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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]

The Jiangmen Underground Neutrino Observatory (JUNO) [1] is a multipurpose neutrino experiment and the determination of the neutrino mass hierarchy is its primary physics goal. JUNO is going to start data taking in 2024 and plans to use distributed computing infrastructure for the data processing and analysis tasks. The JUNO distributed computing system has been designed and built based on DIRAC [2]. Since last year, the official Monte Carlo (MC) production has been running on the system, and petabytes of massive MC data have been shared among JUNO data centers through this system. In this paper, an overview of the JUNO distributed computing system will be presented, including workload management system, data management, and condition data access system. Moreover, the progress of adapting the system to support token-based AAI [3] and HTTP-TPC [4] will be reported. Finally, the paper will mention the preparations for the upcoming JUNO data-taking. [ABSTRACT FROM AUTHOR]

Advances in cloud computing technology coupled with increasing volumes of data has driven the growth and differentiation of cloud-based solutions in teaching and learning. The cloud computing industry has matured over the past decade and the number of publications steadily rose, to build on the maturity of the field researchers investigating cloud computing research in the mobile teaching and learning domain need to be cognisant of the state of the art. The objective of this paper is to analyse the available literature in the field of cloud computing for mobile teaching and learning to identify the main categories of research, the prevalent methodologies and research gaps, and then integrate the findings in a conceptual framework representing the current state of the field in terms of research opportunities. A systematic mapping study on relevant publications in journals and conferences was conducted. Mapping studies are a suitable method for structuring a research field concerning research questions about contents, methods and trends in the available publications. A systematic literature review and mapping was used to select 107 articles from a total of 21 822 publications in five prominent databases, namely ACM, ERIC, IEEE, Google Scholar and Springer. The analysis was done in October 2017 on papers published between 2013 and 2017. The contribution is to classify existing work and suggest future opportunities based on a systematic mapping of mobile cloud computing (MCC) for teaching and learning research. The analysis provides an overview of the field in terms of what is researched, how that is researched and where the future research contributions may lie. The findings are integrated to present a non-prescriptive, conceptual framework on mobile cloud computing research for teaching and learning. Researchers can use the proposed framework as a point of reference in starting or aligning their own projects and establishing where future research opportunities exist. [ABSTRACT FROM AUTHOR]

In recent years, advanced and complex analysis workflows have gained increasing importance in the ATLAS experiment at CERN, one of the large scientific experiments at LHC. Support for such workflows has allowed users to exploit remote computing resources and service providers distributed worldwide, overcoming limitations on local resources and services. The spectrum of computing options keeps increasing across the Worldwide LHC Computing Grid (WLCG), volunteer computing, high-performance computing, commercial clouds, and emerging service levels like Platform-as-a-Service (PaaS), Container-as-a-Service (CaaS) and Function-as-a-Service (FaaS), each one providing new advantages and constraints. Users can significantly benefit from these providers, but at the same time, it is cumbersome to deal with multiple providers, even in a single analysis workflow with fine-grained requirements coming from their applications' nature and characteristics. In this paper, we will first highlight issues in geographically-distributed heterogeneous computing, such as the insulation of users from the complexities of dealing with remote providers, smart workload routing, complex resource provisioning, seamless execution of advanced workflows, workflow description, pseudointeractive analysis, and integration of PaaS, CaaS, and FaaS providers. We will also outline solutions developed in ATLAS with the Production and Distributed Analysis (PanDA) system and future challenges for LHC Run4. [ABSTRACT FROM AUTHOR]

Since 2017, the Worldwide LHC Computing Grid (WLCG) has been working towards enabling token-based authentication and authorization throughout its entire middleware stack. Following the initial publication of the WLCG Token Schema v1.0 in 2019, OAuth2.0 token workflows have been integrated across grid middleware. There are many complex challenges to be addressed before the WLCG can be end-to-end token-based, including not just technical hurdles but also interoperability with the wider authentication and authorization landscape. This paper presents the status of the WLCG coordination and deployment work, and how it relates to software providers and partner communities. The authors also detail how the WLCG token transition timeline has progressed, and how it has changed since its publication. [ABSTRACT FROM AUTHOR]

