The forensic study of mobile apps that use application-level encryption requires the decryption of the data they generate. Such a decryption requires the knowledge of the encryption algorithm and key. Determining them requires, however, a quite complex analysis that is time-consuming, error prone, and often beyond the reach of many forensic examiners. In this paper, we tackle this problem by devising a framework able to automate the decryption of these data when third-party encryption libraries or platforms are used. Our framework is based on the use of dynamic instrumentation of app’s binary code by means of hooking, which enables it to export the plaintext of data after they have been decrypted by the app, as well as the corresponding encryption key and parameters. This framework has been conceived to be used only with test devices used for forensic study purposes, and not with devices that need to be forensically analyzed. We describe the architecture of the framework as well as the implementation of its components and of the hooks supporting three prominent and popular encryption libraries, namely SQLCipher, Realm and Jetpack Security. Also, we validate our framework by comparing its decryption results against those published in the literature for Wickr Me, Signal, Threema, and Element.

In medical process mining, specific domain characteristics have to be dealt with: in particular, in medicine, a significant amount of expert knowledge is typically available; moreover, an interactive approach, letting medical users be involved in the work of process model discovery, is more acceptable than a completely automated strategy. To this end, in our recent work we have defined SIM (Semantic Interactive Miner), an innovative process mining tool able to: (i) support the interaction with medical experts, who can progressively merge parts of the initially mined model, obtaining a more generalized version; (ii) exploit pre-encoded domain knowledge, to move from a model where activities are reported at the ground level to a more user-interpretable high-level version. In this paper we illustrate the features of our tool by showing its application to the case study of neonatal resuscitation simulation: we use SIM to mine the process models produced by two different groups of students of a simulation course, aiming at verifying whether differently skilled young professionals produce different processes, which can finally be compared to the correct guideline.

We propose a framework for structuring the description and results of the forensic analysis of actions of investigative interest in digital applications, and for automated reasoning on such actions. A high level of abstraction is suitable for forensic stakeholders that are not ICT experts; other levels are suitable for automating experiments on the devices to establish traces left by actions, and for associating the results of the experiments. Such results are used in a computational logic framework to conclude evidence on the occurrence of actions. The evidence can be presented to stakeholders or used in further automated reasoning, and traced back to data on the device.

Interoperability between different cloud platforms is a critical requirement for letting users to smoothly switch between different cloud providers and combine their services. However, the lack of standard interfaces to access these cloud platforms may result in the vendor lock-in situation, whereby users are locked into a specific cloud provider. In this paper, we present EasyCloud, a toolkit able to effectively support the creation and usage of Multi-cloud Systems (MSs) by providing interoperability, platform independence, effective resource provisioning, and ease of use. We describe its architecture and implementation, and experimentally assess the performance of EasyCloud, and compare it to existing alternative MS toolkits that are representative of the state-of-the-art. Our results clearly show that EasyCloud is highly scalable, quite efficient, and outperforms the other alternative toolkits.

Summary We consider fog computing scenarios where data generated by a set of IoT applications need to be processed locally by a set of fog nodes, belonging to distinct Fog Infrastructure Providers (FIPs) sharing the same co‐location facility, with the aim of increasing their profits. This is a challenging goal as it requires reducing costs and meeting QoS targets despite time‐varying workloads. We argue that these FIPs may find it profitable to cooperate by mutually sharing their workload and resources, and we show (by using a game‐theoretical framework) that this is indeed the case when stable coalitions can be formed. Based on these results, in this paper, we present (1) a mathematical model for maximizing the profit obtained for allocating IoT applications to a group of FIPs, and (2) a coalition formation algorithm that allows each FIP to decide with whom to cooperate so as to increment its profits. The efficacy of the devised algorithm is assessed by means of an experimental evaluation taking into account different workload intensities. The results from these experiments show the capability of the proposed algorithm to form coalitions of FIPs that are profitable and stable in all the scenarios we take into consideration.

Summary A typical problem that arises when devising a novel resource management strategy for virtualized infrastructures is how to experimentally assess its ability of achieving its design goals and whether it advances the state of the art. Among the available options, the use of a physical testbed is usually considered to be the most appropriate because of its high degree of accuracy. Unfortunately, however, physical testbeds are characterized by a limited controllability of the experimental conditions. Moreover, their implementation is usually a complex and time‐consuming task that requires the integration of many software components that need to interact among them in non‐trivial ways. In this paper, we address the aforementioned issues by proposing Prometheus, a toolkit specifically designed to support the configuration, deployment, and use of physical testbeds suitable to perform experimental studies of resource management strategies and that provides a high degree of controllability and low implementation costs. We discuss the design, implementation, and use of Prometheus, and we show how it can be used in practice to configure and deploy a physical testbed by providing various examples of experimental activities that can be carried out by means of it.

