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.

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.

The widespread diffusion of distributed and cloud storage solutions has changed dramatically the way users, system designers, and service providers manage their data. Outsourcing data on remote storage provides indeed many advantages in terms of both capital and operational costs. The security of data outsourced to the cloud, however, still represents one of the major concerns for all stakeholders. Pollution attacks, whereby a set of malicious entities attempt to corrupt stored data, are one of the many risks that affect cloud data security. In this paper we deal with pollution attacks in coding-based block-level cloud storage systems, i.e., systems that use linear codes to fragment, encode, and disperse virtual disk sectors across a set of storage nodes to achieve desired levels of redundancy, and to improve reliability and availability without sacrificing performance. Unfortunately, the effects of a pollution attack on linear coding can be disastrous, since a single polluted fragment can propagate pervasively in the decoding phase, thus hampering the whole sector. In this work we show that, using rateless codes, we can design an early pollution detection algorithm able to spot the presence of an attack while fetching the data from cloud storage during the normal disk reading operations. The alarm triggers a procedure that locates the polluting nodes using the proposed detection mechanism along with statistical inference. The performance of the proposed solution is analyzed under several aspects using both analytical modelling and accurate simulation using real disk traces. Our results show that the proposed approach is very robust and is able to effectively isolate the polluters, even in harsh conditions, provided that enough data redundancy is used.

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.

Block-level cloud storage (BLCS) offers to users and applications the access to persistent block storage devices (virtual disks) that can be directly accessed and used as if they were raw physical disks. In this paper we devise ENIGMA, an architecture for the back-end of BLCS systems able to provide adequate levels of access and transfer performance, availability, integrity, and confidentiality, for the data it stores. ENIGMA exploits LT rateless codes to store fragments of sectors on storage nodes organized in clusters. We quantitatively evaluate how the various ENIGMA system parameters affect the performance, availability, integrity, and confidentiality of virtual disks. These evaluations are carried out by using both analytical modeling (for availability, integrity, and confidentiality) and discrete event simulation (for performance), and by considering a set of realistic operational scenarios. Our results indicate that it is possible to simultaneously achieve all the objectives set forth for BLCS systems by using ENIGMA, and that a careful choice of the various system parameters is crucial to achieve a good compromise among them. Moreover, they also show that LT coding-based BLCS systems outperform traditional BLCS systems in all the aspects mentioned before.

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.

Desktop grids are being increasingly used as the execution platform for a variety of applications that can be structured as bag-of-tasks (BoT). Scheduling BoT applications on desktop grids has thus attracted the attention of the scientific community, and various schedulers tailored towards them have been proposed in the literature. However, previous work has focused on scheduling a single BoT application at a time, thus neglecting other scenarios in which several users submit multiple BoT applications at the same time. This paper aims at filling this gap by proposing a set of scheduling algorithms able to deal with multiple BoT applications. The performance of these algorithm has been evaluated, by means of simulation, for a large set of operational scenarios obtained by varying both the workload submitted to the desktop grid and the characteristics of the involved resources. Our results show that, although there is no a clear winner among the proposed solutions, knowledge-free strategies (that is, strategies that do not require any information concerning the applications or the resources) can provide good performance.

Abstract Very large size peer‐to‐peer systems are often required to implement efficient and scalable services, but usually they can be built only by assembling resources contributed by many independent users. Among the guarantees that must be provided to convince these users to join the P2P system, particularly important is the ability of ensuring that P2P applications and services run on their nodes will not unacceptably degrade the performance of their own applications because of an excessive resource consumption. In this paper we present the Interceptor, a middleware‐level application segregation and scheduling system, which is able to strictly enforce quantitative limitations on node resource usage and, at the same time, to make P2P applications achieve satisfactory performance even in face of these limitations. A proof‐of‐concept implementation has been carried out for the Linux operating system, and has been used to perform an extensive experimentation aimed at quantitatively evaluating the Interceptor. The results we obtained clearly demonstrate that the Interceptor is able to strictly enforce quantitative limitations on node resource usage, and at the same time to effectively schedule P2P applications. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract The exploration in many scientific disciplines (e.g. High‐Energy Physics, Climate Modeling, and Life Sciences) involves the production and the analysis of massive data collections, whose archival, retrieval, and analysis require the coordinated usage of high‐capacity computing, network, and storage resources. To obtain satisfactory performance, these applications require the availability of a high‐performance, reliable data transfer mechanisms, able to minimize the data transfer time that often dominates their execution time. In this paper we present the File Mover, an efficient data transfer system specifically tailored to the needs of data‐intensive applications, that exploits the overlay networks paradigm to provide superior performance with respect to conventional file transfer systems. An extensive experimental evaluation, carried out by means of a proof‐of‐concept implementation of the File Mover for a variety of network scenarios, shows the ability of the File Mover to outperform alternative data transfer systems. Copyright © 2007 John Wiley & Sons, Ltd.

