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This research aims to study the issues derived from the current trade finance processes, followed by evaluating the available technological solutions proposed by researchers and FinTechs. The purpose of seeking technological solutions is to replace the current obsolete processes of traditional trade finance, which rely heavily on manual monotonous labor and paperwork. In this research, the impact of blockchain technology on the Trade Finance processes is examined using three simulation models, Paper Letter of Credit, Bank Payment Obligation (BPO), and Blockchain Letter of Credit. The research findings observed a significant increase in process efficiency up to 94% while conducting processes under Blockchain Platform and 74% using BPO. Moreover, the study demonstrates how the efficiency gained from applying blockchain technology to the trade finance processes could increase productivity and significantly reduce processing costs. The research findings provided an essential awareness to banks, corporates, and governmental authorities about the importance of blockchain technology in trade finance and highlighted the need for an urgent digital transformation. Researchers can extend studies by applying proof of concept of Blockchain implementation to other trade finance processes such as Documentary Collections and Guarantees. Future work can include more parties from the entire trade ecosystem such as Shippers, Insurers, and Governmental authorities. [ABSTRACT FROM AUTHOR]

This paper is about new approach to teaching in Bauman Moscow State Technical University, Russia, carrying on now in the framework of the ERASMUS+ Capacity Building for Higher Education just starting a project (# 586060-EPP-1-2017-1-RO-EPPKA2-CBHE-JP). The name of the project is "Excellence in Engineering Education through Teacher Training and New Pedagogic Approaches in Russia and Tajikistan". It concerns to Kolmogorov Complexity usage in PhD students curricula. Introduction part is about the growth of massive amount of data generated by different sources at real time or near real time. Generally, data is heterogeneous by its content and exists at every human sphere such as education, government, finance, medicine and so on. That cause fundamental issues in data storage, data security and high-speed access to data. Such issues directly related to new research aims. The first part of the paper describes an example of one PhD student's approach to her research using Kolmogorov complexity. The second part of the paper is about the managing immense volumes of data, which was possible and held only with help of supercomputers that lead to great expenses. Applying of distributed computing systems allow to assign and handle operations on homogeneous or heterogeneous nodes with less expenses compared to supercomputers. However, data and operation partition and distribution among nodes could be a challenge by itself. Furthermore, nodes interaction can cause significant difficulties as generally distributed systems consist of great number of nodes, which could be constant in quantity or constantly growing. In practice, most systems should be scalable due to incessant data growth and tasks for processing that means that any system needs to meet with such circumstances and not limited by constant number of nodes. The third part of the paper is about the possibility's analyses of Kolmogorov complexity usage for optimal way of data dividing and processing in distributed computing systems without quantity nodes limitations. The Conclusion show that the investigation is fundamental as distributed systems could be beneficial at any spheres and organizations including universities and business companies and the PhD students could use Kolmogorov complexity as an investigation tools. Besides, this paper's aims is to describe the advantages, disadvantages of Kolmogorov complexity with criticism and arguments. [ABSTRACT FROM AUTHOR]

Enterprise architects and IT systems developers must often decide ad hoc how to identify, assess and mitigate ethical issues of autonomous, rule-based systems based on blockchain technology. As blockchain systems are decentralized and immutable, developers must assess ethical risks, not only on an individual level but also on a network level along the life-cycle stages of a blockchain system. These ethical issues should be addressed and ideally mitigated by clearly defined norms and values that are intentionally incorporated in a blockchain system. As a reference for a normative system, we have chosen the European Union and its ethical values to discuss them in the context of ethical issues emerging from blockchain systems. This paper presents a top-down approach that establishes ethical guidelines for blockchain systems based on ethical issues mapped against European values. [ABSTRACT FROM AUTHOR]

It is a well-known fact that innovative e-learning concepts have pushed IT technologies to new levels of development or, reciprocally, innovative or even disrupting technologies have opened new possibilities for elearning. There are many examples of IT technologies that contributed to new or improved learning paradigms and styles, such as social networks, mobile devices, augmented and virtual reality, MOOCs and distributed computing. Recently, several research works discuss how the blockchain concepts and technologies can be applied not only in cryptocurrency, but also on elearning and educational processes with an essential impact, e.g. de-centralization of resources, open learning, tokenization of elearning, authenticity and security of information and resources. The current research paper will address how blockchain can be utilized with microservice based architectures, based on similarities, to support the most modern trends in education, such as open learning communities. In the first part of the paper, the main concepts and mechanisms behind the blockchain technologies are reviewed and explained in comparison with concepts and characteristics of microservices, considered a similar architectural pattern. Blockchain relies on the existing algorithms (such as cryptography) and distributed computing to bring new concepts such as ledgers and smart contracts. Likewise, microservices have roots on Service-Oriented Architecture (SOA) and Application Programming Interfaces (API). Microservices represent a new arhitectural pattern, to change the traditional way of software development resulting in "application monoliths" to a true modularization by means of composition of standalone software components (microservices), having well-defined functionality, securely exposed to other microservices or applications. In the second part, a software architecture leveraging blockchain smart contract and microservices to support open learning communities, unique identity, secure storage and retrieval of resources will be proposed and described. [ABSTRACT FROM AUTHOR]

