Showing papers in "Concurrency and Computation: Practice and Experience in 2017"

Wound intensity correction and segmentation with convolutional neural networks

Abstract: Summary Wound area changes over multiple weeks are highly predictive of the wound healing process. A big data eHealth system would be very helpful in evaluating these changes. We usually analyze images of the wound bed for diagnosing injury. Unfortunately, accurate measurements of wound region changes from images are difficult. Many factors affect the quality of images, such as intensity inhomogeneity and color distortion. To this end, we propose a fast level set model-based method for intensity inhomogeneity correction and a spectral properties-based color correction method to overcome these obstacles. State-of-the-art level set methods can segment objects well. However, such methods are time-consuming and inefficient. In contrast to conventional approaches, the proposed model integrates a new signed energy force function that can detect contours at weak or blurred edges efficiently. It ensures the smoothness of the level set function and reduces the computational complexity of re-initialization. To increase the speed of the algorithm further, we also include an additive operator-splitting algorithm in our fast level set model. In addition, we consider using a camera, lighting, and spectral properties to recover the actual color. Numerical synthetic and real-world images demonstrate the advantages of the proposed method over state-of-the-art methods. Experimental results also show that the proposed model is at least twice as fast as methods used widely. Copyright © 2016 John Wiley & Sons, Ltd.

A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments

Abstract: Summary Large-scale scientific problems are often modeled as workflows. The ever-growing data and compute requirements of these applications has led to extensive research on how to efficiently schedule and deploy them in distributed environments. The emergence of the latest distributed systems paradigm, cloud computing, brings with it tremendous opportunities to run scientific workflows at low costs without the need of owning any infrastructure. It provides a virtually infinite pool of resources that can be acquired, configured, and used as needed and are charged on a pay-per-use basis. However, along with these benefits come numerous challenges that need to be addressed to generate efficient schedules. This work identifies these challenges and studies existing algorithms from the perspective of the scheduling models they adopt as well as the resource and application model they consider. A detailed taxonomy that focuses on features particular to clouds is presented, and the surveyed algorithms are classified according to it. In this way, we aim to provide a comprehensive understanding of existing literature and aid researchers by providing an insight into future directions and open issues.

A survey on load balancing algorithms for virtual machines placement in cloud computing

Abstract: Summary The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure, and be charged on pay-per-use basis. However, cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements violations. So as to achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be nondeterministic polynomial time (NP)-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to hosts in infrastructure clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated, and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.

RIoTBench: An IoT benchmark for distributed stream processing systems

Abstract: The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage physical, environmental, and human systems in real time. The inherent closed-loop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed stream processing systems (DSPS) hosted in cloud data centers are becoming the vital engine for real-time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we propose RIoTBench, a real-time IoT benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as modular microbenchmarks. Further, we define four IoT application benchmarks composed from these tasks based on common patterns of data preprocessing, statistical summarization, and predictive analytics that are intrinsic to the closed-loop IoT decision-making life cycle. These are coupled with four stream workloads sourced from real IoT observations on smart cities and smart health, with peak streams rates that range from 500 to 10000messages/second from up to 3million sensors. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public cloud and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, by IoT practitioners to evaluate DSPS platforms, and even reused within IoT solutions.

Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing

Abstract: Summary The cloud infrastructures provide a suitable environment for the execution of large-scale scientific workflow application. However, it raises new challenges to efficiently allocate resources for the workflow application and also to meet the user's quality of service requirements. In this paper, we propose an adaptive penalty function for the strict constraints compared with other genetic algorithms. Moreover, the coevolution approach is used to adjust the crossover and mutation probability, which is able to accelerate the convergence and prevent the prematurity. We also compare our algorithm with baselines such as Random, particle swarm optimization, Heterogeneous Earliest Finish Time, and genetic algorithm in a WorkflowSim simulator on 4 representative scientific workflows. The results show that it performs better than the other state-of-the-art algorithms in the criterion of both the deadline-constraint meeting probability and the total execution cost.