The ATLAS experiment at the LHC utilizes complex multicomponent distributed systems for processing (PanDA WMS) and managing (Rucio) data. The complexity of the relationships between components, the amount of data being processed and the continuous development of new functionalities of the critical systems are the main challenges to consider when creating monitoring and accounting tools able to adapt to this dynamic environment in a short time. To overcome these challenges, ATLAS uses the unified monitoring infrastructure (UMA) provided by CERN-IT since 2018, which accumulates information from distributed data sources and then makes it available for different ATLAS distributed computing user groups. The information is displayed using Grafana dashboards. Based on the information provided, they can be grouped as "data transfers", "site accounting", "jobs accounting" and so on. These monitoring tools are used daily by ATLAS members to spot and fix issues. In addition, LHC Run 3 required the implementation of significant changes in the monitoring and accounting infrastructure to collect and process data collected by ATLAS during the LHC run. This paper describes the recent enhancements to the UMA-based monitoring and accounting dashboards. [ABSTRACT FROM AUTHOR]

Cloud Computing (CC) has gained a tremendous popularity in past few years. The concept of distributed systems in computing network has been reached to a large scale. To implement the cloud environment efficiently, several challenges are to be faced. This paper elaborates the concept of Cloud Computing and throws light on the various challenges in the way of successful implementation of Load Balancing (LB) in cloud computing environment. LB is the major concern in CC environment which imbalances the robustness of the cloud computing system. This paper illustrates the need of load balancing and explores the available techniques of load balancing. The paper also concentrates on the requirement of fault tolerance (FT) in CC and discusses various FT methods. In the last, comparative analysis between present load balancing techniques is represented. [ABSTRACT FROM AUTHOR]

In today's world, the notion of virtualization is the essential technology employed in cloud computing. This virtualization technology may run many operating systems with complete resources on a single piece of hardware. Furthermore, the virtualization innovation has restricted security capabilities for verifying total territorial cloud circumstances. This paper presents another security examination way for distinguishing the attack in the virtualized framework of distributed computing. The paper focuses on extracting accurate data from the organization qualities to choose the presence of assault in the virtualized framework. In this perspective, the projected work has 2 significant stages Feature extraction (I) and Classification (II). In lieu of the fact that the proposed technique is a "Bigdata" based problem on qualities like volume, velocity, variety, value, and veracity, it is wanted to utilize Python's multi-process framework to isolate the add up to Information proper and facilitate the process. The feature extraction happens for each isolated data, and features are extracted from Mutual information-based features. Finally, the characteristics obtained from the multi-functions, are passed through the classification process to detect an attack on the cloud. The presence of an attack in the network is detected using optimized DBN. To improve the detection accuracy, the Deep Belief Network(DBN) weights will be optimally tuned using a whale optimization algorithm[2] that's helps in DBN learning. Then the proposed model performance is compared with the traditional method like SVM, naïve Bayes, k nearest neighbors on various parameters like accuracy, precision, sensitivity recall and f1-score. [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]

This paper discusses the potentiality of virtual reality to support the innovation process and to foster the innovation culture of globally distributed teams in industry. We describe learnings from several case studies of using multi- user virtual reality collaborative environment during product development process in a large multi-national manufacturing company. We discuss how to involve experts with diverse backgrounds and cultures in innovation activities through reimagined collaboration. We also discuss the benefits and limitations of VR for industrial research, development, and innovation process. The results show that multi-user VR enhances the innovation capabilities of distributed teams. Furthermore, a hybrid setup, where some participants join via desktop, also increases the level of understanding and depth of collaboration, thus fostering innovations. A hybrid multi-user platform enables larger participation of subject matter experts during the innovation process, which helps to identify and remove barriers in the implementation and otherwise improves the quality of innovations. [ABSTRACT FROM AUTHOR]

