Emerson Loureiro

A flexible middleware for service provision over heterogeneous pervasive networks

Pervasive computing has gained much attention from the research community due to the possibility of deploying the first pervasive environments. Therefore, many software solutions are emerging, with the intent of facilitating the development of pervasive applications. Within this scope, in this paper, we introduce a service oriented middleware for pervasive computing, enhanced with runtime flexibility, extensibility for applications, and heterogeneous service provision. Our goal is to enable the middleware and its applications to be adapted to changing operational scenarios. Furthermore, different protocols can be used to discover and access services

A Fine-Grained Model for Adaptive On-Demand Provisioning of CPU Shares in Data Centers

Data Centers usually host different third party applications, each of them possibly having different requirements in terms of QoS. To achieve them, sufficient resources, like CPU and memory, must be allocated to each application. However, workload fluctuations might arise, and so, resource demands will vary. Allocations based on worst/average case scenarios can lead to non-desirable results. A better approach is then to assign resources on demand. Also, due to the complexity and size of current and future systems, self-adaptive solutions are essential. In this paper, we then present Grains , a self-adaptive approach for resource management in Data Centers under varying workload.

Decentralized and optimal control of shared resource pools

Resource pools are collections of computational resources (e.g., servers) which can be used by different applications in a shared way. A crucial aspect in these pools is to allocate resources so as to ensure their proper usage, taking into account workload and specific requirements of each application. An interesting approach, in this context, is to allocate the resources in the best possible way, aiming at optimal resource usage. Workload, however, varies over time, and in turn, resource demands will vary too. To ensure that optimal resource usage is always in place, resource shares should be defined dynamically and over time. It has been claimed that utility functions are the main tool for enabling such self-optimizing behavior. Whereas many solutions with this characteristic have been proposed to date, none of them presents true decentralization within the context of shared pools. In this article, we then propose a decentralized model for optimal resource usage in shared resource pools, providing practical and theoretical evidence of its feasibility.

A Java code annotation approach for model checking software systems

Many works related to the verification of software systems using model checking integrated to development environments have been proposed. JPF [7] and Bandera [1] are examples of such works that abstract the model to be verified from the Java code. An alternative way for JPF and Bandera approaches is to provide mechanisms for describing the model during the programming task. Thus, it is possible to integrate such mechanisms to the development environment and hide the model checking task from the programmer.

Decentralized Utility Maximization for Adaptive Management of Shared Resource Pools

Resource pools are collections of computational resources which can be shared by different applications. The goal with that is to accommodate the workload of each application, by splitting the total amount of resources in the pool among them. In this sense, utility functions have been pointed as the main tool for enabling self-optimizing behaviour in such pools. The goal with that is to allow resources from the pool to be split among applications, in a way that the best outcome is obtained. Whereas different solutions in this context exist, it has been found that none of them tackles the problem we deal with in a total decentralized way. In this paper, we then present a decentralized utility maximization approach for adaptive and optimal management of shared resource pools.

Adaptive Management of Shared Resource Pools with Decentralized Optimization and Epidemics

Shared resource pools are facilities featuring a certain amount of resources which can be used by different applications. For managing resources in such pools, the demand of each application can be used. Such a demand, however, is driven by the workload, which varies over time. For that reason, adaptive approaches have been proposed for the management of shared resources pools. Whereas a number of solutions exist in this context, they are either not truly decentralized or do not apply to the problem we are dealing with. In this paper, we then present Darma, an approach for managing shared resource pools in a truly decentralized, adaptive, and optimal way.

An infrastructure for developing context aware applications in pervasive environments

This paper presents the Lotus, an infrastructure to develop context-aware applications, providing the mechanisms for acquiring, representing, reasoning and delivering of contextual information. This infrastructure consists of a framework, which is composed by an API and a middleware, a language to represent contextual information and a mechanism to share and delivery contextual information.

Decentralized Self-optimization in Shared Resource Pools

Resource pools are collections of computational resources which can be shared by different applications. The goal with that is to accommodate the workload of each application, by splitting the total amount of resources in the pool among them. In this sense, utility functions have been pointed as the main tool for enabling self-optimizing behaviour in such pools. The goal with that is to allow resources from the pool to be split among applications, in a way that the best outcome is obtained. Whereas different solutions in this context exist, it has been found that none of them tackles the problem we deal with in a total decentralized way. In this paper, we then present a decentralized and self-optimizing approach for resource management in shared resource pools.