Cloud incident handling and forensic‐by‐design: cloud storage as a case study

Abstract: Summary Information security incident handling strategies or models are important to ensure the security of organisations, particularly in cloud and big data environments. However, existing strategies or models may not adequate as cloud data are generally virtualised, geographically distributed and ephemeral, presenting both technical and jurisdictional challenges. We present an integrated cloud incident handling and forensic-by-design model. We then seek to validate the model using a set of controlled experiments on a cloud-related incident. Three popular cloud storage applications were deployed namely, Dropbox, Google Drive, and OneDrive. This study demonstrates the utility of the model for organisational cloud users to undertake incident investigations (e.g. collect and analyse residual data from cloud storage applications). Copyright © 2016 John Wiley & Sons, Ltd.

PAWN: a payload-based mutual authentication scheme for wireless sensor networks

Abstract: Summary Wireless sensor networks (WSNs) consist of resource-starving miniature sensor nodes deployed in a remote and hostile environment. These networks operate on small batteries for days, months, and even years depending on the requirements of monitored applications. The battery-powered operation and inaccessible human terrains make it practically infeasible to recharge the nodes unless some energy-scavenging techniques are used. These networks experience threats at various layers and, as such, are vulnerable to a wide range of attacks. The resource-constrained nature of sensor nodes, inaccessible human terrains, and error-prone communication links make it obligatory to design lightweight but robust and secured schemes for these networks. In view of these limitations, we aim to design an extremely lightweight payload-based mutual authentication scheme for a cluster-based hierarchical WSN. The proposed scheme, also known as payload-based mutual authentication for WSNs, operates in 2 steps. First, an optimal percentage of cluster heads is elected, authenticated, and allowed to communicate with neighboring nodes. Second, each cluster head, in a role of server, authenticates the nearby nodes for cluster formation. We validate our proposed scheme using various simulation metrics that outperform the existing schemes.

Dynamic Voltage and Frequency Scaling‐aware dynamic consolidation of virtual machines for energy efficient cloud data centers

Abstract: Summary Computational demand in data centers is increasing because of the growing popularity of Cloud applications. However, data centers are becoming unsustainable in terms of power consumption and growing energy costs so Cloud providers have to face the major challenge of placing them on a more scalable curve. Also, Cloud services are provided under strict Service Level Agreement conditions, so trade-offs between energy and performance have to be taken into account. Techniques as Dynamic Voltage and Frequency Scaling (DVFS) and consolidation are commonly used to reduce the energy consumption in data centers, although they are applied independently and their effects on Quality of Service are not always considered. Thus, understanding the relationship between power, DVFS, consolidation, and performance is crucial to enable energy-efficient management at the data center level. In this work, we propose a DVFS policy that reduces power consumption while preventing performance degradation, and a DVFS-aware consolidation policy that optimizes consumption, considering the DVFS configuration that would be necessary when mapping Virtual Machines to maintain Quality of Service. We have performed an extensive evaluation on the CloudSim toolkit using real Cloud traces and an accurate power model based on data gathered from real servers. Our results demonstrate that including DVFS awareness in workload management provides substantial energy savings of up to 41.62% for scenarios under dynamic workload conditions. These outcomes outperforms previous approaches, that do not consider integrated use of DVFS and consolidation strategies.

Forensic data acquisition from cloud-of-things devices: windows Smartphones as a case study

Abstract: Summary The continued amalgamation of cloud technologies into all aspects of our daily lives and the technologies we use (i.e. cloud-of-things) creates business opportunities, security and privacy risks, and investigative challenges (in the event of a cybersecurity incident). This study examines the extent to which data acquisition from Windows phone, a common cloud-of-thing device, is supported by three popular mobile forensics tools. The effect of device settings modification (i.e. enabling screen lock and device reset operations) and alternative acquisition processes (i.e. individual and combined acquisition) on the extraction results are also examined. Our results show that current mobile forensic tool support for Windows Phone 8 remains limited. The results also showed that logical acquisition support was more complete in comparison to physical acquisition support. In one example, the tool was able to complete a physical acquisition of a Nokia Lumia 625, but its deleted contacts and SMSs could not be recovered/extracted. In addition we found that separate acquisition is needed for device removable media to maximize acquisition results, particularly when trying to recover deleted data. Furthermore, enabling flight-mode and disabling location services are highly recommended to eliminate the potential for data alteration during the acquisition process. These results should provide practitioners with an overview of the current capability of mobile forensic tools and the challenges in successfully extracting evidence from the Windows phone platform. Copyright © 2016 John Wiley & Sons, Ltd.