The message passing interface (MPI) application is a parallel application that uses different communication routines mixed with computation to solve the problem. When executing this application over a heterogeneous platform will lead to undesired idle times, according to the synchronization between nodes. In other words, this may cause an imbalance of computation in the system. However, the increase in the slack time between the nodes wasting the computing powers of the computing nodes. In this paper, a dynamic load balancing algorithm is applied to parallel Jacobi method is proposed. It reduces the slack times depending on some gathered information from a parallel application. The obtained results show that it improved the execution time of an application by 29% compared to the original version of the parallel application. [ABSTRACT FROM AUTHOR]

In today's world Cloud computing is perhaps the most utilized technology. Distributed computing offers web administrations, information stockpiling and computing assets over the web effortlessly. Cloud computing permits clients to get to the IT assets anyplace and whenever on a compensation for every utilization premise. As the interest for distributed computing develops quickly, the traffic additionally increases. There are two answers for this issue, one is to upgrade a single server to a high-performance server but upgraded server may also overload soon and second is multi server (gathering of workers). Multi server arrangement is adaptable and practical. While making a group of servers, the issue is Load adjusting. Load balancing is one of the critical issues in cloud computing. Load balancing is an interaction of parting the unique responsibility and dispersing the responsibility among every one of the hubs with the end goal that no hub in the cloud climate is overloaded/under loaded or inactive. Load balancing in Cloud computing is utilized to improve the proficiency and utilizing resources effectively. This journal paper objective is on investigation on cloud load balancing, Load balancing methods, Load balancing algorithms. [ABSTRACT FROM AUTHOR]

Customization is one of the known challenges in traditional ERP systems. With the advent of Cloud ERP systems, a question of determining the state of such systems regarding customization and configuration ability arises. As there are only a few literature sources partially covering this topic, a more comprehensive and systematic literature review is needed. Thus, this paper presents a literature review performed in order to give an overview of reported research on "Cloud ERP Customization" topic performed in the last 5 years. In two search iterations, a total of 32 relevant papers are identified and analyzed. The results show that several dominant research trends are identified along with 12 challenges and issues. Additionally, based on the results, the possible future researches are proposed. [ABSTRACT FROM AUTHOR]

This paper proposes a task scheduling strategy DEACO based on the combination of Differential Evolution (DE) and Ant Colony Optimization (ACO), aiming at the single problem of optimization objective in cloud computing task scheduling, this paper combines the shortest task completion time, cost and load balancing. DEACO uses the solution of the DE to initialize the initial pheromone of ACO, reduces the time of collecting the pheromone in ACO in the early, and improves the pheromone updating rule through the load factor. The proposed algorithm is simulated on cloudsim, and compared with the min-min and ACO. The experimental results show that DEACO is more superior in terms of time, cost,and load. [ABSTRACT FROM AUTHOR]

Modern numerical modeling experiments and data analytics problems in various fields of science and technology reveal a wide variety of serious requirements for distributed computing systems. Many scientific computing projects sometimes exceed the available resource pool limits, requiring extra scalability and sustainability. In this paper we share the experience and findings of our own on combining the power of SLURM, BOINC and GlusterFS as software system for scientific computing. Especially, we suggest a complete architecture and highlight important aspects of systems integration. [ABSTRACT FROM AUTHOR]

In the last few years, the world of Internet communications as well the application's requirements has changed significantly in terms of speed, quality of service, computing, storing, security, energy consumption, availability, and etc. Everything gets connected to the Internet and accessible from anywhere, anytime. The traditional networks based on distributed control and network transport protocols are not enough flexible, and are hard to be managed. To overwhelm this problem, the software defined networking (SDN) imposes a new paradigm to split the control plane from the data plane thus centralizing the network resource management. Nowadays, SDN becomes one of the most emerging and fast developing technology. In this paper, key features of SDN architecture such as network abstraction and open interfaces (Eastbound, Westbound, Northbound and Southbound) are presented and analyzed. A lot of researchers and manufacturers are involved in the process of developing a variety of solutions for these interfaces some of them already standardized. As a result, a number of SDN platforms exist. Their features are compared and some recommendations are also given. The most important feature of the SDN technology is concentrated in the control plane which defines the way how the network resources in the infrastructure layer are managed, based on the service (application) requirements. This function is accomplished by the SDN controller and its Southbound interface. OpenFlow is one of the most popular protocol that provides a standard Southbound interface for data plane resources programming. The OpenFlow as well as its alternatives are also considered in this paper. The survey and analyses of SDN technology presented might be considered as a summary and might be used to better understand SDN. [ABSTRACT FROM AUTHOR]