Training models in machine learning often requires big computational capacities, even more when it involves Big Data. The Patient Rule Induction Method (PRIM), introduced as a bump hunting algorithm and a subgroup discovery method, already consumes a lot of computing resources when searching for the rules. In this computational research area, several methods have been developed to handle the problem of computing a big dataset: parallel learning, distributed learning and federated learning. In this paper, we investigate the literature review of these three methods to define each one and the differences between them, and then we determine which of these methods is best suited for the implementation of PRIM in big dataset for out futur works in Big Data. [ABSTRACT FROM AUTHOR]

Multimedia content has become more and more popular in current eLearning platforms. However, there are still many use cases which require virtualized resources from cloud platforms: gamification, video analytics, and surveillance during exams, video conferencing for coaching, and video streaming (live and video-on-demand) services. This paper has the purpose to present how video services can exploit the virtualization techniques to provide ubiquitous video learning experience. Virtualization consists of multiple operating systems on a single physical system. Virtualization shares the underlying hardware resources, providing multiple execution environments for software partitioning and time-sharing. Furthermore, virtualization is the basis for cloud computing, which is adaptable and secure sharing of resources as a service, leading to ease of use for different types of computing platforms. This gives to the teachers the authority to deploy their curricula to new channels and to promote it with efficient video communication solutions. It is expected, from this point of view, that virtualization will contribute in a beneficial way to these use cases. The main concept we present in this paper is how to virtualize the video services and utilizing them combined with other eLearning techniques, whereas the virtualization technique permits virtualization for full server machines along with the whole operating system. The main objective is to demonstrate they can be monitored and easily be maintained, as well being extremely secure and provide uninterrupted services. Furthermore, virtualized video using Cloud would establish and provide quality eLearning relationships regardless of distance and devices or operating systems, being accessible through a web browser. [ABSTRACT FROM AUTHOR]

Distributed Ledger Technology (DLT) acts as a medium for transferring value amongst members of a shared network. Tokenization is a prominent factor enabling this type of value transfer by allowing non-DLT based assets to undergo a digital transformation to a distributed ledger environment. With the advent of new and emerging asset classes, it is necessary to explore the suitability of migrating these non-DLT based assets from their conventional data forms to a distributed ledger environment. In this paper, assets are examined to evaluate if their investment appeal is increased after tokenization, by analyzing changes in the underlying assets' structure and potential disruption to external parties. This study reviews the existing body of knowledge at the intersection of DLT, digitalization and finance to map the benefits of tokenization on different asset classes. Additionally, this paper aims to contribute to existing research by providing a framework to assess the suitability of tokenizing an asset. For this paper, tokenization is viewed as the consolidation of embedding financial functionality (improvements to an asset's liquidity, composability or available information)(Merton & Bodie, 1995) and digitalization (disruption to the actors, settings or offerings) of an asset (Hagberg et al. 2016). The following question will be addressed: How can the tokenization suitability framework derived in this paper be used as a decision support tool? [ABSTRACT FROM AUTHOR]

The purpose of this study is to identify the potential security and privacy issues for the entities providing cloud identity services known as cloud identity providers. We document the key security and privacy issues and problems that arise when users and service providers co-operate for cloud service utility. The Delphi method is adopted to facilitate identifying the security and privacy issues in the cloud identity environment. The benefit of using the Delphi method is identification of the issues and prioritizing the relative importance of these issues. A four-round modified Delphi process was applied to a general database of security literature, and then the most relevant articles selected for further analysis. The analysis highlights key issues, advantages and disadvantages of the identity and access management methods; and, risk mitigation mechanisms for identity theft. [ABSTRACT FROM AUTHOR]

For supporting the conceptualization and the management of enterprise models in a decentralized manner, this paper introduces an approach based on model versioning and blockchain technologies. The main contribution is twofold, consisting of a., the creation of models for inter-organizational business processes in a decentralized environment, and b., means for tracking process instances using meta-data at run time. Models for business processes, workflows, and instance states are collaboratively created as part of a decentralized architecture. Based on this approach, a hierarchical versioning and modeling approach is employed in order to create and manage public and private models in a transactional fashion. For forming relationships among decentralized participants, semi-formal models linked to a blockchain are suggested. The approach is evaluated with a supply chain use case and demonstrated in an implemented modeling tool. [ABSTRACT FROM AUTHOR]

Over the last decade, face recognition has become a popular area of research and with the cloud computing paradigm gaining more and more ground, thus ensuring the resources needed for real-time image/video analysis, new and more powerful face detection algorithms have been developed. Face recognition has a wide spectrum of applicability such as: access control, identification systems, surveillance or monitoring group emotions and combined with real-time and cloud-computing paradigms has led to the development of various successful systems used by important entities (SmartGate -- Australian Customs Services, Next Generation Identification --FBI, Visidon Applock -- Android ). The main advantage of the face recognition is that it is a non-contact process thus, it is able to offer a wide rage of information about a subject or group of subjects without requiring explicit actions from the target except the presence. The paper presents the face recognition for educational purpose, with the challenges and opportunities of cloud computing. The perspective of usability is more on intelligent testing based on real-time communications, with thin clients and face display embedded in usual browsers but with strong data analytics in the cloud, for authentication or classification. [ABSTRACT FROM AUTHOR]