Heterogeneous cloudlet deployment and user-cloudlet association toward cost effective fog computing

Abstract: Summary Both mobile computing and cloud computing have experienced rapid development in recent years. Although centralized cloud computing exhibits abundant resources for computation-intensive tasks, the unpredictable and unstable communication latency between the mobile users and the cloud makes it challenging to handle latency-sensitive mobile computing tasks. To address this issue, fog computing recently was proposed by pushing the cloud computing to the network edge closer to the users. To realize such vision, we can augment existing access points in wireless networks with cloudlet servers for hosting various mobile computing tasks. In this paper, we investigate how to deploy the servers in a cost-effective manner without violating the predetermined quality of service. In particular, we practically consider that the available cloudlet servers are heterogeneous, ie, with different cost and resource capacities. The problem is formulated into an integer linear programming form, and a low-complexity heuristic algorithm is invented to address it. Extensive simulation studies validate the efficiency of our algorithm by it performs much close to the optimal solution.

A generic parallel pattern interface for stream and data processing

Abstract: Summary Current parallel programming frameworks aid developers to a great extent in implementing applications that exploit parallel hardware resources. Nevertheless, developers require additional expertise to properly use and tune them to operate efficiently on specific parallel platforms. On the other hand, porting applications between different parallel programming models and platforms is not straightforward and demands considerable efforts and specific knowledge. Apart from that, the lack of high-level parallel pattern abstractions, in those frameworks, further increases the complexity in developing parallel applications. To pave the way in this direction, this paper proposes GRPPI, a generic and reusable parallel pattern interface for both stream processing and data-intensive C++ applications. GRPPI accommodates a layer between developers and existing parallel programming frameworks targeting multi-core processors, such as C++ threads, OpenMP and Intel TBB, and accelerators, as CUDA Thrust. Furthermore, thanks to its high-level C++ application programming interface and pattern composability features, GRPPI allows users to easily expose parallelism via standalone patterns or patterns compositions matching in sequential applications. We evaluate this interface using an image processing use case and demonstrate its benefits from the usability, flexibility, and performance points of view. Furthermore, we analyze the impact of using stream and data pattern compositions on CPUs, GPUs and heterogeneous configurations.

FPGA‐accelerated deep convolutional neural networks for high throughput and energy efficiency

Abstract: Summary Recent breakthroughs in the deep convolutional neural networks (CNNs) have led to great improvements in the accuracy of both vision and auditory systems. Characterized by their deep structures and large numbers of parameters, deep CNNs challenge the computational performance of today. Hardware specialization in the form of field-programmable gate array offers a promising path towards major leaps in computational performance while achieving high-energy efficiency. In this paper, we focus on accelerating deep CNNs using the Xilinx Zynq-zq7045 FPGA SoC. As most of the computational workload can be converted to matrix multiplications, we adopt a matrix multiplier-based accelerator architecture. Dedicated units are designed to eliminate the conversion overhead. We also design a customized memory system according to the memory access pattern of CNNs. To make the accelerator easily usable by application developers, our accelerator supports Caffe, which is a widely used software framework of deep CNN. Different CNN models can be adopted by our accelerator, with good performance portability. The experimental results show that for a typical application of CNN, image classification, an average throughout of 77.8 GFLOPS is achieved, while the energy efficiency is 4.7× better than an Nvidia K20 GPGPU. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd

Game theoretic approach on Real-time decision making for IoT-based traffic light control