The token-based certification method is spreading in the distributed computing system of high energy physics. More and more software and middleware are supporting tokens as one of the certification methods. As an example, WLCG has upgraded all the services to support WLCG tokens [1]. In IHEP (Institute of High Energy Physics in China), the Kerberos [2] token has been used as the main certification method in the local cluster. Naturally, it is selected as the certification method in the distributed computing system. In this case, a set of toolkits were developed or introduced to use Kerberos tokens in the distributed computing system, including token producer, token repository, token transfer and token client engine. The token producer is responsible for creating a token and publishing the token file to the token repository. The token repository stores all the latest token files and a refresh service periodically renews the lifetime of those tokens stored in the token repository. The token transfer brings the token file to the worker node. The token client engine initializes the token environment and renews the token's lifetime on the worker node. With these toolkits, the jobs can run in any worker node in any site and use the Kerberos token to access other services, such as EOS [3] and the XRootd [4] proxy service. In IHEP, the Kerberos toolkit has been deployed in the distributed computing system. Currently, three experiments (LHAASO [5], BES [6] and HERD [7]) are using Kerberos tokens to remotely access the data in EOS or Lustre [8]. [ABSTRACT FROM AUTHOR]

Array operations are one of the most concise ways of expressing common filtering and simple aggregation operations that are the hallmark of a particle physics analysis: selection, filtering, basic vector operations, and filling histograms. The High Luminosity run of the Large Hadron Collider (HL-LHC), scheduled to start in 2026, will require physicists to regularly skim datasets that are over a PB in size, and repeatedly run over datasets that are 100's of TB's – too big to fit in memory. Declarative programming techniques are a way of separating the intent of the physicist from the mechanics of finding the data and using distributed computing to process and make histograms. This paper describes a library that implements a declarative distributed framework based on array programming. This prototype library provides a framework for different sub-systems to cooperate in producing plots via plug-in's. This prototype has a ServiceX data-delivery sub-system and an awkward array sub-system cooperating to generate requested data or plots. The ServiceX system runs against ATLAS xAOD data and flat ROOT TTree's and awkward on the columnar data produced by ServiceX. [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]

Data is an important asset that needs to be safeguarded. Loss of data leads to direct financial losses. Storing data has evolved to meet the increasing needs of companies and individuals. The traditional storage approach, a specialized storage box no longer works for technical and economical reasons. Need is not for faster drives or networks but need is for a new approach for satisfying current demands for storing data. "Storing data on many standard servers but behave as one storage system although data is distributed between these servers." This Distributed Storage System (DSS) is an advanced form of the "Storage" concept. Data security is a major issue for businesses and organizations today. Ensuring that your data is secure is becoming more important every day and vital to business operations. Protection of sensitive data is end goal of many IT security measures. The agenda is to ensure the avoidance of theft and protect privacy. Hence it comes to everyone's advantage to ensure sensitive data protection. Measures are taken to keep data secure. Measures are like having regular backups of documents, protecting against attacks, using authentications for accessing data and so on. This paper speaks about data storage over distributed system ensuring data security with Erasure codes, a new technology ensuring data security. Here Erasure codes are a part of Repair tree algorithm. Repair tree algorithm helps to get the data of interest over distributed storage system. [ABSTRACT FROM AUTHOR]

