Performance and Energy-Based Cost Prediction of Virtual Machines Auto-Scaling in Clouds

Virtual Machines (VMs) auto-scaling is an important technique to provision additional resource capacity in a Cloud environment. It allows the VMs to dynamically increase or decrease the amount of resources as needed in order to meet Quality of Service (QoS) requirements. However, the auto-scaling mechanism can be time-consuming to initiate (e.g. in the order of a minute), which is unacceptable for VMs that need to scale up/out during the computation, besides additional costs due to the increase of the energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs auto-scaling by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the auto-scaling workload, power consumption and total cost for heterogeneous VMs, with a cost-saving of up to 25% for the predicted total cost of VM self-configuration as compared to the current approaches in literature

Rethinking engagement in urban design: reimagining the value of co-design and participation at every stage of planning for autonomous vehicles.

The practical demonstrations and research which led to the preparation of this paper involved a combination of stakeholder engagement, policy debate and the practical demonstration and testing of autonomous vehicles. By adhering to a design approach which in centred on participation and human-centred engagement, the advent of autonomous vehicles might avoid many of the problems encountered in relation to conventional transport. The research explored how a new and potentially disruptive technology might be incorporated in urban settings, through the lens of participation and problem-based design. The research critically reviews key strands in the literature (autonomous vehicles, social research and participatory design), with allusion to current case study experiments. Although there are numerous examples of autonomous vehicles (AV) research concentrating on technical aspects alone, this paper finds that such an approach appears to be an unusual starting point for the design of innovative technology. That is, AVs would appear to hold the potential to be genuinely disruptive in terms of innovation, yet the way that disruption takes place should surely be guided by design principles and by issues and problems encountered by potential users. Practical implications: The research carries significant implications for practice in that it advocates locating those socio-contextual issues at the heart of the problem definition and design process and ahead of technical solutions. What sets this research apart from other studies concerning AVs was that the starting point for investigation was the framing of AVs within contexts and scenarios leading to the emergence of wicked problems. This begins with a research position where the potential uses for AVs are considered in a social context, within which the problems and issues to be solved become the starting point for design at a fundamental level

Facilitating the Quantitative Analysis ofComplexEvents through a Computational Intelligence Model-Driven Tool

Complex event processing (CEP) is a computational intelligence technology capable of analyzing big data streams for event pattern recognition in real time. In particular, this technology is vastly useful for analyzing multicriteria conditions in a pattern, which will trigger alerts (complex events) upon their fulfillment. However, one of the main challenges to be faced by CEP is how to define the quantitative analysis to be performed in response to the produced complex events. In this paper, we propose the use of the MEdit4CEP-CPN model-driven tool as a solution for conducting such quantitative analysis of events of interest for an application domain, without requiring knowledge of any scientific programming language for implementing the pattern conditions. Precisely, MEdit4CEP-CPN facilitates domain experts to graphically model event patterns, transform them into a Prioritized Colored Petri Net (PCPN) model, modify its initial marking depending on the application scenario, and make the quantitative analysis through the simulation and monitor capabilities provided by CPN tools

Multi-granular Software Annotation using File-level Weak Labelling

One of the most time-consuming tasks for developers is the comprehension of new code bases. An effective approach to aid this process is to label source code files with meaningful annotations, which can help developers understand the content and functionality of a code base quicker. However, most existing solutions for code annotation focus on project-level classification: manually labelling individual files is time-consuming, error-prone and hard to scale. The work presented in this paper aims to automate the annotation of files by leveraging project-level labels; and using the file-level annotations to annotate items at larger levels of granularity, for example, packages and a whole project. We propose a novel approach to annotate source code files using a weak labelling approach and a subsequent hierarchical aggregation. We investigate whether this approach is effective in achieving multi-granular annotations of software projects, which can aid developers in understanding the content and functionalities of a code base more quickly. Our evaluation uses a combination of human assessment and automated metrics to evaluate the annotations' quality. Our approach correctly annotated 50% of files and more than 50\% of packages. Moreover, the information captured at the file-level allowed us to identify, on average, three new relevant labels for any given project. We can conclude that the proposed approach is a convenient and promising way to generate noisy (not precise) annotations for files. Furthermore, hierarchical aggregation effectively preserves the information captured at file-level, and it can be propagated to packages and the overall project itself.Comment: Accepted at the Journal of Empirical Software Engineerin

Community Safety and Well-being in Touristic Spots Using Open Data

Assis, D., De Castro Neto, M., & Motta, M. (2021). Community Safety and Well-being in Touristic Spots Using Open Data. International Journal of Modeling and Optimization, 11(1), 1-11. https://doi.org/10.7763/IJMO.2021.V11.770There are many different reasons that can lead a tourist to decide which destination will be chosen on his/her next trip. Besides knowing what are the attractions that must be visited, it is also common to look for more information regarding the overall safety and well-being conditions of travel destinations. Usually shared by local authorities, this kind of information can also be found in a less structured form through public sources, such as web sites and social platforms. However, there are a couple of challenges to be considered: the predominance of unstructured data; the lack of a common standard to distinguish safe and unsafe places; the distinct period needed to update the collected data. In this study, the proposed model combines official census data with open data, social platforms and other online sources, allowing the definition of a score for touristic spots in Lisbon. The resulting score should be able to quantify the community safety and well-being, as well as to identify threats and opportunities for the local tourism industry. Furthermore, it would not only help tourists in their traveling decisions but also, allow decision-makers to track socioeconomic issues and to support public management through a data-driven approach.publishersversionpublishe