Abstract: Summary Smart traffic light control at intersections is 1 of the major issues in Intelligent Transportation System. In this paper, on the basis of the new emerging technologies of Internet of Things, we introduce a new approach for smart traffic light control at intersection. In particular, we firstly propose a connected intersection system where every objects such as vehicles, sensors, and traffic lights will be connected and sharing information to one another. By this way, the controller is able to collect effectively and mobility traffic flow at intersection in real-time. Secondly, we propose the optimization algorithms for traffic lights by applying algorithmic game theory. Specially, 2 game models (which are Cournot Model and Stackelberg Model) are proposed to deal with difference scenarios of traffic flow. In this regard, based on the density of vehicles, controller will make real-time decisions for the time durations of traffic lights to optimize traffic flow. To evaluate our approach, we have used Netlogo simulator, an agent-based modeling environment for designing and implementing a simple working traffic. The simulation results shows that our approach achieves potential performance with various situations of traffic flow.

Reputation‐based credibility analysis of Twitter social network users

Abstract: Summary This paper addresses the problem of finding credible sources among Twitter social network users to detect and prevent various malicious activities, such as spreading false information on a potentially inflammatory topic, forging accounts for false identities, etc. Existing research works related to source credibility are graph-based, considering the relationships among users to predict the spread information; human-based, using human perspectives to determine reliable sources; or machine learning-based, relying on training classifiers to predict users' credibility. Very few of these approaches consider a user's sentimentality when analyzing his/her credibility as a source. In this paper, we propose a novel approach that combines analysis of the user's reputation on a given topic within the social network, as well as a measure of the user's sentiment to identify topically relevant and credible sources of information. In particular, we propose a new reputation metric that introduces several new features into the existing models. We evaluated the performance of the proposed metric in comparison with two machine learning techniques, determining that the accuracy of the proposed approach satisfies the stated purpose of identifying credible Twitter users. Copyright © 2016 John Wiley & Sons, Ltd.

Cooperative frameworks and network models for flying ad hoc networks: a survey

Abstract: Summary Integrated frameworks have extended the applications of networks beyond a simple data sharing unit. Simultaneously, operating networks can form a layered structure that can operate as homogeneous as well as dissociated units. Networks using unmanned aerial vehicles follow similar criteria in their operability. Unmanned aerial vehicles can act as single searching unit controlled by human or can form an aerial swarm that can fly autonomously with the capability of forming an aerial network. Such aerial swarms are categorized as aerial ad hoc networks. Cooperation amongst different networks can be realized using various frameworks, models, architectures and middlewares. Several solutions have been developed that can provide easy network deployment of aerial nodes. However, a combined literature is not present that provides a comparison between these approaches. Keeping this in view, various cooperative approaches for similar formation using aerial vehicles have been discussed in this paper. The detailed study and comparative analysis of these approaches have been included. Further, the paper also includes various software solutions and their comparisons based on common parameters. Finally, various open issues have been discussed that can provide insight of ongoing research and problems that are yet to be resolved in these networks. Copyright © 2016 John Wiley & Sons, Ltd.

Mobile crowdsourcing: framework, challenges, and solutions

Abstract: Summary Crowdsourcing is the generalized act of outsourcing tasks, traditionally performed by an employee or contractor, to a large group of Internet population through an open call. With the great development of smartphones with rich built-in sensors and multiple ratio interfaces, mixing smartphone-based mobile technologies and crowdsourcing offers significant flexibilities and leads to a new paradigm called mobile crowdsourcing (MCS), which can be fully explored for real-time and location-sensitive crowdsourced tasks. In this paper, we present a taxonomy for the MCS applications, which are explicitly divided as using human as sensors, and exploiting the wisdom of crowd (i.e., human intelligence). Moreover, two paradigms for mobilizing users in MCS are outlined: direct mode and word of mouth mode. A comprehensive MCS framework and typical workflow of MCS applications are proposed, which consist of nine functional modules, pertaining to three stakeholders in MCS: crowdsourcer, crowdworkers, and crowdsourcing platform. Then, we elaborate the MCS challenges including task management, incentives, security and privacy, and quality control, and summarize the corresponding solutions. Especially, from the viewpoints of various stakeholders, we propose the desired properties that an ideal MCS system should satisfy. The primary goal of this paper is to comprehensively classify and provide a summary on MCS framework, challenges, and possible solutions to highlight the MCS related research topics and facilitate to develop and deploy interesting MCS applications. Copyright © 2016 John Wiley & Sons, Ltd.