Distributed Computing has been catching the eyes of researchers and enthusiasts lately, because of its ability to handle the increasing workload on systems/machines. Instead of updating a single system again and again to improve workload handling, another system can be added to handle the escalating workload. The agenda behind distributed computing is for a community of computers/systems to work together at the back end while it appears as one for the end-user. Solitary systems operating together in such groups run at the same time and allow the entire system to continue processing even though one or more of them fails. Distributed computing can be used by researchers and students in the form of an application suite to train their AI and ML models and render graphics. Not all systems/computers out there are qualified to perform high processing tasks such as AI / ML model training or graphics rendering. So instead of spending hundreds and thousands on buying a system with the ability to process heavy tasks, this paper aims to provide a suite that allows neighbouring systems to act as nodes and share workloads between available nodes that can distribute tasks among themselves and processes simultaneously without pressing any pockets. [ABSTRACT FROM AUTHOR]

There is a considerable volume of data made accessible to people of the modern digital age, thanks to the vast array of innovations and the enormous amount of content available over the Internet. Consequently, a disorder called "information overload" develops a mental confusion due to more information. As a result, it is quite difficult for an individual to find for and access information to make informed decisions. The amount of information that contains each unstructured and structured data and its information, has big heavily in recent days. Recommendation systems (RS) are becoming more widespread, as they are being used everywhere in the E-commerce sector and analysing each qualified and validated data to have the best recommendation. To process distributed data in this project, a large framework known as jupyter notebook is used. Unlike previous mapping features, jupyter can accommodate redundant algorithms, dynamic algorithms, and time intervals that are stripped down. Because of the rise of the internet, a helpful technology known as counselled method has evolved into a cost-effective application for forming consumer suggestions. Today, many collective recommender programmes have succeeded in fields such as film, music, and internet applications. However, there are several options to make them more useful RS. This paper implements a combined similarity-based substitute for an item-based collective filtering technique, as well as a solution to the most common cold start problem. A series of tests show that the new approach can make better recommendations than the traditional item-based collective filtering method. [ABSTRACT FROM AUTHOR]

In this paper we will describe the LHAASO distributed computing system based on virtualization and cloud computing technologies. Particularly, we discuss the key points of integrating distributed resources. A solution of integrating cross-domain resources is proposed, which adopt the Openstack+HTCondor to make the distributed resources work as a whole resource pool. A flexible resource scheduling strategy and a job scheduling policy are presented to realize the resource expansion on demand and the efficient job scheduling to remote sites transparently, so as to improve the overall resource utilization. We will also introduce the deployment of the computing system located in Daocheng, the LHAASO observation base using cloud-based architecture, which greatly helps to reduce the operation and maintenance cost as well as to make sure the system availability and stability. Finally, we will show running status of the system. [ABSTRACT FROM AUTHOR]

As a well established, large-scale distributed storage system, dCache is used to manage and serve huge amounts of data collected by high energy physics, astrophysics and photon science experiments. Based on a microservices-like architecture, dCache is built as a modular distributed system, where each component provides a different core functionality. These services communicate by passing serialized messages to each other, a core behavior whose performance properties can consequently affect the entire system. This paper compares and evaluates different data serialization protocols in computer science with the objective of replacing and improving upon Java Object Serialization (JOS), which has increasingly presented itself as no longer being sufficiently performant for encoding messages. The criteria for choosing a new framework are collected, analyzed and formalized. The primary motivation for replacing Java serialization for encoding dCache messages is increasing the general speed of message-passing and thereby reducing the round-trip time for user requests. Emphasis is also placed on schema evolution capabilities and framework usability. Approaches for generalizing (de)serialization speed and size measurements based on data structure complexity are introduced, criteria for measuring documentation, learning curve, maintainability and introduction effort are defined. Finally, several selected serialization protocols are evaluated and compared accordingly, concluding with a recommendation for a suitable JOS replacement. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is an arrangement of advancements that are between disciplinary. It is utilized to have compelling combination of both physical and computerized things. With IoT physical things can have personal virtual identities and participate in distributed computing. Realization of IoT needs the usage of sensors based on the sector for which IoT is integrated. For instance, in healthcare domain, IoT needs to have integration with wearable sensors used by patients. As sensor devices produce huge amount of data, often called big data, there should be efficient routing protocols in place. To the extent remote systems is worried there are some current protocols, for example, OLSR, DSR and AODV. It additionally tosses light into Trust based routing protocol for low-power and lossy systems (TRPL) for IoT. These are broadly utilized remote directing protocols. As IoT is developing round the corner, it is basic to investigate routing protocols that and evaluate their execution regarding throughput, end to end delay, and directing overhead. The execution experiences can help in settling on very much educated choices while incorporating remote systems with IoT. In this paper, we analyzed different routing protocols and their performance is compared. It is found that AODV showed better performance than other routing protocols aforementioned. [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]