In traditional research on task scheduling algorithms based on user satisfaction, all users can only have the same satisfaction function that means system assumes all users have the same scheduling motivation. But in real distributed system, different users usually have different motivations. To solve this problem, in this paper, we propose a genetic scheduling algorithm based on user overall satisfaction, which is the sum of the value of every user satisfaction. This algorithm supports different satisfaction functions for different users, which meets the requirements of real distributed environment well. [ABSTRACT FROM PUBLISHER]

This paper is on homonymous distributed systems where processes are prone to crash failures and have no initial knowledge of the system membership (``homonymous'' means that several processes may have the same identifier). New classes of failure detectors suited to these systems are first defined. Among them, the classes $\HO$ and $\HS$ are introduced that are the homonymous counterparts of the classes $\Omega$ and $\Sigma$, respectively. (Recall that the pair $\langle \Omega, \Sigma\rangle$ defines the weakest failure detector to solve consensus.) Then, the paper shows how $\HO$ and $\HS$ can be implemented in homonymous systems without membership knowledge (under different synchrony requirements). Finally, two algorithms are presented that use these failure detectors to solve consensus in homonymous asynchronous systems where there is no initial knowledge of the membership. One algorithm solves consensus with $\langle \HO, \HS\rangle$, while the other uses only $\HO$, but needs a majority of correct processes. Observe that the systems with unique identifiers and anonymous systems are extreme cases of homonymous systems from which follows that all these results also apply to these systems. Interestingly, the new failure detector class $\HO$ can be implemented with partial synchrony, while the analogous class $\AO$ defined for anonymous systems can not be implemented (even in synchronous systems). Hence, the paper provides us with the first proof showing that consensus can be solved in anonymous systems with only partial synchrony (and a majority of correct processes). [ABSTRACT FROM PUBLISHER]

This paper describes the efforts at Ohio University to incorporate selected topics from the IEEE-TCPP Curriculum Initiative into the Computer Science/Computer Engineering curriculum prior to a transition to semesters at Ohio University that will occur in the Fall of 2012. In particular, this paper describes our efforts to incorporate (and evaluate) selected elements of the IEEE-TCPP Curriculum Initiative into three courses in order to best determine the appropriate placement of topics related to parallel and distributed computing in the new CS/CpE curriculum under the semester calendar. In particular, we plan to add or revise existing modules and assignments for CS2 (CS 240B, CS 240C at Ohio University, CS 2401 under semesters), DS/A (CS 361 Data Structures at Ohio University, CS 3610 under semesters), and Systems (CS 442 Operating Systems and Computer Architecture I, CS 4420 under semesters) to help us determine which curricular recommendations belong in those three courses in the new semesters curriculum and which topics are more appropriately placed in new required courses entitled EE 3613 Computer Organization and CS 4000 Introduction to Parallel, Distributed, and Web-Centric Computing or in other existing advanced courses such as CS 4040 Design and Analysis of Algorithms or CS 4100 Formal Languages and Compilers. [ABSTRACT FROM PUBLISHER]

Cloud Computing -- some treat it as a catchphrase, others as the next step in evolution of their organization and the Internet. However, what is for sure is that, it is a way of leveraging less expensive services based environment providing cost-effective solutions to various organizations to cater for their computing needs, without investing in computing infrastructure. However, cloud computing, just like other formats of computing, is not free from issues. One of the major barriers in adoption of cloud is security. Once migrated to cloud, you do not know where your data is physically stored, what laws and regulations govern the same and most important of all, who has access to it. This paper explores security issues related to cloud computing and proposes a paradigm for securing cloud. The paper investigates some of the key research challenges of implementing cloud-aware security solutions which can plausibly secure the ever-changing and dynamic cloud environment, followed by conclusion where we try to entail the whole research and try to formulate a practical security paradigm, which will enable the cloud providers and consumers to ensure that their data and valuable assets are safe from otherwise prying eyes. [ABSTRACT FROM PUBLISHER]

We have recently proposed a Distributed Reconfigurable Active SSD computation platform (RASSD) for processing data-intensive applications at the storage node itself, without having to move data over slow networks. In this paper, we present the design of an operating system (OS) for the RASSD node. RASSD OS is a multitasking real-time operating system that runs on the 32-bit MicroBlaze® soft processor core available for Xilinx® FPGA's. We discuss in this paper our OS design features which include initializing the node and configuring the different components of the RASSD node, monitoring the node's activities, and processing middleware requests. RASSD OS provides a set of services to the middleware through which it hides the low-level details of the node's hardware architecture. We describe the functions essential for the data-intensive processing within the RASSD node using examples that capture the common states of the node and various possible requests. [ABSTRACT FROM PUBLISHER]

The data of smart grid have high frequency of collection, large data size as the characteristics, so traditional technical methods can't meet the demand of the data processing of smart grid. The distributed parallel computing framework the core of which is MapReduce provides the basis for the construction of smart grid cloud data analysis platform. The paper proposes and has actually built a cloud data analysis processing platform used in the smart grid environment. The platform uses MapReduce technology to achieve the data loading of smart grid, multi-dimensional analysis and knowledge discovery based on data mining technology. At the same time, the paper uses the platform to construct an intelligent industrial park, which has solved some problems, such as the storage of massive data of the park, load forecasting and enterprise power consumption behaviour analysis and so on. Finally, the paper also analyzes the defect of cloud data analysis processing platform and the focus of the research direction in the future. [ABSTRACT FROM PUBLISHER]