Fast synchronization‐free algorithms for parallel sparse triangular solves with multiple right‐hand sides

Abstract: Summary The sparse triangular solve kernels, SpTRSV and SpTRSM, are important building blocks for a number of numerical linear algebra routines. Parallelizing SpTRSV and SpTRSM on today's manycore platforms, such as GPUs, is not an easy task since computing a component of the solution may depend on previously computed components, enforcing a degree of sequential processing. As a consequence, most existing work introduces a preprocessing stage to partition the components into a group of level-sets or colour-sets so that components within a set are independent and can be processed simultaneously during the subsequent solution stage. However, this class of methods requires a long preprocessing time as well as significant runtime synchronization overheads between the sets. To address this, we propose in this paper novel approaches for SpTRSV and SpTRSM in which the ordering between components is naturally enforced within the solution stage. In this way, the cost for preprocessing can be greatly reduced, and the synchronizations between sets are completely eliminated. To further exploit the data-parallelism, we also develop an adaptive scheme for efficiently processing multiple right-hand sides in SpTRSM. A comparison with a state-of-the-art library supplied by the GPU vendor, using 20 sparse matrices on the latest GPU device, shows that the proposed approach obtains an average speedup of over two for SpTRSV and up to an order of magnitude speedup for SpTRSM. In addition, our method is up to two orders of magnitude faster for the preprocessing stage than existing SpTRSV and SpTRSM methods.

Optimal scheduling workflows in cloud computing environment using Pareto-based Grey Wolf Optimizer

Abstract: Summary A workflow consists of dependent tasks, and scheduling of a workflow in a cloud environment means the arrangement of tasks of the workflow on virtual machines (VMs) of the cloud. By increasing VMs and the diversity of task size, we have a huge number of such arrangements. Finding an arrangement with minimum completion time among all of the arrangements is an Non-Polynomial-hard problem. Moreover, the problem becomes more complex when a scheduling should consider a couple of conflicting objectives. Therefore, the heuristic algorithms have been paid attention to figure out an optimal scheduling. This means that although the single-objective optimization, ie, minimizing completion time, proposes the workflow scheduling as an NP-complete problem, multiobjective optimization for the scheduling problem is confronted with a more permutation space because an optimal trade-off between the conflicting objectives is needed. To this end, we extended a recent heuristic algorithm called Grey Wolf Optimizer (GWO) and considered dependency graph of workflow tasks. Our experiment was carried out using the WorkflowSim simulator, and the results were compared with those of 2 other heuristic task scheduling algorithms.

Scheduling trade‐off of dynamic multiple parallel workflows on heterogeneous distributed computing systems

Abstract: Scheduling multiple parallel workflows, which arrive at different instants on heterogeneous distributed computing systems, is a great challenge because of the different requirements of resource providers and users. Overall scheduling length is the main concern of resource providers, whereas deadlines of workflows are the major requirements of users. Most algorithms use fairness‐based strategies to reduce the overall scheduling length. However, these algorithms cause obvious unfairness to longer‐makespan workflows or shorter‐makespan workflows. Furthermore, the systems cannot meet the deadlines of all workflows, particularly on large‐scale resource‐constrained computational grids. Gaining a reasonable balance between the overall scheduling length and the deadlines of workflows is a desirable goal. In this study, we first propose a fairness‐based scheduling algorithm called fairness‐based dynamic multiple heterogeneous selection value to achieve high performance of systems compared with existing works. Then, to meet the deadlines of partial higher‐priority workflows, we present a priority‐based scheduling algorithm called priority‐based dynamic multiple heterogeneous selection value. Finally, combining fairness‐based dynamic multiple heterogeneous selection value and priority‐based dynamic multiple heterogeneous selection value, we present the tradeoff‐based scheduling algorithm to meet the deadlines of more higher‐priority workflows while still allowing the lower‐priority workflows to be processed actively for better performance of systems. Both example and extensive experimental evaluations demonstrate significant improvement of our proposed algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

Semantic keyword searchable proxy re-encryption for postquantum secure cloud storage