Very large size peer-to-peer systems are often required to implement efficient and scalable services, but usually they can be built only by assembling resources contributed by many independent users. Among the guarantees that must be provided to convince these users to join the P2P system, particularly important is the ability of ensuring that P2P applications and services run on their nodes will not unacceptably degrade the performance of their own applications because of an excessive resource consumption. In this paper we present Interceptor, a middleware-level application segregation and scheduling system that is able to strictly enforce quantitative limitations on node resource usage and, at same time, to make P2P applications achieve satisfactory performance even in face of these limitations.

Abstract Data‐intensive Grid applications require the availability of tools able to transfer very large files in the shortest amount of time. Many file‐transfer tools, based on solutions aimed at overcoming the limitations imposed by the TCP protocol, have recently been developed. In this paper we experimentally compare the performance of some of these tools in various network scenarios by running experiments on PlanetLab, an open platform for the development, deployment, and access of planetary‐scale services, that comprises hundreds of hosts scattered across the globe. Our results show that solutions based on UDP and adopting rate‐based algorithms result in better performance than other alternatives in most cases, while solutions based on TCP achieve similar performance only under specific circumstances. Copyright © 2005 John Wiley & Sons, Ltd.

In this paper we present the File Mover, a data transfer system designed to optimize the transfer of potentially very large files. The File Mover relies on an overlay network architecture, where a set of machines cooperate in the transfer by forwarding among them portions of the files being transferred. Data transfer times are minimized by choosing, for each transfer the set of relays that maximize the expected throughput. Preliminary experiments show that the File Mover is able to profitably exploit existing network paths not chosen by IP routing algorithms, thereby enhancing file transfer performance.

The automatic construction of classifiers (programs able to correctly classify data collected from the real world) is one of the major problems in pattern recognition and in a wide area related to artificial intelligence, including data mining. We present G-Net, a distributed evolutionary algorithm able to infer classifiers from precollected data. The main features of the system include robustness with respect to parameter settings, use of the minimum description length criterion coupled with a stochastic search bias, coevolution as a high-level control strategy, ability to face problems requiring structured representation languages, and suitability to parallel implementation on a network of workstations (NOW). Its parallel version, NOW G-Net, also described in this paper, is able to profitably exploit the computing power delivered by these platforms by incorporating a set of dynamic load distribution techniques that allow it to adapt to the variations of computing power arising typically in these systems. A proof-of-concept implementation is used in this paper to demonstrate the effectiveness of NOW G-Net on a variety of datasets.

In recent years workstation clusters have been increasingly used as general purpose computing servers for the execution of parallel and sequential applications submitted by many competing users. To make clusters a real alternative to more traditional general purpose computing platforms, scheduling techniques able to efficiently and fairly schedule collections of parallel and sequential applications must be devised. In this paper we propose a scheduling technique able to achieve the above goals by combining stride scheduling with a ticket redistribution policy that results in the spontaneous coscheduling of parallel applications. A simulation analysis carried out for a variety of workloads and operational conditions shows that our strategy outperforms previous strategies both in terms of efficiency and fairness.

Implicit coscheduling strategies enable parallel applications to dynamically share the machines in a network of workstations (NOW) with interactive, CPU and IO-bound sequential jobs. We present a simulation study that compares 12 coscheduling strategies in terms of their impact on the performance of parallel and sequential applications executed simultaneously on a NOW. Our results show that the coscheduling strategy has a strong impact on the performance of the applications (both parallel and sequential) composing the workload, and that no single strategy is able to effectively handle all workloads. In spite of that, our results can be used to identify the strategy that represents the best choice for a given application class, or the best compromise for various workloads. Moreover, we show that in many cases simple strategies outperform more complex ones.

We address the problem of providing transparent, reliable, and efficient network-level multicasting in wormhole LANs. We describe some alternatives for achieving deadlock-free multicasting using fast buffer reservation techniques at the host interface level. Tradeoffs involving complexity and performance of various solutions are discussed, and are illustrated using simulation results. Our results show that in most cases the straightforward approach of sending multiple unicast copies of the multicast message at the originator host turns out to be the most simple and effective approach.

We address the problem of performance prediction for parallel programs execut,ed on clusters of heterogeneous

Self-managing systems able to dynamically re-configure with respect to time-varying workload mixes and changing system resource availability are of growing importance in heterogeneous multicomputer environments. Advanced performance evaluation techniques are needed to induce and assess the impact of such re-configurations where guaranteeing timeliness of reconfiguration activities is particularly challenging. A whole new class of methods supporting "pro-active" adaptivity based on the predicted system state at the re-configuration instant are needed to alleviate the shortcomings of "re-active" systems which bring reconfigurations in effect after the system state has changed. In this paper we argue for self-contained performance management of distributed applications, i.e. integrated performance tuning with the ability to automatically adapt the application behavior to the performance characteristics of the execution environment. Particularly, we study pro-active performance management for distributed simulation based on the Time Warp protocol executing on a network of workstations (NOWs).