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]

The recent period of image gathering process cannot be manipulated effectively on the system because of huge set of images and due to the high expenditure costs for the algorithms used to process on the recent images. Therefore, the processing of an image needs the help of distributed computing concepts. The subject named distributed computing is considered to be a complex area which requires sound technical learning and cannot be utilized by the research conducted so as to develop on the algorithms used for image processing; all the existing works are more concentrated on the optimization process used for processing the images with the aid of algorithms like-wise completely ignoring the inherent deficiency of an individual node-based processing method, where the quality of the image looks poor. In order to rectify this problem, static big image is processed by Image Cloud Processing (ICP) is a technique, which is proposed on modern days, which contains 2 different parts of processing methods i.e. SICP and DICP, so as to obtain the dynamic input of the static big image. The techniques used in the processing of small images are SICP and DICP that limits the capabilities of processing and the even images are broken down, at the time of cluster failure, when it needs to process the clusters on the timely basis. In order to rectify this problem, this paper proposes the design of Swallow Swarm Fuzzy C-Means Algorithm (SSFCM) based distributed processing framework for the clustering of images because of their poor requirements for computations. This work includes pre-processing of images with the help of image enhancement using Histogram Equalization (HE) and Adaptive Median Filtering (AMF). So as to accomplish SICP, 2 novel representations named Pure-image and Big-image are modeled to combine and execute by means of the algorithm named SSFCM-Map Reduce (SSFCM-MR) to attain large number of optimized configurations. In the formulated work, SSFCM algorithm is implemented through the Reduce Cluster to deal with the issues associated with clustering of a large image. DICP is implemented by means of a technique called as processing in parallel procedural working along with the traditional methods used for processing using the distributed system. The important feature of design in SSFCM-MR is the usage of affluent resources used for resources, given by the distributed system in the implementation of efficient parallel processing techniques. The output of the experiments of Corel-10k dataset are chosen to validate the potential of the formulated SSFCM-MR layout over the conventional state of arts techniques, in terms of efficiency and the standard of results. [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]

The opportunity of using Cloud resources on a pay-as-you-go basis and the availability of powerful data centers and high bandwidth connections are speeding up the success and popularity of Cloud systems, which is making on-demand computing a common practice for enterprises and scientific communities. The reasons for this success include natural business distribution, the need for high availability and disaster tolerance, the sheer size of their computational infrastructure, and/or the desire to provide uniform access times to the infrastructure from widely distributed client sites. Nevertheless, the expansion of large data centers is resulting in a huge rise of electrical power consumed by hardware facilities and cooling systems. The geographical distribution of data centers is becoming an opportunity: the variability of electricity prices, environmental conditions and client requests, both from site to site and with time, makes it possible to intelligently and dynamically (re)distribute the computational workload and achieve as diverse business goals as: the reduction of costs, energy consumption and carbon emissions, the satisfaction of performance constraints, the adherence to Service Level Agreement established with users, etc. This paper proposes an approach that helps to achieve the business goals established by the data center administrators. The workload distribution is driven by a fitness function, evaluated for each data center, which weighs some key parameters related to business objectives, among which, the price of electricity, the carbon emission rate, the balance of load among the data centers etc. For example, the energy costs can be reduced by using a "follow the moon" approach, e.g. by migrating the workload to data centers where the price of electricity is lower at that time. Our approach uses data about historical usage of the data centers and data about environmental conditions to predict, with the help of regressive models, the values of the parameters of the fitness function, and then to appropriately tune the weights assigned to the parameters in accordance to the business goals. Preliminary experimental results, presented in this paper, show encouraging benefits. [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 widely available smart-phones as well as other mobile devices can be utilized as valuable compute resources with the support of the powerful and elastic paradigm of cloud computing. This is possible by performing computations of a limited scale on mobile devices and migrating or offloading complex services to the cloud. Use of mobile devices for computations will enable a plethora of applications to execute on mobile devices alone without dependence on static infrastructure. However, this entails the cost of transfer of computations to the cloud along with the cost of cloud computing resources. This paper formulates computation offloading as an optimization problem and utilizes nature-inspired approach of Grey Wolf Algorithm (GWO) to achieve near-optimal solutions. Results precisely depict that although the best cost solutions are attained by the brute force technique but the number of computations is significantly higher as compared to Grey Wolf Algorithm. Moreover with the small increase in number of tasks, there is exponential increase in number of computations. Considering these tradeoffs, its more appropriate to use nature-inspired algorithms for computation offloading in a mobile cloud computing environment. [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]