Abstract: Summary With the advent of cloud computing, more and more consumers prefer to use the cloud services with the pay-as-you-consume mode. The cloud storage brings about great convenience to users, who store data in cloud and access to it using the smart devices anytime and anywhere. Consumers' information should be encrypted to guarantee the data privacy. Flexible searching on ciphertext is a critical challenge to be solved for effective data utilization. In this paper, we propose a novel semantic keyword searchable proxy re-encryption scheme for secure cloud storage. A highlight of this work is that the scheme is quantum attack resistant, while most of the available searchable encryption schemes are not. It supports not only exact keyword search but also synonym keyword search. Moreover, the data owner is capable to delegate his search right to another user using the proxy re-encryption mechanism. In the generation process of re-encryption key, the delegator and delegatee do not need to be interactive with each other. The scheme is also collusion resistant. Under the learning with errors hardness problem, this scheme is proved secure in standard model.

A list scheduling algorithm for heterogeneous systems based on a critical node cost table and pessimistic cost table

Abstract: Summary This paper presents a novel list-based scheduling algorithm called Improved Predict Earliest Finish Time for static task scheduling in a heterogeneous computing environment. The algorithm calculates the task priority with a pessimistic cost table, implements the feature prediction with a critical node cost table, and assigns the best processor for the node that has at least 1 immediate successor as the critical node, thereby effectively reducing the schedule makespan without increasing the algorithm time complexity. Experiments regarding aspects of randomly generated graphs and real-world application graphs are performed, and comparisons are made based on the scheduling length ratio, robustness, and frequency of the best result. The results demonstrate that the Improved Predict Earliest Finish Time algorithm outperforms the Predict Earliest Finish Time and Heterogeneous Earliest Finish Time algorithms in terms of the schedule length ratio, frequency of the best result, and robustness while maintaining the same time complexity.

On the design of a secure user authentication and key agreement scheme for wireless sensor networks

Abstract: Summary A wireless sensor network (WSN) typically consists of a large number of resource-constrained sensor nodes and several control or gateway nodes. Ensuring the security of the asymmetric nature of WSN is challenging, and designing secure and efficient user authentication and key agreement schemes for WSNs is an active research area. For example, in 2016, Farash et al. proposed a user authentication and key agreement scheme for WSNs. However, we reveal previously unpublished vulnerabilities in their scheme, which allow an attacker to carry out sensor node spoofing, password guessing, user/sensor node anonymity, and user impersonation attacks. We then present a scheme, which does not suffer from the identified vulnerabilities. To demonstrate the practicality of the scheme, we evaluate the scheme using NS-2 simulator. We then prove the scheme secure using Burrows–Abadi–Needham logic. Copyright © 2016 John Wiley & Sons, Ltd.

Astroturfing detection in social media: a binary n-gram–based approach

Abstract: Summary Astroturfing is appearing in numerous contexts in social media, with individuals posting product reviews or political commentary under a number of different names, and is of concern because of the intended deception. An astroturfer works with the aim of making it seem that a large number of people hold the same opinion, promoting a consensus based on the astroturfer's intentions. It is generally done for commercial or political advantage, often by paid writers or ideologically motivated writers. This paper brings the notion of authorship attribution to bear on the astroturfing problem, collecting quantities of data from public social media sites and analyzing the putative individual authors to see if they appear to be the same person. The analysis comprises a binary n-gram method, which was previously shown to be effective at accurately identifying authors on a training set from the same authors, while this paper shows how authors on different social media turn out to be the same author. The method has identified numerous instances where multiple accounts are apparently being operated by a single individual.

Accelerating Linux and Android applications on low-power devices through remote GPGPU offloading

Abstract: Summary Low-power devices are usually highly constrained in terms of CPU computing power, memory, and GPGPU resources for real-time applications to run. In this paper, we describe RAPID, a complete framework suite for computation offloading to help low-powered devices overcome these limitations. RAPID supports CPU and GPGPU computation offloading on Linux and Android devices. Moreover, the framework implements lightweight secure data transmission of the offloading operations. We present the architecture of the framework, showing the integration of the CPU and GPGPU offloading modules. We show by extensive experiments that the overhead introduced by the security layer is negligible. We present the first benchmark results showing that Java/Android GPGPU code offloading is possible. Finally, we show the adoption of the GPGPU offloading into BioSurveillance, a commercial real-time face recognition application. The results show that, thanks to RAPID, BioSurveillance is being successfully adapted to run on low-power devices. The proposed framework is highly modular and exposes a rich application programming interface to developers, making it highly versatile while hiding the complexity of the underlying networking layer.

