Android malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and metadata to identify patterns in malware applications. Our experiments show the following: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android markets are more relevant in detecting malware, such as the application developer and certificate issuer; and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing.

This work performs a deep analysis on the behaviour of Anti-Virus (AV) engines regarding Android malware detection. A large dataset, with more than 80K apk files tagged as Malware by one or many AV engines is used in the analysis. With the help of association rule learning, we show interesting patterns and dependencies between different AV engines.

The recent expansion of the cloud computing paradigm has motivated educators to include cloud-related topics in computer science and computer engineering curricula. While programming and algorithm topics have been covered in different undergraduate and graduate courses, cloud architecture/system topics are still not usually studied in academic contexts. But design, deployment and management of datacenters, virtualization technologies for cloud, cloud management tools and similar issues should be addressed in current computer science and computer engineering programs. This work presents our approach and experiences in designing and implementing a curricular module covering all these topics. In this approach the utilization of a simulation tool, CloudSim, is essential to allow the students a practical approximation to the course contents.

The Infrastructure as a Service (IaaS) paradigm allows service providers and/or end-users to outsource the computing resources they need. Cloud providers offer the infrastructure as a utility with a pay-per-use model, relying on virtualization technologies to provide hardware resources to the cloud consumers. IaaS can potentially bring obvious advantages, but the virtualized resources use to be manually controlled by the consumers, and this may lead to unaffordable administration costs. The demand of computing resources can fluctuate from one time to another and managing the virtual machines “rented” to the cloud provider to meet peak requirements but to avoid overprovisioning, is a significant challenge. This paper presents AMAS (Automatic MAchine Scaling), a distributed solution capable of automatically creating and releasing virtual machines in order to minimize the number of virtual resources instantiated to run an application and to meet the consumer performance requirements. Furthermore, the complete design and a first real implementation of this solution is described to validate that it is capable of handling sudden load changes, maintaining the desired quality of service, minimizing the number of virtual machines and significantly reducing the consumer management efforts.

Lean Manufacturing initiatives try to understand where the sources of waste are in a manufacturing process in order to minimize or avoid them and to add value to the stakeholders. The improvement actions related to lean methods are always incremental, fast and easy; mainly based on people’s experience and on simple tools. For example, a Value Stream Map (VSM) is created to have a visual representation of the material and information ¤ows involved in the production process, allowing improvement teams to detect where is the waste introduced and where is the value added. The integration of simulation with VSM could considerably improve the results obtained in lean projects, helping the decision makers in adding dynamic information to the usually considered static pictures of the processes. The information obtained from what-if simulations allows to detect improvement opportunities, to prioritize them, to analyze the best implementation alternative, and to quantify the possible bene£ts of the proposed actions. This work presents the LeanSim framework, an easy to use tool based on Matlab and Simulink, capable of integrating a lean method such as VSM with simulation. Furthermore, a case study of an improvement project at a milk production plant is presented to illustrate the utilization of this new framework on a real environment.

New high performance architectures combining high and low level techniques are widely used today, and FPGA-based designs offer excellent platforms for this kind of systems. This paper describes the HIPAOC (HIgh Performance Architecture On Chip) system, a general purpose and reconfigurable high performance architecture implemented on a single FPGA. The proposed design can be configured to work as a shared memory system (multiprocessor) or as a distributed memory system (multicomputer or cluster). The selection of the interconnection network topology in this kind of system is a major design decision. Performance metrics are needed to guide the designer and the user in their decisions with quantitative information. This work proposes a scalability metric and a scalability analysis procedure to optimize the design of Networks on Chip.

The concept of Zero Latency Enterprise (ZLE) has received considerable attention through the last years. Many companies and organizations want to see the value of this kind of strategy before changing their IT infrastructure to implement ZLE solution. This paper proposes a new project methodology defining five main steps that should be followed to success in ZLE implementation projects. One of these steps, the Planning and Decision Making stage, is based on simulation techniques, allowing a comparison of the organization performance with and without ZLE strategies. Furthermore, in this work the different approaches for the ZLE simulation are examined and discussed.

The workload on a cluster system can be highly variable, increasing the difficulty of balancing the load across its nodes. And the general rule is that high variability leads to wrong load balancing decisions taken with out-of-date information and difficult to correct in real-time during applications execution.In this work the workload variability is studied from theperspective of the load balancing performance, focusing onthe design of algorithms capable of dealing with this variability. Two different robustness metrics are proposed to select the best distribution rule for the algorithm, remote execution or process migration. With the proposed solution only when a high variability causes an intolerable performance degradation the process migration is used to balance the load in the system.

The success of different computing models, performance analysis and load balancing and algorithms depends on the processor availability information because there is a strong relationship between a process response time and the processor time available for its execution. Therefore, predicting the processor availability for a new process or task in a computer system is a basic problem that arises in in many important contexts. Unfortunately, making such predictions is not easy because of the dynamic nature of current computer systems and their workload, which can vary drastically in a short interval of time. This paper presents two new availability prediction models. The first, called SPAP (Static Process Assignment Prediction) model, is capable of predicting the CPU availability for a new task on a computer system having information about the tasks in its run queue. The second, called DYPAP (DYnamic Process Assignment Prediction) model, is an improvement of the SPAP model capable of making these predictions from real-time measurements provided by a monitoring tool, without any kind of information about the tasks in the run queue. Furthermore, the implementation of this monitoring tool for Linux workstations is presented.

Cluster heterogeneity increases the difficulty of balancing the load across the system nodes. Although the relationship between heterogeneity and load balancing is difficult to describe analytically, in this paper an exhaustive analysis of the effects of this system feature on load balancing algorithms performance is presented. Considering the performed analysis, there are two main challenges that need to be faced when dealing with cluster heterogeneity in load balancing algorithms: one related to the state measurement stage and another to the initiation rule. In this paper techniques to deal with heterogeneity in these two algorithm stages are proposed. Furthermore, suggestions to improve the performance of the rest of algorithm stages in heterogeneous environments are made too