Rafael Mira De Oliveira Libardi

PEESOS: A Web Tool for Planning and Execution of Experiments in Service Oriented Systems

Performing functionality testing in service-oriented architectures is not a trivial task. The difficulty is especially the large number of components that may be present in a SOA such as brokers, providers, service registries, clients, monitoring tools, data storage tools, etc. Thus, in order to facilitate the process of conducting functional testing and capacity planning in service-oriented systems, we present PEESOS. This first version is a functional prototype that offers facilities to assist researchers and industry to test their new applications, allowing collaborations that can be done between the participants to achieve an appropriate objective when developing a new application. The first results show that it is possible to make a planning environment easier to operate and to readily obtain results for performance evaluation of a target architecture. Since this is a first version of the prototype, it has interface and scalability limitations as well as needing improvements in performance of the logs repository and also in a core engine. We hope that such limitations can be corrected in the near future, including gathering information from the scientific community to make the prototype a useful and accessible tool. PEESOS is on-line and available at http://peesos.wsarch.lasdpc.icmc.usp.br.

A Study Case of Restful Frameworks in Raspberry Pi: A Performance and Energy Overview

This paper analyzes the execution behavior of web services on devices with limited resources. The experiments compare web services in the Axis2 and CXF frameworks analyzing performance and power consumption. To determine which framework is better suited for service provision, a testing environment and a performance and energy evaluation between them are presented. We show that the Raspberry Pi can be useful in service-oriented applications for different types of tasks. Bringing together the best features of small devices and SoC, it is possible to provide diverse, mobile and green applications.

Design and implementation of fault tolerance techniques to improve QoS in SOA

Fault tolerance techniques can improve the trust of users in service oriented architectures as they can ensure data availability. This paper presents an implementation of a novel fault tolerance mechanism in a SOA architecture which simultaneously provides increased availability and better quality of service. In addition to this mechanism, a service selector using reputation ratings of the architecture components is discussed. The selection is based on information from past transactions of the components of the architecture, which allows to identify the best web services able to meet the requests of customers. The mechanisms are tested and a performance evaluation is presented to validate the results.

Quality of service on services selection using anycast techniques: A convergence analysis

Anycast is an effective technique to select system servers among many and by spreading client requests achieve high performance and scalability. In our previous work, we presented the Global Application Layer Anycast (GALA) system. By combining real time network distances measures and geolocation, it performed better than its inherited algorithm, GAA. In this work we analyze how fast the GALA algorithm can find the best server to attend a client request and comparing its convergence to GAA. Using simulations, we show that it converges much faster and we propose a maximum selection time metric to be used in the selection process. Experimental results reveal that the GALA algorithm is two times better than the GAA considering the metric proposed.

Fast Selection of Web Services with QoS Using a Distributed Parallel Semantic Approach

This paper presents a solution to performance issues in the quality of service aware selection of Web services using techniques of parallelism and mechanisms of inference provided by Semantic Web. The results point to a significant improvement in the speed of searching Web services and thus makes the use of semantic resources viable in distributed systems to provide better quality of service to the clients.

A low cost workload generation approach through the cloud for capacity planning in service-oriented systems

This paper presents a cloud approach for low cost capacity planning evaluations. To perform these evaluations we have to specify and measure the workload on the target system to discover issues and make the necessary adjustments. However, due to high costs, these evaluations are usually done using simulations, which does not consider stochastic effects. We propose to use a tool named PEESOS, a generic and flexible approach to apply real workloads and measure used resources on these real systems. As a proof of concept, our case study use a real ticket sales service to evaluate the influence of scalability in the resource provisioning to show how PEESOS can lower the cost of such real evaluations. The results show the efficiency and savings that we can obtain using PEESOS for large-scale capacity planning evaluations before the real services are deployed. This approach can avoid several problems that real services faces when they launch.

Efficient web services selection based on QoS through a distributed parallel semantic approach

This paper proposes a solution to performance issues in the selection of Web services based on Quality of Service (QoS) that uses inference mechanisms from Semantic Web resources. Although several researchers highlight the benefits provided by the use of the Semantic Web techniques for searching and compose Web services with QoS, it can become costly when it depends on the approach adopted. Thus, we present a web service selection that uses QoS information in a replicated and distributed ontologies over different service providers. The results point to a significant improvement in the ontology inference process and thus makes the use of semantic resources viable in distributed systems to provide better QoS.

MSSF: User-Friendly Multi-cloud Data Dispersal

Using a multi-cloud storage solution requires a user to make complex decisions. Making these decisions can be a problem for regular users who are not familiar with multi-cloud storage. We propose MSSF, a Multi-cloud Storage Selection Framework to automatically select a storage dispersal strategy. MSSF formalises the selection process using a knapsack optimisation problem using integer linear programming along with a rule-based system to select a multi-cloud storage strategy that fits the user needs and requires only simple inputs from the user. Our experiments show the performance and usability aspects of our solution, making it useful in real environments.

Performance and energy evaluation of RESTful web services in Raspberry Pi

Green computing has emerged as a hot topic leading to a need to understand energy consumption of computations. This need also extends to devices with limited resources as are common in the internet of things. RESTful services have shown their potential on such devices, but there are many choices of frameworks for their development and execution. Current research has analysed performance of the frameworks but no attention has been given to systematically studying their power consumption. In this paper we analyse the execution behaviour and power consumption of web services on devices with limited resources and make initial observations that should influence future development of web service frameworks. Specifically, we conduct experiments comparing web services in the Axis2 and CXF frameworks analysing the respective performance and power consumption. Bringing together the best features of small devices and SoC, it is possible to provide diverse, mobile and green applications - however careful selection of development environments can make significant differences in performance and energy consumption.