The paper is devoted to the study of the effectiveness of using cloud computing in a corporate cloud on the example of reachable sets calculation for control systems. The paper describes a numerical method for constructing the reachable set of a control system, which belongs to the class of grid methods that make it possible to construct such sets approximately. This method was used to develop a parallel algorithm for reachable sets calculation on the basis of which a multiplatform program in C language was developed. Applying this program, which is based on the OpenMP parallel computing technology, a number of numerical experiments were performed on a virtual server located in the corporate cloud of the Ural Branch of the Russian Academy of Sciences. The results of these experiments allowed to evaluate the effectiveness of cloud computing with respect to the problem of reachable sets calculation. [ABSTRACT FROM AUTHOR]

Task scheduling is a NP puzzle. Its algorithm is an important research direction. This paper proposes a task scheduling algorithm based on real-coded immune algorithm by studying and analyzing task scheduling models and immune algorithms existed. This paper discusses the coding method, the generation and update of population, the update of memory cells and the values of partial parameters. This paper explores the affinity function and the concentration function. In the end, this new algorithm is implemented in the software VC++, and is proven validity and feasibility by comparing and analyzing examples [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as a next generation high-performance computing infrastructure. This paper presents a Cloud computing infrastructure on clusters that aims to offer a virtual compute resource manager for compute nodes of clusters. Thus users can access and deploy theirs applications from anywhere on demand in transparent via web interface. The paper mainly introduces the basic ideas of the infrastructure, and discusses some techniques such as the method for efficient scheduling to obtain optimum job throughput, job scheduling using user and job resource attributes and so on. [ABSTRACT FROM AUTHOR]

Today there is a discrepancy between the available resolutions of modern screens and the ability of standard windows applications to automatically adapt their user forms to those resolutions (resizing forms). Another problem is the justified aspiration and need of individual users of client/server applications to dynamically and permanently ada pt user forms of the application to their personal creativity and needs, in accordance with the available options of the PC's screen resolution. The mentioned problems are reflected as a flaw in a number of the existing business windows applications developed in various development systems, and they may result in pronounced dissatisfaction of users. This paper presents a model of a solution to the mentioned problem. It is based on the addition of a separate object control (class) to each form of the client/server windows application. That class enables dynamic and permanent adaptation of every form and all of its objects to the active resolution of the PC screen in the business network, and also the recording in the data base of the form coordinates is adapted on the PC for each user of a form of the client/server application. In fact, it is a system of dynamic generation of the personal user interface of the application. As a result, an entirely new and extremely useful option has been created for the users of standard client/server windows applications.. The model uses a modified, generally licensed object class (http://www.activewebsoftwares.com, WebIntel@Webintel.net, VFP Form Resizer Source Code V-3) that is developed for the MS Visual FoxPro 7.0 - 9.0 development system. The implementation of the mentioned model in the MS Visual FoxPro 9.0. development system in this particular type of client/server windows applications provides a high-quality and new visual dimension, and also significantly contributes to users' satisfaction. In view of the above, this paper is aimed at promoting the concept of the need to develop a generally closed AktiveX Controls of the class that will allow dynamic management of the user interface that may be integrated into a variety of OS Windows development systems intended for the development of client/server business applications, which would solve the above described problem. [ABSTRACT FROM AUTHOR]