Nowadays there is a huge growth of massive amount of data generated by different sources at real time or soft real time. Generally data is heterogeneous by its content and exists at every human sphere such as education, government, finance, medicine and so on. This paper is about the possibility to use the Kolmogorov complexity as a technological innovation in such area as education. The article describes fundamental issues such as data storage, data security and high speed access to data as one of the main parameters for successful using of eLearning platforms for education. It is considered comparisons of different eLearning platforms and its functional principles. Most eLearning platforms based on supercomputer and distributed computing systems, however, the growing volumes of data, especially in educational sphere, required other approaches of data storing and access. Nevertheless, educational platforms should be scalable and cross accessible. To increase data processing and storing efficiency it is considered data and operation partition idea among heterogeneous computational nodes which is the main infrastructure for eLearning platforms and methods. Kolmogorov complexity approach is investigated for such purposes. Furthermore, Kolmogorov complexity approach is examined for reduction a number of transmitting data between nodes. The possibility of using Kolmogorov complexity for optimal way of data dividing and processing in distributed and heterogeneous computing system and nodes without quantity nodes limitations is considered. The advantages and disadvantages of Kolmogorov complexity were considered applying to eLearning study aims. Experiments and results are given. The further plan investigation is described. [ABSTRACT FROM AUTHOR]

The attitude towards cloud computing is influenced by multiple factors acting as drivers and barriers for its adoption. This research paper aims at the reconceptualization and operationalization of widely accepted influencing factors on technology adoption in relation to cloud computing. A literature review on technological innovation characteristics in this context is conducted to identify potential gaps in ongoing research. The review also provides an overview of relevant empirical studies on cloud computing that are based on theories for innovation adoption such as the Diffusion of Innovation (DoI) theory and the Technology Acceptance Model (TAM). Consequently, the focus is set on the examination of the factors "compatibility", "relative advantage", "complexity", "image" and "security & trust". Furthermore, an operationalization of each factor in the context of cloud computing is provided. [ABSTRACT FROM AUTHOR]

In recent times, cloud computing has become increasingly popular among the public and private sectors. The ability to store information using an easily accessible and distributed environment represents the next important paradigm shift. This allows different businesses to store a high quantity of information and access their information in a faster and more efficiently. Although the cloud is conceptually advantageous for many users, there are several disadvantages that prevent its adoption. Cloud computing faces many challenges dealing with the security and the vulnerabilities. Cloud service providers have yet to completely resolve the security and vulnerability issues. Since there is a large amount of information being stored in the cloud, it is important to limit the number of people who have access to the information of the cloud service user. In this paper, we propose a novel framework for securing information in the cloud. This method entails a Demilitarized Zone (DMZ) in which a user may implement identity management to authenticate users and network routers. Moreover, we analyze how a two factor identity management approach can efficiently secure information. [ABSTRACT FROM AUTHOR]

With the increasing digitalization of the economy, it is becoming increasingly important for organizations to embrace digital innovation. The emergence of blockchain technology exemplifies the disruptive impact of digital innovation and, simultaneously, poses challenges for organizations. Hence, organizations are in transition with regard to fostering its organizational adoption. This kind of adoption is reflected in far-reaching measures in terms of technology, organization, and people. The measures are governed by project management that aligns internal measures with the outside environment. Based on this framework, our research builds on an empirical study to explore the organizational adoption of blockchain technology. We conducted in-depth interviews in 11 cases with experts leading the adoption of blockchain. The objective of our study is to better understand organizational adoption and to explore the factors influencing adoption. Our results uncover a multitude of factors and inherent tensions. The paper's contributions are twofold. First, our findings can be used as guidelines for organizations planning to adopt digital innovations such as blockchain and to effectively cope with the related phenomena. Second, the factors advance the literature on blockchain by conceptualizing its organizational adoption and aligning the factors to the organizational adoption framework. [ABSTRACT FROM AUTHOR]

Technological advancements, such as machine learning or the concept of blockchain, have the potential to reshape entire branches of industry as a disruptive technology, but also offer the possibility to optimize existing processes. In this paper, we present a concept for vendor managed inventory relationships based on blockchain technology. Hence, we initially define necessary processes and transactions that highlight a special need for transparency and trust between the participants. We designed and developed a proof of concept that addresses the weaknesses of current VMI solutions by performing certain process steps in the blockchain. Thus, we implement a method based on smart contracts, which can interact with existing systems by using different data interfaces and therefore represents an application of Blockchain 3.0. The present work is intended to explain the concept to the reader and to support researchers for the future development of further industrial applications of the blockchain. [ABSTRACT FROM AUTHOR]

Genetic Algorithm (GA), one of the artificial intelligence algorithms, performs much better than the other algorithms for the document clustering. However, it has problem known as the premature convergence occurrence. So, Fuzzy Logic based GA (FLGA) was proposed to solve it. Nevertheless, it has still weakness such as the parameter dependence problem. In order to overcome this problem, the Multi-Objective Genetic Algorithms (MOGAs), NSGA-II and SPEA2, have been proposed. The MOGAs showed high performance compared to other algorithms including the general GA, but their computational times have increased. In order to reduce these computational times, the distributed computing method, MATLAB Distributed Computing (MDC) with 10 processors, is applied to the MOGAs in this paper. The performances of MOGAs on MDC show about 12% higher than others. [ABSTRACT FROM PUBLISHER]

