Marco A. C. Barbosa

Resources Planning in Database Infrastructures

Anticipating resources consumption is essential to project robust database infrastructures able to support transactions to be processed with certain quality levels. In Database-as-a-Service (DBaaS), for example, it could help to construct Service Level Agreements (SLA) to intermediate service customers and providers. A proper database resources assessment can avoid mistakes when choosing technology, hardware, network, client profiles, etc. However, to be properly evaluated, a database transaction usually requires the physical system to be measured, which can be expensive an time consuming. As most information about resource consumption are useful at design time, before developing the whole system, is essential to have mechanisms that partially open the black box hiding the in-operation system. This motivates the adoption of predictive evaluation models. In this paper, we propose a simulation model that can be used to estimate performance and availability of database transactions at design time, when the system is still being conceived. By not requiring real time inputs to be simulated, the model can provide useful information for resources planning. The accuracy of the model is checked in the context of a SLA composition process, in which database operations are simulated and model estimations are compared to measurements collected from a real database system.

Supervisory control of multiple robots subject to context switching

Coordinating concurrent multiple robots, in an efficient manner, is a task that challenges control engineers by its complexity. To be properly addressed, this task usually requires the support of formal approaches, such as the Supervisory Control Theory (SCT), which provides an automated way to calculate control strategies for event-based robots coordination. Little has been reported, however, on how to program controllers that automatically adapt themselves to the system context. In fact, self-adaptation support implies providing the robot brain with context-sensitive control strategies subject to changes at runtime. Without self-adaptation, on the other hand, each system configuration may require an entire control solution to be recalculated, which implies reestablishing the whole modeling structure. This paper shows that a system model can nevertheless be enriched with elements collected from the system context, which optimizes the design of formula-based constraints that can then be integrated to the SCT framework for control synthesis and posterior code generation. The result is a controller that recognizes the context and take control decisions accordingly. An example of multiple robots coordination illustrates the approach.

A modeling architecture for the orchestration of service components in factory automation

Service-Oriented Architecture (SOA) is a paradigm for software development that has been increasingly adopted for factory automation. In SOA, services are independently developed and a central engine orchestrates their functional behavior according to the process workflow. If on one hand this orchestration is required to maximize performance and productivity, i.e., the software is required to be maximally permissive, on the other hand, implementing a service orchestrator is a creative task which cannot be totally automated. Furthermore, industrial processes tend to be very large, making it difficult to empirically provide in-advance quality guarantees for industrial SOA-based applications. In this paper, we show how maximally permissive and deadlock-free service orchestrators can be implemented. We propose a model for each activity that compose a SOA programming language. Then, we show how pieces of a workflow can be individually represented by combining activity models. Afterwards, we specify the logical behavior of the workflow in order to organize those pieces and reproduce the orchestration effect. By using controllability concepts, we finally compute from the orchestrator a version of it that formally provides certain quality guarantees. Examples illustrate the approach.

A Formal Method Applied to the Automated Software Engineering with Quality Guarantees

Modern systems tend to be larger, more complex and to depend on an increasingly numerous set of requirements. In contrast, system development practices remain human-centered, depending mostly on the engineer expertise to be carried out. This paper shows how maximally permissive and deadlock-free components of software can be automatically produced. We argue that modeling methods and mathematical operations can be combined to systematically manage the software development process, based on high-level views of the system. Results show that possibly complex programming tasks become easier and independent from the expertise of the software engineer. Examples are provided to illustrate the approach.

Coordinating Agents in Dynamic Environment

This paper presents strategies for speeding up the convergence of agents on swarm. Speeding up the learning of an agent is a complex task since the choice of inadequate updating techniques may cause delays in the learning process or even induce an unexpected acceleration that causes the agent to converge to a non-satisfactory policy. We have developed strategies for updating policies which combines local and global search using past policies. Experimental results in dynamic environments of different dimensions have shown that the proposed strategies are able to speed up the convergence of the agents while achieving optimal action policies, improving the coordination of agents in the swarm while deliberating.

Combining Learning Algorithms: An Approach to Markov Decision Processes

In this paper we present a technique for estimating policies which combines instance-based learning and reinforcement learning algorithms in Markovian environments. This approach has been developed for speeding up the convergence of adaptive intelligent agents that using reinforcement learning algorithms. Speeding up the learning of an intelligent agent is a complex task since the choice of inadequate updating techniques may cause delays in the learning process or even induce an unexpected acceleration that causes the agent to converge to a non-satisfactory policy. Experimental results in real-world scenarios have shown that the proposed technique is able to speed up the convergence of the agents while achieving optimal policies, overcoming problems of classical reinforcement learning approaches.