Workflows have been used to represent a variety of applications involving high processing and storage demands. As a solution to supply this necessity, the cloud computing paradigm has emerged as an on-demand resources provider. While public clouds charge users in a per-use basis, private clouds are owned by users and can be utilized with no charge. When a public cloud and a private cloud are merged, we have what we call a hybrid cloud. In a hybrid cloud, the user has elasticity provided by public cloud resources that can be aggregated to the private resources pool as necessary. One question faced by the users in such systems is: Which are the best resources to request from a public cloud based on the current demand and on resources costs? In this paper we deal with this problem, presenting HCOC: The Hybrid Cloud Optimized Cost scheduling algorithm. HCOC decides which resources should be leased from the public cloud and aggregated to the private cloud to provide sufficient processing power to execute a workflow within a given execution time. We present extensive experimental and simulation results which show that HCOC can reduce costs while achieving the established desired execution time.

Vehicular Edge Computing (VEC), based on the Edge Computing motivation and fundamentals, is a promising technology supporting Intelligent Transport Systems services, smart city applications, and urban computing. VEC can provide and manage computational resources closer to vehicles and end-users, providing access to services at lower latency and meeting the minimum execution requirements for each service type. This survey describes VEC’s concepts and technologies; we also present an overview of existing VEC architectures, discussing them and exemplifying them through layered designs. Besides, we describe the underlying vehicular communication in supporting resource allocation mechanisms. With the intent to overview the risks, breaches, and measures in VEC, we review related security approaches and methods. Finally, we conclude this survey work with an overview and study of VEC’s main challenges. Unlike other surveys in which they are focused on content caching and data offloading, this work proposes a taxonomy based on the architectures in which VEC serves as the central element. VEC supports such architectures in capturing and disseminating data and resources to offer services aimed at a smart city through their aggregation and the allocation in a secure manner.

Organizations face the ever growing challenge of providing security within their IT infrastructures. Static approaches to security, such as perimetral defense, have proven less than effective-and, therefore, more vulnerable-in a new scenario characterized by increasingly complex systems and by the evolution and automation of cyber attacks. Moreover, dynamic detection of attacks through IDSs (Instrusion Detection Systems) presents too many false positives to be effective. This work presents an approach on how to collect and normalize, as well as how to fuse and classify, security alerts. This approach involves collecting alerts from different sources and normalizes them according to standardized structures-IDMEF (Intrusion Detection Message Exchange Format). The normalized alerts are grouped into meta-alerts (fusion, or clustering), which are later classified using machine learning techniques into attacks or false alarms. We validate and report an implementation of this approach against the DARPA Challenge and the Scan of the Month, using three different classifications-SVMs, Bayesian Networks and Decision Trees-having achieved high levels of attack detection with little false positives. Our results also indicate that our approach outperforms other works when it comes to detecting new kinds of attacks, making it more suitable to a world of evolving attacks.

In the traditional approach for centrality measures, also known as sociocentric, a network node usually requires global knowledge of the network topology in order to evaluate its importance. Therefore, it becomes difficult to deploy such an approach in large-scale or highly dynamic networks. For this reason, another concept known as egocentric has been introduced, which analyses the social environment surrounding individuals (through the ego-network). In other words, this type of network has the benefit of using only locally available knowledge of the topology to evaluate the importance of a node. It is worth emphasizing that in this approach, each network node will have a sub-optimal accuracy. However, such accuracy may be enough for a given purpose, for instance, the vehicle selection mechanism (VSM) that is applied to find, in a distributed fashion, the best-ranked vehicles in the network after each topology change. In order to confirm that egocentric measures can be a viable alternative for implementing a VSM, in particular, a case study was carried out to validate the effectiveness and viability of that mechanism for a distributed information management system. To this end, we used the egocentric betweenness measure as a selection mechanism of the most appropriate vehicle to carry out the tasks of information aggregation and knowledge generation. Based on the analysis of the performance results, it was confirmed that a VSM is extremely useful for VANET applications, and two major contributions of this mechanism can be highlighted: (i) reduction of bandwidth consumption; and (ii) overcoming the issue of highly dynamic topologies. Another contribution of this work is a thorough study by implementing and evaluating how well egocentric betweenness performs in comparison to the sociocentric measure in VANETs. Evaluation results show that the use of the egocentric betweenness measure in highly dynamic topologies has demonstrated a high degree of similarity compared to the sociocentric approach.