José Gilvan Rodrigues Maia

Scale-Space Filtering for Workload Analysis and Forecast

Dynamic resource provisioning poses a major challenge for infrastructure providers because it is necessary to both forecast resource consumption and react to recent surges on demand for maintaining a tradeoff between quality of service and cost. However, approaches to workload analysis and forecast are affected due to noise in observed data, specially in forecast models. Moreover, most studies do not consider different prediction horizons may be necessary in order to take action before an SLA violation occurs. This paper presents an approach based in the scale-space theory to assist the dynamic resource provisioning. This theory is capable of eliminating the presence of irrelevant information from a signal that can potentially induce wrong or late decision making. In order to evaluate our approach, some experiments are presented considering both reactive and proactive strategies. The results confirm that our approach improves the workload analysis and forecast.

Machine Learning Approach for Cloud NoSQL Databases Performance Modeling

Cloud computing is a successful, emerging paradigm that supports on-demand services with pay-as-you-go model. With the exponential growth of data, NoSQL databases have been used to manage data in the cloud. In these newly emerging settings, mechanisms to guarantee Quality of Service heavily relies on performance predictability, i.e., the ability to estimate the impact of concurrent query execution on the performance of individual queries in a continuously evolving workload. This paper presents a performance modeling approach for NoSQL databases in terms of performance metrics which is capable of capturing the non-linear effects caused by concurrency and distribution aspects. Experimental results confirm that our performance modeling can accurately predict mean response time measurements under a wide range of workload configurations.

Elastic provisioning for cloud databases with uncertainty management

Cloud computing is a compelling, emerging paradigm that supports on-demand services with pay-as-you-go model. It is fundamental for cloud providers to allocate resource quantities suitable to ensure performance while reducing the operational costs related to both overprovisioning and penalties for SLA violations. Performance of cloud services may be unstable because of the environment. Most automatic provisioning techniques lack the capacity to handle the uncertainty of service performance. In this work, we investigate uncertainty management for cloud database elasticity from a probabilistic, performance-driven standpoint, and propose ProDBC, a novel approach for elastic provisioning. ProDBC uses a separate network for profiling in order to build a probabilistic model describing the relationship between workload, resource quantities and the subsequent performance. This model is embedded into a cost function in which the trade-off between infrastructure cost, SLA violation rate and the confidence level (uncertainty) is controlled intuitively. Experimental results obtained with the OLTP Database Benchmark showed provisioning actions taken by ProDBC provides elasticity while limiting the number of SLA violations.

An Interactive Storytelling Model for Non-player Characters on Electronic RPGs

Software-based RPG consists of traditional RPG themes and rules. However, the challenge of emulating the universe arises in the context of electronic RPGs. Moreover, such universe must be broad, be populated with interacting characters and allow exploration. Interactive Storytelling (IS) models are able to produce plots and interactive stories showing up proactive intelligent agents. This work proposes an IS model for electronic RPGs that takes into account its traditional counterpart. Our model includes a coherent scenario representation and an action system that considers time lapse. Moreover, itallows the description of autonomic characters which are ableto define goals, plans and make decisions. This paper also describes a case study constructed by means of a prototype implementation.

GameVis: Game Data Visualization for the Web

Gaming and eSports have become increasingly popular and complex. For that reason, it is necessary that tournament hosts and game developers provide a great amount of meaningful information for their audiences. Data visualization tools and frameworks are of upmost importance since these provides means to accomplish this task. In this paper we propose GameVis, a framework designed to aid developers in building game data visualization components for the Web. GameVis allows multiple data formats to be translated into a single data structure that can be extended and customized in many ways, allowing visualizations to suit different screen sizes and platforms while showing a great amount of variation and meaning. Visualizations built within the framework may feature animations, annotations and might even adapt to different visual themes in order to provide a richer, more interactive user experience. This is shown through experimental results.

Towards improvements on the quality of service for multi-tenant RDBMS in the cloud

Cloud computing is a trend of technology aimed at providing on-demand services with payment based on usage. To improve the use of resources, cloud providers adopt multi-tenant approaches, reducing the operation cost of services. Moreover, tenants have irregular workload patterns, impacting in the guarantees of quality of service, mainly due to interference between tenants. This paper proposes an approach to improve quality of service for multi-tenant RDBMS. This approach employs migration techniques of tenants, system monitoring, allocation strategy, forecast approach, and benefits of cloud infrastructure to improve performance and reduce provider cost. We carried out experiments on performance and resource usage in order to evaluate it.

How to deploy a virtual learning environment in the cloud

Cloud computing is a trend of technology aimed at providing on-demand services with payment based on usage. Virtual Learning Environments (VLEs) are applications that require a highly scalable architecture that provides for its users an acceptable level of Quality of Service (QoS). This work aims to show the steps needed to install a VLE in a cloud computing infrastructure. The VLEs migration to this new type of execution environment allows the increase of its use but also brings some performance issues that must be considered. The case study will consider the Moodle VLE which was chosen for its widespread use.

Analyzing Actions in Play-by-Forum RPG

Interactive Storytelling (IS) technologies are enabling richer experiences for electronic games. Current computational IS models are based on investigations about how games are planned by game designers and actually played by the audience. Unfortunately, most research efforts are limited to the structured data for obtaining insights about IS models. This paper presents a study aimed at determining which actions modeled by Role-Playing Games (RPG) are more important for actual gameplay and how these actions are related. For doing so, we first extracted textual data from existing adventures found in a play-by-forum RPG portal. Such gameplay data is written in Portuguese and reflect natural gameplay without observer intervention. Our analyses from a natural language processing perspective provide valuable insights for IS models in reducing the gameplay chasm between electronic and tabletop RPG.

Regression based performance modeling and provisioning for NoSQL cloud databases

Abstract Cloud computing is a successful and emerging paradigm that supports on-demand services with pay-as-you-go model. Because of the exponential growth of data, NoSQL databases have been used to manage data in the cloud. In this scenario, it is fundamental for cloud providers guarantee Quality of Service (QoS) by avoiding violations to Service Level Agreement (SLA) contract while reducing the operational costs related to overprovisioning and underprovisioning. In this regard, elastic provisioning mechanisms are employed to maintain QoS by dynamically adding and removing resources to handle workload fluctuations. These mechanisms can also take more accurate provisioning decisions based on performance predictions of the cluster shrinkage and growth. Performance prediction is a challenging task since concurrent access of distributed data can cause non-linear effects on performance. This paper presents a performance modeling approach for NoSQL databases in terms of SLA-based metrics capable of capturing non-linear effects caused by concurrency and distribution aspects. Moreover we present a elastic provisioning strategy that takes advantage on performance models to deliver a reliable resource provisioning. We carried out experiments in order to evaluate our performance modeling and provisioning approaches. The results confirmed that our performance modeling can accurately predict throughput and SLA violations measurements under a wide range of workload settings and also that our elastic provisioning approach can ensure QoS while using resources efficiently.

Learning Optimal Decision Lists as a Metaheuristic Search for Diagnosis of Parkinson’s Disease

Decision Lists are a very general model representation. In learning decision structures from medical datasets one needs a simple understandable model. Such a model should correctly classify unknown cases. One must search for the most general decision structure using the training dataset as input, taking into account both complexity and goodness-of-fit of the underlying model. In this paper, we propose to search the Decision List state space as an optimization problem using a metaheuristic. We implemented the method and carried out experimentation over a well-known Parkinson’s Disease training set. Our results are encouraging when compared to other machine learning references on the same dataset.