Which is the best algorithm for virtual machine placement optimization

Abstract: Summary One of the key problems for Infrastructure-as-a-Service providers is finding the optimal allocation of virtual machines on the physical machines available in the provider's data center. Since the allocation has significant impact on operational costs as well as on the performance of the accommodated applications, several algorithms have been proposed for the virtual machine placement problem. So far, no objective comparison of the proposed algorithms has been provided; therefore, it is not known which one works best or what factors influence the performance of the algorithms. In this paper, we present an environment and methodology for such comparisons and compare 7 different algorithms using the proposed environment and methodology. Our results showcase differences of up to 66% between the effectiveness of different algorithms on the same real-world workload traces, thus underlining the importance of objectively comparing the performance of competing algorithms.

On the effectiveness of isolation‐based anomaly detection in cloud data centers

Abstract: Summary The high volume of monitoring information generated by large-scale cloud infrastructures poses a challenge to the capacity of cloud providers in detecting anomalies in the infrastructure. Traditional anomaly detection methods are resource-intensive and computationally complex for training and/or detection, what is undesirable in very dynamic and large-scale environment such as clouds. Isolation-based methods have the advantage of low complexity for training and detection and are optimized for detecting failures. In this work, we explore the feasibility of Isolation Forest, an isolation-based anomaly detection method, to detect anomalies in large-scale cloud data centers. We propose a method to code time-series information as extra attributes that enable temporal anomaly detection and establish its feasibility to adapt to seasonality and trends in the time-series and to be applied online and in real-time.

Security and privacy in social networks

Abstract: This special issue collates a selection of representative research articles that were primarily presented at the 9th International Conference on Network and System Security. This annual conference brings together researchers and practitioners from both academia and industry who are working on security and privacy in computer systems and social networks, in order to promote an exchange of ideas, discuss future collaborations, and develop new research directions. Online social networks have pervaded all aspects of our daily lives. With their unparalleled popularity, online social networks have evolved from platforms for social communication and news dissemination, to indispensable tools for professional networking, social recommendations, marketing, and online content distribution. Because of their scale, complexity, and heterogeneity, many technical and social challenges in online social networks must be addressed. It has been widely recognized that security and privacy are the critical issues in online social networks. This special issue presents many examples of how researchers, scholars, vendors, and practitioners are collaborating to address security and privacy research challenges. The scope of this special issue is broad and is representative of the multi-disciplinary nature of privacy and security. In addition to submissions that deal with malicious attacks, information control and detection, privacy protection, network data analytics for security and privacy, trust and reputation in social networks, this issue also includes articles that address practical challenges with privacy-preserving data publishing and efficient data encryption schemes. Protecting the security and privacy of user data in the context of social networks is a central topic of this issue. Xiaofen Wang et al. [3] propose a new privacy-preserving data search and sharing protocol for social networks. The protocol leverages an ID-based multi-user searchable encryption scheme to achieve data search pattern privacy-preserving, anonymity, and request unlinkability. Majed Alrubian et al. [4] describe a novel approach for finding credible sources among Twitter social network users to detect and prevent various malicious activities. They combine analysis of the user’s reputation on a given topic, as well as a measure of the user’s sentiment to identify topically relevant and credible sources of information. Shuhong Chen et al. [9] propose a new multi-dimensional fuzzy trust evaluation method for mobile social networks. They construct implicit social behavioral graphs based on dynamic complex community structures to infer trust relations between users. Zechao Liu et al. [6] propose a new offline and online attribute-based encryption scheme with verifiable outsourced decryption. Using the proposed scheme, the majority of the computational workload in decryption can be outsourced to third parties. Chunyong Yin et al. [8] propose an improved anonymity model for big data security based on clustering algorithm. The model integrates K-anonymity with L-diversity and addresses the problem of imbalanced sensitive attribute distribution. The security and privacy issues under emerging scenarios, such as mobile and cloud computing, Internet of Things, etc., are interesting topics of this issue. Shasi Pokharel et al. [1] describe a new attacking method for codec identification and decoding of captured communications from 15 popular Android VoIP apps. Using this method, the authors can recover the original voice conversations from intercepted calls. Bowei Yang et al. [2] propose a Quality of Service-aware indiscriminate volume storage cloud scheme over dynamic networks. The scheme employs a data redundancy policy based on indiscriminate recovery volumes and Quality of Service-aware data replacement strategy. Keke Gai et al. [7] present a secure cyber incident analytics framework using Monte Carlo Simulations for financial cyber-security insurance in cloud computing. Jiageng Chen et al. [5] propose a novel variable message encryption scheme for constrained devices in Internet of Things. The authors present two different block cipher compression functions to satisfy efficiency and upper security bound, respectively.