This paper first briefly introduces the Mobile Agent technology, then analyzes some other Mobile Agent communication mechanisms by comparing them with each other, based on which we generate Assistant Agent by Mobile Agent with combination of Shortest Path First (SPF) algorithm to receive the message as a new Mobile Agent communication system and give the experimental results to demonstrate. [ABSTRACT FROM PUBLISHER]

Information systems based on the cloud computing model and peer-to-peer (P2P) model are now getting popular. In the cloud computing model, a cloud of servers support thin clients with various types of service like Web pages and databases. On the other hand, every computer is peer and there is no centralized coordinator in the P2P model. It is getting more significant to discuss how to reduce the total electric power consumption of computers in information systems to realize eco-society. In this paper, we consider a Web type of application on P2P overlay networks. A model shows how much server peer consumes electric power to performWeb requests from client peers. Here we are creating VM migration to consolidate the server utilization. An algorithm is used for a client peer to select a server peer in a collection of server peers so that the total power consumption can be reduced. Lastly, we evaluate the algorithms in terms of the total power consumption and throughput compared with traditional round robin algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents the impact analysis of the hyper visor layer over database applications. The conclusions were reached by performing experiments with hybrid environments, composed by bare-metal servers and two types of hyper visors. The main objectives were to compare the behaviour of database applications and to determine the maximum service capacity of a virtualized database server on different environments. With the results, it was possible to assess the scalability of a database environment, according to the type of hyper visor and the number of simultaneous connections, and decide how and when to expand or reduce the virtualized infrastructure. [ABSTRACT FROM PUBLISHER]

Asynchronous task allocation is a fundamental problem in distributed computing in which p asynchronous processes must execute a set of m tasks. Also known as write-all or do-all, this problem been studied extensively, both independently and as a key building block for various distributed algorithms. In this paper, we break new ground on this classic problem: we introduce the To-Do Tree concurrent data structure, which improves on the best known randomized and deterministic upper bounds. In the presence of an adaptive adversary, the randomized To-Do Tree algorithm has O(m+plogplog2m) work complexity. We then show that there exists a deterministic variant of the To-Do Tree algorithm with work complexity O(m+p log5 m log2 max(m, p)). For all values of m and p, our algorithms are within log factors of the O(m + p log p) lower bound for this problem. The key technical ingredient in our results is a new approach for analyzing concurrent executions against a strong adaptive scheduler. This technique allows us to handle the complex dependencies between the processes' coin flips and their scheduling, and to tightly bound the work needed to perform subsets of the tasks. [ABSTRACT FROM PUBLISHER]

This paper examines the integration of the NSF/TCPP Core Curriculum Recommendations in a liberal arts undergraduate setting. We examine how parallel and distributed computing concepts can be incorporated across the breadth of the undergraduate curriculum. As a model of such an integration, changes are proposed to Data Structures and Design and Analysis of Algorithms. These changes were implemented in Design and Analysis of Algorithms and the results were compared to previous iterations of that course taught by the same instructor. The student feedback received shows that the introduction of these topics made the course more engaging and conveyed an adequate introduction to this material. [ABSTRACT FROM PUBLISHER]

Cloud is a promising source for computing resources when solving scientific problems. Several large companies have entered the cloud computing market in recent times and are providing cloud computing services like Infrastructure as a Service (IaaS) that provide virtual computer machines on demand. These virtual machines can easily be used for the high performance computing needs by applying distributed computing methods and frameworks. The SciCloud project at the University of Tartu has studied adapting scientific computing algorithms to different distributed computing frameworks like MapReduce and its optimizations. The study has concluded that the current solutions do not fully support complex iterative algorithms, which are commonly used in scientific simulations. This paper presents a design for a new distributed computing framework, Stratus, which fully supports scientific computing algorithms and takes advantage of the characteristics that have made cloud such a convenient and popular source for computing resources. The paper also describes the motivation for creating a brand new solution, outlines its architecture and design, and gives an overview on how algorithms can be adapted to this framework. [ABSTRACT FROM PUBLISHER]

In this paper we present a Platform-as-a-Service (PaaS) approach for rapid development of wireless sensor network (WSN) applications based on the dinam-mite concept, i.e. an embedded web-based development environment and run-time platform for WSN systems integrated in a single information appliance. The PaaS is hosted by a cloud of dinam-mite nodes which facilitates the on-demand development, deployment and integration of WSN applications. We introduce the dinam Cloud architecture and focus, in this paper, on the PaaS layer established by the dinam-mite nodes. In addition to the description of this so-called dinam PaaS, a performance analysis of the dinam-mite node towards its applicability to forming a dinam PaaS layer is demonstrated. We then present the MASON mobile vehicular network as an example of such a WSN which delivers spatially and temporally fine-grained environmental measurements within the city of Beijing, and illustrate how to utilize the dinam PaaS for integrating the data from the MASON network into its back-end business system. Finally, we discuss the five essential properties of the Cloud Computing stack, according to the NIST definition, with respect to the dinam PaaS and illustrate the benefits of the dinam PaaS for system integration as well as WSN application development. [ABSTRACT FROM PUBLISHER]