Summary Cloud providers (CPs) rely on server consolidation (the allocation of several virtual machines [VMs] on the same physical server) to minimize their costs. Maximizing the consolidation level is thus become 1 of the major goals of cloud providers. This is a challenging task because it requires the ability of estimating, in a resource contention scenario, multidimensional resource demands for multitier cloud applications that must meet service‐level agreements (SLAs) in face of nonstationary workloads. In this paper, we cope with the problem of jointly allocating CPU and memory capacity to (a) precisely estimate their capacity required by each VM to meet its SLAs and (b) coordinate their allocation to limit the negative effects due to the interactions of dynamic allocation mechanisms, which, if ignored, can lead to SLA violations. We tackle this problem by devising FCMS, a feedback fuzzy controller that is able to dynamically adjust the CPU and memory capacity allocated to each VM in a coordinated way, to precisely match the needs induced by the incoming workload. By means of an extensive experimental evaluation, we show that FCMS is able to achieve the above goals and works better than existing state‐of‐the‐art alternative solution in all the considered experimental scenarios.

Summary Modern cloud data centers rely on server consolidation (the allocation of several virtual machines on the same physical host) to minimize their costs. Choosing the right consolidation level (how many and which virtual machines are assigned to a physical server) is a challenging problem, because contemporary multitier cloud applications must meet service level agreements in face of highly dynamic, nonstationary, and bursty workloads. In this paper, we deal with the problem of achieving the best consolidation level that can be attained without violating application service level agreements. We tackle this problem by devising fuzzy controller for consolidation and QoS (FC2Q), a resource management framework exploiting feedback fuzzy logic control, that is able to dynamically adapt the physical CPU capacity allocated to the tiers of an application in order to precisely match the needs induced by the intensity of its current workload. We implement FC2Q on a real testbed and use this implementation to demonstrate its ability of meeting the aforementioned goals by means of a thorough experimental evaluation, carried out with real‐world cloud applications and workloads. Furthermore, we compare the performance achieved by FC2Q against those attained by existing state‐of‐the‐art alternative solutions, and we show that FC2Q works better than them in all the considered experimental scenarios. Copyright © 2014 John Wiley & Sons, Ltd.

Federations among sets of Cloud Providers (CPs), whereby a set of CPs agree to mutually use their own resources to run the VMs of other CPs, are considered a promising solution to the problem of reducing the energy cost. In this paper, we address the problem of federation formation for a set of CPs, whose solution is necessary to exploit the potential of cloud federations for the reduction of the energy bill. We devise an algorithm, based on cooperative game theory, that can be readily implemented in a distributed fashion, and that allows a set of CPs to cooperatively set up their federations in such a way that their individual profit is increased with respect to the case in which they work in isolation. We show that, by using our algorithm and the proposed CPs' utility function, they are able to self-organize into Nash-stable federations and, by means of iterated executions, to adapt themselves to environmental changes. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

Cloud computing is growing in popularity among computing paradigms for its appealing property of considering "Everything as a Service". The goal of a Cloud infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with service providers, and, at the same time, by lowering infrastructure costs. Among these costs, the energy consumption induced by the Cloud infrastructure, for running Cloud services, plays a primary role. Unfortunately, the minimization of QoS violations and, at the same time, the reduction of energy consumption is a conflicting and challenging problem. In this paper, we propose a framework to automatically manage computing resources of Cloud infrastructures in order to simultaneously achieve suitable QoS levels and to reduce as much as possible the amount of energy used for providing services. We show, through simulation, that our approach is able to dynamically adapt to time-varying workloads (without any prior knowledge) and to significantly reduce QoS violations and energy consumption with respect to traditional static approaches.

ShareGrid is a peer-to-peer desktop grid aimed at satisfying the computing needs of the small research laboratories located in the Piedmont area in Northern Italy. Share- Grid adopts a cooperative approach, in which each participant allows the other ones to use his/her own resources on a reciprocity basis. ShareGrid is based on the OurGrid middleware, that provides a set of mechanisms enabling participating entities to quickly, fairly, and securely share their resources. In this paper we report our experience in designing, deploying, and using ShareGrid, and we describe the applications using it, as well as the lessons we learned, the problems that still remain open, and some possible solutions to them.