Table 1 - uploaded by Tobias Hossfeld
Content may be subject to copyright.
Context in source publication
Similar publications
The description of the Parallel Low-order PC (PLPC) algorithm. The formal pseudo codes of PLPC and simulation tests.
(PDF)
File S1 contains supporting information
Tables 1
–
4
and
Figures 1
–8.
(PDF)
![Figure S7. ESI (+) MS and MS/MS spectra on m/z 358 [M + Na] + of...](profile/Mouloud-Kecha/publication/294067179/figure/fig4/AS:355110438883328@1461676334210/Figure-S7-ESI-MS-and-MS-MS-spectra-on-m-z-358-M-Na-of-deuterated-13-from-a-CD_Q320.jpg)
![Figure S8. ESI (−) MS and MS/MS spectra on m/z 332 [M − H] − of...](profile/Mouloud-Kecha/publication/294067179/figure/fig5/AS:355110438883329@1461676334580/Figure-S8-ESI-MS-and-MS-MS-spectra-on-m-z-332-M-H-of-deuterated-13-from-a-CD-3_Q320.jpg)
PDF-Document (PDF, 1078 KB)
في عدد فبراير 2022.. ميريت الثقافية تناقش العلاقة الجدلية بين الفلسفة والأدب
---------
صدر العدد رقم (38)- فبراير 2022، من مجلة "ميريت الثقافية"؛ مجلة كل التنويريين العرب، الشهرية الإلكترونية التي تصدر عن دار ميريت للنشر بصيغة pdf، المدير العام الناشر محمد هاشم، ورئيس التحرير الشاعر سمير درويش.
= علاقة الأدب بالفلسفة =
تصدَّر العدد "الملف الثقافي"...
Citations
... PCSSs bring us a great deal of benefit (Vartiainen and Väänänen 2010;Marston et al. 2011;Marshall and Tang 2012;Amrehn et al. 2013;Shin 2015). One of the major features of cloud services is their accessibility at any time and location. ...
... There are a variety of PCSSs (such as Dropbox, Google Drive, OneDrive, Mega, iCloud, SkyDrive, etc.) with different free and non-free capacities that users can utilise depending on their needs and budget. According to the existing reports, Dropbox with more than 100 million users is the most popular PCSS (Amrehn et al. 2013). It is a cloud service which enables users to access and update their files from different devices. ...
... Investigating the adoption of cloud services is one of the most significant issues in the field of cloud computing (Marston et al. 2011;Lin and Chen 2012;Lee, Chae, and Cho 2013). This issue is not only influenced by pure technical aspects, but also by human and social factors (Amrehn et al. 2013;Casas et al. 2013;Casas and Schatz 2014). Exploring the users' experiences and perceptions of using PCSSs will yield worthwhile insights towards the essence of cloud services' adoption. ...
Recently, cloud computing has attracted great attention for both personal and organisational usages. Despite a large number of research conducted about cloud adoption, little is known about how users practically experienced the usage and adoption of Personal Could Storage Service (PCSSs) particularly in the context of developing countries. To address this gap, this paper seeks a clear understanding of how participants have profoundly experienced and perceived PCSSs to obtain worthwhile insights towards the essence of PCSSs’ adoption as a multifaceted phenomenon. Applying the interpretive phenomenology as a research method, required data were collected by conducting semi-structured interviews with 12 experienced participants. The results reveal that users’ intentions to adopt PCSSs are highly influenced by three emerged themes, including demand-side factors, supply-side factors, and institutional factors. Concerning the demand-side factors, the paper demonstrates how user-centric aspects work in favour of PCSSs’ adoption. As to the supply-side factors, this research promotes an understanding of how the participants have perceived tangible and non-tangible features of PCSSs as a determinant of the adoption. Regarding the formal and informal institutions, remarkable roles of regulatory environments and culture are highlighted, respectively. The theoretical and practical implications of this study are discussed for both academia and practitioners.
... In [36], the authors focus on ve fundamental challenges for wide adoption of cloud computing using the OPTIMIS toolkit. Amrehn et al. conclude that for general le storage services, the upload and download speed, nancial aspects, privacy, and security are important QoE in uence factors [37]. The authors in [38] use a prediction system to forecast the CPU demands for web based cloud services. ...
In this thesis various aspects of Quality of Experience (QoE) research are examined. The work is divided into three major blocks: QoE Assessment, QoE Monitoring, and VNF Performance Evaluation. First, prominent cloud applications such as Google Docs and a cloud-based photo album are explored. The QoE is characterized and the influence of packet loss and delay is studied. Afterwards, objective QoE monitoring for HTTP Adaptive Video Streaming (HAS) in the cloud is investigated. Additionally, by using a Virtual Network Function (VNF) for QoE monitoring in the cloud, the feasibility of an interworking of Network Function Virtualization (NFV) and cloud paradigm is evaluated. To this end, a VNF that exploits deep packet inspection technique was used to parse the video traffic. An algorithm is then designed accordingly to estimate video quality and QoE based on network and application layer parameters. To assess the accuracy of the estimation, the VNF is measured in different scenarios under different network QoS and the virtual environment of the cloud architecture. The insights show that the different geographical deployments of the VNF influence the accuracy of the video quality and QoE estimation. Various Service Function Chain (SFC) placement algorithms have been proposed and compared in the context of edge cloud networks. On the one hand, this research is aimed at cloud service providers by providing methods for evaluating QoE for cloud applications. On the other hand, network operators can learn the pitfalls and disadvantages of using the NFV paradigm for such a QoE monitoring mechanism.