Using the high fidelity model in the multidisciplinary design optimization (MDO) application is becoming more and more popular, but it brings complex coupling relationship and huge computing intensity, which need efforts to be well organized to reduce the computation time. In this paper, a parallel computing environment distributed in local area network (LAN) was established based on the collaborative optimization (CO) method. The distributed computing system communicated with Transfer Control Protocol (TCP) and used XML database to store and manage the data. The proposed system was applied in a remote sensing satellite system parameters design considering four subsystems, which includes structure, attitude control (AC), power, and thermal control (TC). The result shows that the efficiency can be improved by about 60% relative to the sequential running plan. [ABSTRACT FROM PUBLISHER]

The computational humanity is flattering extremely bulky and multifaceted. Cloud computing is becoming one of the most expanding methodologies in the computing industry. It is a novel approach for the deliverance of IT services on the World Wide Web. This model provides computing resources in the puddle for consumers, all the way through Internet. In cloud computing, resource allocation and scheduling of numerous aggregate web services is an imperative and demanding quandary. This paper estimates the various network resource allocation strategies and their applications in Cloud Computing Environment. A brief description for network resource allocation in Cloud Computing, based on differentially adapted dynamic proportions, has also been done. This paper addresses and categorizes the foremost challenges normal to the resource allocation progress of cloud computing in terms of diverse types of resource allocation techniques. [ABSTRACT FROM PUBLISHER]

In distributed computing field, there are many challenges, and one of them is leader election in distributed system. It is really tough task to find suitable and efficient algorithms for leader election. The main role of an elected leader is that it performs a centralized coordination after being selected and manages the use of a shared resource in an optimal manner. Whenever a failure occurs the new leader is elected by nodes using various algorithms so that nodes can continue working. In this paper, the proposal is a new approach, the improved heap tree mechanism for electing the coordinator. The higher efficiency and better performance in the presented algorithms with respect to the existing algorithms is validated through results. [ABSTRACT FROM PUBLISHER]

In recent years, MapReduce parallel computing model has gained lots of attentions from industry and academia. In Google, Yahoo, Facebook, etc., it has played a very good effect, which greatly simplifies the design of large-scale data-intensive applications. MapReduce-based systems were originally used to manage massive unstructured and semi-structured data, for example: to generate the inverted index, to calculate web page rank, log analysis, etc. Therefore, current MapReduce systems don't give more considerations to the optimization of structured data, for example: it uses the brute-force scanning mode to process the whole datasets, which confront the common workflow of structured data processing, range query and analysis. To address the problem, this paper propose to build a global B-tree like index on top of hadoop distributed file system for structured data, and use the global index to eliminate unnecessary map tasks during range queries, thereby reducing the overhead of data I/O and tasks scheduling, which not only reduces query response time, but also greatly optimizes system resource utilization. [ABSTRACT FROM PUBLISHER]

In recent years, many service providers have started migrating their service offerings to cloud infrastructure. Sometimes, parts of the service workflow can however not be moved to cloud environments. This can occur due to client policies, or because some services are linked to physical client-site devices. The result of the migration is then a hybrid cloud environment, where part of the services are executed within the client network, while most of the processing is moved to the cloud. Migration to the cloud enables a more flexible deployment of services, but also increases the strain on underlying networks as most tasks are partially handled in a remote cloud, and no longer just in the local network. An important question that providers must answer before new service workflows are deployed is whether they can provide the workflow with sufficient quality of service, and whether the deployment will impact existing service workflows. In this paper we discuss strategies based on multi-commodity flow problems, a subset of graph flow problems that can be used to determine whether new service workflows can be sufficiently provisioned, and whether the addition of new workflows can negatively impact the performance of existing flows. We evaluate the proposed solution by comparing the performance of three approaches with respect to the number of successful workflows and with respect to their execution speed. [ABSTRACT FROM PUBLISHER]

With network developed quickly and used widely, how to store mass data has become a problem we have to face. Traditional methods usually add more servers into to increase computing speed and storage ability, while the hardware cost is very expensive and the storage efficiency is low. Through deep study of MapReduce programming model and Hadoop framework, this paper presented a mass data storage model based on Hadoop framework, then we designed and implemented a mass data storage system for campus network based on cloud computing. This system can effectively solved issues in mass data storage management in teaching and study. This system is cheap, easily operated, fast dealing and stable. [ABSTRACT FROM PUBLISHER]

Nowadays, evacuation is mostly based on individual decisions, which is unreliable. This paper introduces a practical solution towards emergency evacuation by guiding people out of building efficiently. Built on wireless sensor network (WSN), our system is capable of learning and dynamically generating evacuation strategy. Distributed mechanism and backup mechanism feature the scalability and invulnerability. Simulations prove the efficiency and reliability of the system. [ABSTRACT FROM PUBLISHER]