Cloud computing in a distributed e‐infrastructure using the web processing service standard

Abstract: New Science paradigms have recently evolved to promote open publication of scientific findings as well as multi‐disciplinary collaborative approaches to scientific experimentation. These approaches can face modern scientific challenges but must deal with large quantities of data produced by industrial and scientific experiments. These data, so‐called Big Data, require to introduce new computer science systems to help scientists cooperate, extract information, and possibly produce new knowledge out of the data. E‐infrastructures are distributed computer systems that foster collaboration between users and can embed distributed and parallel processing systems to manage big data. However, in order to meet modern Science requirements, e‐Infrastructures impose several requirements to computational systems in turn, eg, being economically sustainable, managing community‐provided processes, using standard representations for processes and data, managing big data size and heterogeneous representations, supporting reproducible Science, collaborative experimentation, and cooperative online environments, managing security and privacy for data and services. In this paper, we present a cloud computing system (gCube DataMiner) that meets these requirements and operates in an e‐Infrastructure, while sharing characteristics with state‐of‐the‐art cloud computing systems. To this aim, DataMiner uses the web processing service standard of the open geospatial consortium and introduces features like collaborative experimental spaces, automatic installation of processes and services on top of a flexible and sustainable cloud computing architecture. We compare DataMiner with another mature cloud computing system and highlight the benefits our system brings, the new paradigms requirements it satisfies, and the applications that can be developed based on this system.

A Survey of Techniques for Architecting TLBs

Abstract: Summary Translation lookaside buffer (TLB) caches virtual to physical address translation information and is used in systems ranging from embedded devices to high-end servers. Because TLB is accessed very frequently and a TLB miss is extremely costly, prudent management of TLB is important for improving performance and energy efficiency of processors. In this paper, we present a survey of techniques for architecting and managing TLBs. We characterize the techniques across several dimensions to highlight their similarities and distinctions. We believe that this paper will be useful for chip designers, computer architects, and system engineers.

Online virtual machine migration for renewable energy usage maximization in geographically distributed cloud data centers

Abstract: Summary Energy consumption and its associated costs represent a huge part of cloud providers' operational costs In this study, we explore how much energy cost savings can be made knowing the future level of renewable energy (solar/wind) available in data centers Since renewable energy sources have intermittent nature, we take advantage of migrating virtual machines to the nearby data centers with excess renewable energy In particular, we first devise an optimal offline algorithm with full future knowledge of renewable level in the system Since in practice, accessing long-term and exact future knowledge of renewable energy level is not feasible, we propose two online deterministic algorithms, one with no future knowledge called deterministic and one with limited knowledge of the future renewable availability called future-aware We show that the deterministic and future-aware algorithms are 1+1/s and 1+1/s−ω/sTm competitive in comparison to the optimal offline algorithm, respectively, where s is the network to the brown energy cost, ω is the look-ahead window-size, and Tm is the migration time The effectiveness of the proposed algorithms is analyzed through extensive simulation studies using real-world traces of meteorological data and Google cluster workload