... The same is also possible using test beds; however, no real end-user interactions are available. One example for gathering application information using CNM and voluntary participation is given in [23]. In this study we implement a Dropbox application using the service's ocial API to gather meta information about the participants' account, e.g., the available and used Dropbox space. ...
Computer systems have replaced human work-force in many parts of everyday life, but there still exists a large number of tasks that cannot be automated, yet. This also includes tasks, which we consider to be rather simple like the categorization of image content or subjective ratings. Traditionally, these tasks have been completed by designated employees or outsourced to specialized companies. However, recently the crowdsourcing paradigm is more and more applied to complete such human-labor intensive tasks. Crowdsourcing aims at leveraging the huge number of Internet users all around the globe, which form a potentially highly available, low-cost, and easy accessible work-force. To enable the distribution of work on a global scale, new web-based services emerged, so called crowdsourcing platforms, that act as mediator between employers posting tasks and workers completing tasks. However, the crowdsourcing approach, especially the large anonymous worker crowd, results in two types of challenges. On the one hand, there are technical challenges like the dimensioning of crowdsourcing platform infrastructure or the interconnection of crowdsourcing platforms and machine clouds to build hybrid services. On the other hand, there are conceptual challenges like identifying reliable workers or migrating traditional off-line work to the crowdsourcing environment. To tackle these challenges, this monograph analyzes and models current crowdsourcing systems to optimize crowdsourcing workflows and the underlying infrastructure. First, a categorization of crowdsourcing tasks and platforms is developed to derive generalizable properties. Based on this categorization and an exemplary analysis of a commercial crowdsourcing platform, models for different aspects of crowdsourcing platforms and crowdsourcing mechanisms are developed. A special focus is put on quality assurance mechanisms for crowdsourcing tasks, where the models are used to assess the suitability and costs of existing approaches for different types of tasks. Further, a novel quality assurance mechanism solely based on user-interactions is proposed and its feasibility is shown. The findings from the analysis of existing platforms, the derived models, and the developed quality assurance mechanisms are finally used to derive best practices for two crowdsourcing use-cases, crowdsourcing-based network measurements and crowdsourcing-based subjective user studies. These two exemplary use-cases cover aspects typical for a large range of crowdsourcing tasks and illustrated the potential benefits, but also resulting challenges when using crowdsourcing. With the ongoing digitalization and globalization of the labor markets, the crowdsourcing paradigm is expected to gain even more importance in the next years. This is already evident in the currently new emerging fields of crowdsourcing, like enterprise crowdsourcing or mobile crowdsourcing. The models developed in the monograph enable platform providers to optimize their current systems and employers to optimize their workflows to increase their commercial success. Moreover, the results help to improve the general understanding of crowdsourcing systems, a key for identifying necessary adaptions and future improvements.
... Indeed, we are aware of only a few recent efforts to analyze the characteristics of cloud storage services [1], focusing either on architectural design aspects [4,5], quality of experience [6,7], or benchmark-driven performance studies [3,8,9]. Yet, a characterization of the underlying client processes that generate workload to the system, notably the data transfer patterns that emerge from such processes, is only partly addressed by our preliminary efforts presented in [10,11]. ...
... Other works analyze cloud storage focusing on economics [26], security [14], quality of experience [6,7] or privacy [27], and thus are orthogonal to our work. ...
Cloud storage systems are currently very popular with many companies offering services, including worldwide providers such as Dropbox, Microsoft and Google. These companies as well as providers entering the market could greatly benefit from a deep understanding of typical workload patterns their services have to face in order to develop cost-effective solutions. Yet, despite recent studies of usage and performance of these systems, the underlying processes that generate workload to the system have not been deeply studied.
... The same is also possible using test beds; however, no real end user interactions are available. On example for gathering application information using CNM and voluntary participation is given in [45]. In this study, we analyzed the usage and the most important and annoying features of the service Dropbox as perceived by the participants. ...
Network measurements are of high importance both for the operation of networks and for the design and evaluation of new management mechanisms. Therefore, several approaches exist for running network measurements, ranging from analyzing live traffic traces from campus or Internet Service Provider (ISP) networks to performing active measurements on distributed testbeds, e.g., PlanetLab, or involving volunteers. However, each method falls short, offering only a partial view of the network. For instance, the scope of passive traffic traces is limited to an ISP’s network and customers’ habits, whereas active measurements might be biased by the population or node location involved. To complement these techniques, we propose to use (commercial) crowdsourcing platforms for network measurements. They permit a controllable, diverse and realistic view of the Internet and provide better control than do measurements with voluntary participants. In this study, we compare crowdsourcing with traditional measurement techniques, describe possible pitfalls and limitations, and present best practices to overcome these issues. The contribution of this paper is a guideline for researchers to understand when and how to exploit crowdsourcing for network measurements.