In this paper, we propose three lower bounds to the constrained task allocation problem (CTAP). These three lower bounds are: a heuristic lower bound (HLB), a linear programming relaxation (LPR) lower bound and a lagrangean relaxation with subgradient optimization (LRSO) based lower bound. We empirically compare the performance of these three bounds under different task executing times. Our experiments with real-world CTAPs indicate that the LPR provides efficient and tight lower bounds for the CTAP. [ABSTRACT FROM PUBLISHER]

This paper presents an approach to perform bit rate reduction Transco ding by video segmentation. The paper shows how a high performance distributed video transcoder can be built using multiple processing units and a Message Passing Interface based parallel programming model. The computation parallelization and data distribution among computing units is discussed. For data distribution coarse grain approach is used in which significant gain in terms of execution speedup is obtained. The segmentation of video stream with (1) equal size having unequal number of intra frames and (2) unequal size having equal number of intra frames is performed to achieve high performance. The results show that the proposed distributed video transcoder provides very short startup times. [ABSTRACT FROM PUBLISHER]

This paper presents an ontology-based approach for data quality inference on streaming observation data originating from large-scale sensor networks. We evaluate this approach in the context of an existing river basin monitoring program called the Intelligent River®. Our current methods for data quality evaluation are compared with the ontology-based inference methods described in this paper. We present an architecture that incorporates semantic inference into a publish/subscribe messaging middleware, allowing data quality inference to occur on real-time data streams. Our preliminary benchmark results indicate delays of 100ms for basic data quality checks based on an existing semantic web software framework. We demonstrate how these results can be maintained under increasing sensor data traffic rates by allowing inference software agents to work in parallel. These results indicate that data quality inference using the semantic sensor network paradigm is viable solution for data intensive, large-scale sensor networks. [ABSTRACT FROM PUBLISHER]

Nowadays, technology grows fast and cloud computing and big data become important parts of the businesses. Cloud computing brings many advantages, such as flexibility, easy recovery, automatic software updates, availability -- which allows users to work from everywhere. One of the most important characteristic is that cloud computing provides resources on demand, all we need is an internet connection. So, cloud computing is adopted by more and more companies due its benefits. An important aspect of the society is education. As the technology is expanding continuously, also the education needs to move in parallel. As a consequence, we can follow different courses online, on the eLearning platforms. There are many eLearning platforms, with different subjects of learning, some courses being provided by top universities, or top companies with certificate of completion after a final exam. Due to cloud computing benefits, eLearning platforms could be developed as services. Still, a sensitive subject regarding to cloud computing is its security. The cloud computing security is threatened from many points of view, because of its complexity. The security issues could occur at the communication level (because of shared infrastructure, virtualization, and configuration), architectural level (security challenges for data storage, web applications and APIs, access control), or contractual and legal level. The researchers are working continuously in order to provide strong security. For example, at the architectural level, could be used attribute based encryption or identity based encryption techniques. In this paper we propose a new framework for an eLearning environment, provided as a service, such that, it could be accessed from everywhere and on every type of device. We secure our framework using attribute based encryption scheme applied in cloud computing. [ABSTRACT FROM AUTHOR]

Cloud brokers are intermediaries supporting the evaluation, selection, and implementation of cloud services. Especially small and medium-sized enterprises with limited technical competencies struggle with a complex and non-transparent market, selecting and combining services from different cloud service providers. They need guidance in the pre-adoption phase to successfully handle selection decisions. Using decisional guidance theory, we have designed a cloud broker platform that aims to provide such guidance. The results of its evaluation show how such a platform improves selection decisions by reducing complexity and structuring the selection process. They also show that the platform’s usefulness may be limited in the case of very complex cloud services depending, for example, on the degree of customization needs. Our study provides design knowledge for decisional guidance (in the context of cloud broker platforms) and shows how cloud service selection can be implemented in practice. [ABSTRACT FROM AUTHOR]

High availability in complex distributed systems is a challenge that has to be considered not only at design time, but also at execution time. This paper proposes an automatized monitoring architecture, event driven, based in rules with the goal of minimizing systems unavailability. Translating the expertise that operators have to rules, and providing an appropriate interface for services operation, allow us to execute self-healing actions to anticipate or correct fault cases as quickly as possible. [ABSTRACT FROM AUTHOR]

We document ongoing research on a digital artefact comprising a settlement layer for financial contracts traded over the counter (OTC). Working with a team of stakeholders from a hosting organization we employ the design science research methodology (DSR) in the design, implementation and evaluation of a DLT based settlement layer for OTC contracts. Our findings indicate that DLT may serve towards reducing execution costs while mitigating counterparty credit risk through deterministic automation of key processes in the OTC contract lifecycle. Extrapolating these early results, we introduce three general propositions on the systemic and commercial implications of DLT. We contribute to the broader IS discourse on the capacity for emerging digital technologies to upend traditional knowledge processes in the financial services. [ABSTRACT FROM AUTHOR]

This paper proposes a model of a colluding attack in Volunteer Computing (VC), where some of participants are unreliable and may return incorrect results. The colluding attack is one of the most important issues to realize reliable VC because it may ruin the most basic assumption, i.e. "the majority in voting is correct". Especially, check-by-voting archives the largest efficiency by sorting reliable participants based on whether their results are the majority or not. Thus, if some incorrect results become the majority by colluding attack, it may have significant impacts on check-by-voting. In this paper, we perform a Monte Carlo simulation of VC using the proposed colluding model and evaluate the sabotage-tolerance performance of voting methods. Simulation results show that check-by-voting works well if colluding attack happens. [ABSTRACT FROM AUTHOR]

