuning individual components for such a system. The experimental results show that there is no single configuration for the system that performs well for all kinds of workloads. By tuning the parameters, the Active Harmony helps the system adapt to different workloads and improve the performance up to 16%. For scalability, we demonstrate how to reduce the time when tuning a large system with many tunable parameters. Finally an algorithm is proposed to automatically adjust the structure of cluster-based web systems, and the system throughput is improved up to 70% using this technology. (UMIACS-TR-2003-84

Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning

Fine tuning distributed systems is considered to be a craftsmanship, relying on intuition and experience. This becomes even more challenging when the systems need to react in near real time, as streaming engines have to do to maintain pre-agreed service quality metrics. In this article, we present an automated approach that builds on a combination of supervised and reinforcement learning methods to recommend the most appropriate lever configurations based on previous load. With this, streaming engines can be automatically tuned without requiring a human to determine the right way and proper time to deploy them. This opens the door to new configurations that are not being applied today since the complexity of managing these systems has surpassed the abilities of human experts. We show how reinforcement learning systems can find substantially better configurations in less time than their human counterparts and adapt to changing workloads

Towards Automatic Performance Tuning

When the computing environment becomes heterogeneous and applications become modular with reusable components, automatic performance tuning is needed for these applications to run well in different environments. We present the Active Harmony automated runtime tuning system and describe the interface used by programs to make applications tunable. We present the optimization algorithm used to adjust application parameters and the Library Specification Layer which helps program library developers expose multiple variations of the same API using different algorithms. By comparing the experience stored in a database, the tuning server is able to find appropriate configurations more rapidly. Utilizing historical data together with a mechanism that estimates performance speeds up the tuning process. To avoid performance oscillations during the initial phase of the tuning process, we use improved search refinement techniques that use configurations equally spaced throughout the performance search space to make the tuning process smoother. We also introduce a parameter prioritizing tool to focus on those performance critical parameters. We demonstrate how to reduce the time when tuning a large system with many tunable parameters. The search space can be reduced by checking the relations among parameters to avoid unnecessary search. In addition, for homogeneous processing nodes, we demonstrate how to use one set of the parameters and replicate the values to the remaining processing nodes. For environments where parameters can be divided into independent groups, an individual tuning server is used for each group. An algorithm is given to automatically adjust the structure of cluster-based web systems and it improves the system throughput up to 70%. We successfully apply the Active Harmony system to a cluster-based web service system and scientific programs. By tuning the parameters, Active Harmony helps the system adapt to different workloads and improve the performance up to 16%. The performance improvement cannot easily be achieved by tuning individual components for such a system and there is no single configuration that performs well for all kinds of workloads. All the design and experimental results show that Active Harmony is a feasible and useful tool in performance tuning

ALOJA: A benchmarking and predictive platform for big data performance analysis

The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of cost-effectivenessof Big Data deployments. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system's cost-performance1. This article describes the evolution of the project's focus and research lines from over a year of continuously benchmarking Hadoop under dif- ferent configuration and deployments options, presents results, and dis cusses the motivation both technical and market-based of such changes. During this time, ALOJA's target has evolved from a previous low-level profiling of Hadoop runtime, passing through extensive benchmarking and evaluation of a large body of results via aggregation, to currently leveraging Predictive Analytics (PA) techniques. Modeling benchmark executions allow us to estimate the results of new or untested configu- rations or hardware set-ups automatically, by learning techniques from past observations saving in benchmarking time and costs.This work is partially supported the BSC-Microsoft Research Centre, the Span- ish Ministry of Education (TIN2012-34557), the MINECO Severo Ochoa Research program (SEV-2011-0067) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

An information retrieval approach to ontology mapping

In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e., using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported. \ud \u

D-SPACE4Cloud: A Design Tool for Big Data Applications

The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools and techniques to support the design of the underlying hardware configuration backing such systems. In particular, the focus in this report is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying QoS constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method