... A Qualidade de Experiência (QoE) de serviços de armazenamento foi estudada por [Amrehn et al. 2013, Casas andSchatz 2014]. Diversos aspectos técnicos como a largura de banda disponível e o tempo de sincronização são identificados como parâmetros importantes para a satisfação dos usuários. ...
Cloud storage services (e.g., Dropbox) are a popular means for sharing content and performing collaborative work. Yet, content sharing via cloud might result in bandwidth wastage when repetitive data is downloaded by different users in the same network domain. This paper first characterizes sharing patterns in Dropbox by analyzing data collected from a campus network for 4 months. We identify that the volume of data sharing in such homogeneous environment is reasonably high. Next, we use the characterization results to implement a synthetic workload generator that allows us to test alternatives for the Dropbox synchronization protocol. We then propose a synchronization architecture that includes network caches to temporarily hold user updates. Our evaluation of the proposed solution indicates that, even with a small cache, it is possible to achieve almost the maximum possible reduction of downloads from remote servers, thus benefiting storage providers, end users and the Internet.
... The authors of [5] focused on five fundamental challenges for wide adoption of cloud computing using the OPTIMIS toolkit. Amrehn et al. concluded that for general file storage services, the upload and download speed, financial aspects, privacy, and security are important QoE influence factors [6]. The authors of [7] used a prediction system to forecast the CPU demands for web based cloud services. ...
Software as a Service allows end users to use complex software directly from their browsers, transferring heavy computation to servers in the cloud. One use of this paradigm is word processing, former a classic use cases of Thin-Client computing. Similar to Thin-Clint systems, the network parameters are an important influence factor for the cloud application performance. In this paper, we study Google Docs as an example for online word processing tools. We consider a traditional single user scenario as well as a collaborative scenario with two users working on one document simultaneously. We identify multiple relevant sub-processes per scenario as performance metrics and use a testbed to automatically evaluate the performance of Google Docs under varying network parameters. The main contributions of the paper are (1) the quantification of the impact of network parameters such as delay and packet loss on application performance metrics for both scenarios and (2) linear regression models to derive the application performance for giving network parameters.
... • video and image quality in general, • QoE for HTTP streaming [31,32] and HTTP adaptive streaming [19,30], • selfie portrait images perception in a recruitment context [10], • privacy in HDR images and video [39,20,36], • compression of HDR images [37] [38], • evaluation of 3D video [38], • image recognizability and aesthetic appeal [12,13], • multidimensional modeling of web QoE [14], • QoE factors of cloud storage services [21], • enabling eye tracking experiments using web technologies [41]. ...
Lessons learned from the Qualinet Task Force "Crowdsourcing"
COST Action IC1003 European Network on Quality of Experience in Multimedia Systems and Services (QUALINET)
... The availability of high speed mobile Internet enables additional use cases. One such widely considered use case is the synchronization of images 3 The appearance of devices like Google Glass 4 and network or Bluetooth enabled cameras combine these use cases with the goal of enabling synchronization of images from a mobile user to a fixed network on demand. ...
... Here, the authors provide a model allowing for the evaluation of cloud service providers according to a variety of metrics, including bandwidth, latency, and response time. In [3] the authors study the main impact factors on QoE of Dropbox users. They find that the main impact factor is the waiting time for file synchronization. ...
Over the last few years, the importance of cloud services for file synchronization has been increasing. With the release of network enabled cameras like Google Glass the trend of synchronizing new photos with the cloud has emerged as an important new use case for mobile networks. Multiple stakeholder are interested in optimizing this synchronization process according to different orthogonal metrics. The end user is interested in a fast synchronization of images as well as a low energy consumption of the client used for upload to the network. The network operator requires a low number of network connections per synchronization of a batch of images in order to reduce the signalling load of the mobile network. This paper answers the question if the goals of the stakeholders can be achieved by selecting an appropriate synchronization scheduling mechanism. Therefore, we first present a model for the waiting time of cloud file synchronization services. Second, we perform measurements on the popular Dropbox service to obtain parameters required in the model. Finally, we perform a parameter study over the considered mechanisms and parameters, suggest a preferable algorithm and identify trade-offs favorable for all stakeholders.
Over the last decade, there has been an exponential growth of services over the Internet. Services such as online gaming are gaining popularity, whereas, Voice over Internet (VoIP) has been well established. Storage, management and processing of data have moved away from the devices to the cloud. Quality of experience (QoE) defined as the user’s perception of service quality is utilized in different technologies, services and products for improving end user satisfaction and provides quality of service (QoS) according to their demands. In this paper, we describe the utilization of QoE in different technologies, services and products for improving end user satisfaction. Specifically, we present the latest developments in QoE in multimedia services, network management, VoIP, web development, games and cloud computing. Finally, we discuss the current research, solutions and present open issues of future research in QoE.