In this paper we consider the core issues that are inhibiting the application of general Service Oriented Architecture (SOA) principles to distributed real-time military environments. In particular, we propose a Distributed Real-Time Service Oriented Architecture (DRT-SOA) framework intended to overcome some of the problems that distributed SOA systems face in needing to exhibit verifiable deterministic behaviour. Although the time behaviour of asynchronous systems is inherently non-deterministic, such systems offer advantages in terms of extensibility, cost and utilisation. We show that DRT-SOA presents a framework where the sources of non-determinism can be supressed, making it suitable for certain classes of real-time system. We also show that decentralised execution of complex workflows is achievable using dynamic rule-based logic. We also present preliminary results demonstrating the architecture's ability to perform decentralised execution of workflow and to resolve deadline constrained tasks. We also present results of a sensitivity analysis showing that the architectural framework is able to scale in support of dynamic redeployment of workflow. This has application when needing to adapt to changing run-time needs in the case of self-healing. [ABSTRACT FROM PUBLISHER]

Many complex systems, such as multi-agent modeling and simulation which are crucial in military applications, necessitate concurrent task planning and decision making. These applications include battlefield modeling and simulation for soldier virtual training. In general these systems are implemented on automated systems with distributed architectures on heterogeneous operating systems and hardware platforms. In this paper we present an autonomous multi agent path planning system with agents-in-formation constraints in a distributed computing environment. Here a given agent, an entity with behaviors, exchanges sensory positioning feedback information over the network with other agents that are running on separate networked distributed computing nodes than the one the given agent is running on. Hence the agents do not have complete knowledge of the environment, but only a limited one. That is, in keeping with the agent formation constraint, the agents have knowledge only of the agents' information residing on the distributed memory of the computing node the agents are running on. The agents exchange agents' positioning sensory information of the rest of the agents they are interacting with, over a distributed network. Here agents autonomous trajectory planning is achieved for each agent to reach its destination via an unstructured distributed environment filled with dynamic obstacles and other agents. One focus is on the agents achieving their goals under real time formation constraints. We propose a multi-threaded architecture to achieve light-weight concurrency and multi-agent cooperative concurrency, as they allow separation between address space and thread control. Additionally, a threads-based architecture benefits from non-blocking system calls. Finally, experimental results are presented to show the potential of the proposed method for multi-agent distributed path planning. [ABSTRACT FROM PUBLISHER]

This paper describes the work carried out to implement a wireless sensor network capable of automatically evacuating a building. A distributed computing cloud was created in nesC for TinyOS and two different types of evacuee detection sensors were designed and tested. LED display boards were designed from first principles and used to guide evacuees to the nearest exit. Control room software was developed to display evacuation data to a control room operator and used a building overlay together with graphs and charts to display the information in several meaningful ways. The system was successfully deployed at the Council of Scientific and Industrial Research (CSIR) and several tests were conducted. The distributed computing cloud functioned as expected, directing evacuees along the most effective routes, based on routing metrics acquired during the evacuation. The system was able to re-route evacuees based on congestion estimates, additional evacuation triggers and manual overrides. [ABSTRACT FROM PUBLISHER]

We consider the problem of reliably broadcasting information in a multi hop asynchronous network that is subject to Byzantine failures. That is, some nodes of the network can exhibit arbitrary (and potentially malicious) behavior. Existing solutions provide deterministic guarantees for broadcasting between all correct nodes, but require that the communication network is highly-connected (typically, 2k+1 connectivity is required, where k is the total number of Byzantine nodes in the network). In this paper, we investigate the possibility of Byzantine tolerant reliable broadcast between most correct nodes in low-connectivity networks (typically, networks with constant connectivity). In more details, we propose a new broadcast protocol that is specifically designed for low-connectivity networks. We provide sufficient conditions for correct nodes using our protocol to reliably communicate despite Byzantine participants. We present experimental results that show that our approach is especially effective in low-connectivity networks when Byzantine nodes are randomly distributed. [ABSTRACT FROM PUBLISHER]

High-performance scientific applications require more and more compute power. The concurrent use of multiple distributed compute resources is vital for making scientific progress. The resulting distributed system, a so-called Jungle Computing System, is both highly heterogeneous and hierarchical, potentially consisting of grids, clouds, stand-alone machines, clusters, desktop grids, mobile devices, and supercomputers, possibly with accelerators such as GPUs. One striking example of applications that can benefit greatly of Jungle Computing Systems are Multi-Model / Multi-Kernel simulations. In these simulations, multiple models, possibly implemented using different techniques and programming models, are coupled into a single simulation of a physical system. Examples include the domain of computational astrophysics and climate modeling. In this paper we investigate the use of Jungle Computing Systems for such Multi-Model / Multi-Kernel simulations. We make use of the software developed in the Ibis project, which addresses many of the problems faced when running applications on Jungle Computing Systems. We create a prototype Jungle-aware version of AMUSE, an astrophysical simulation framework. We show preliminary experiments with the resulting system, using clusters, grids, stand-alone machines, and GPUs. [ABSTRACT FROM PUBLISHER]