Nowadays, the air quality is considered a key point, and its monitoring is not only suggested but it is even required in many countries. Since traditional standard monitors for air quality are very expensive, the use of a low-cost distributed network of sensors represents a valid complementary approach. This paper discusses the benefits of a distributed approach and analyzes the main elements that should be taken into account during the design of a distributed system for the air quality monitoring. This paper aims at representing a valuable aid for researchers and practitioners interested in the topic. [ABSTRACT FROM AUTHOR]

With technical linkages to Grid Computing, Utility Computing, and Distributed Computing, Cloud Computing is fast gaining traction. A cloud service provider, such as Amazon Web Service, IBM, Google Application, Microsoft Azure, and others, provides users with data storage and development applications that they can access from anywhere in the world. With the help of cloud service providers, data is stored and retrieved on a remote server. As data is sent to a remote internet server, maintaining security is paramount. Cloud service users are constantly concerned about data loss, security risks, and service interruptions. With the introduction of machine learning methodologies, learning-based security applications have recently gained interest in books. The most difficult aspect of these systems, however, is gathering real-time and impartial data. We discuss numerous data security approaches, their benefits, data protection difficulties, ongoing data security problems, smart data security methods, data-related security challenges in the cloud-based environment, and solutions that must be solved in this study. [ABSTRACT FROM AUTHOR]

In this time and age, ecological awareness rules to everybody. Digitalization generally denotes a shift to more energy-effective methods of activity. Regardless of that, arrangement and appropriation of cloud-based information include an enormous measure of force cost. Around there, creating distributed computing projects which enhance the utilization of efficient power energy while likewise saving the creation costs, makes certain to be a colossal hit among various business ventures. The point is to decrease the force utilization throughout the worker organization measure. The backbone of this venture will be founded on at the organization stage since this will start subbing the customary energy with environmentally friendly power. This cycle of reasonable energy combination with the information workers won't just diminish the carbon impressions generally yet in addition will bring about a drop in the absolute expense of responsibility for distributed storage also. The accessibility of efficient power energy stays a critical factor in the acknowledgment of this task. [ABSTRACT FROM AUTHOR]

CMS is tackling the exploitation of CPU resources at HPC centers where compute nodes do not have network connectivity to the Internet. Pilot agents and payload jobs need to interact with external services from the compute nodes: access to the application software (CernVM-FS) and conditions data (Frontier), management of input and output data files (data management services), and job management (HTCondor). Finding an alternative route to these services is challenging. Seamless integration in the CMS production system without causing any operational overhead is a key goal. The case of the Barcelona Supercomputing Center (BSC), in Spain, is particularly challenging, due to its especially restrictive network setup. We describe in this paper the solutions developed within CMS to overcome these restrictions, and integrate this resource in production. Singularity containers with application software releases are built and pre-placed in the HPC facility shared file system, together with conditions data files. HTCondor has been extended to relay communications between running pilot jobs and HTCondor daemons through the HPC shared file system. This operation mode also allows piping input and output data files through the HPC file system. Results, issues encountered during the integration process, and remaining concerns are discussed. [ABSTRACT FROM AUTHOR]

Although the National Science Foundation (NSF) funds approximately 24% of basic research conducted in U.S. colleges and universities 1, we know little about how NSF funding decisions have shaped the current research terrain. For instance, what new research areas have emerged and how have faculty collaborated over time? The Deep Insights Anytime, Anywhere (DIA2) project was precipitated by the need to better understand these issues and translate them into easy to understand insights for the STEM education community. We were also motivated to understand how research outcomes, particularly of STEM education projects, impact STEM education practice. As part of this project, the DIA2 project team has designed an information and visualization portal (http://www.dia2.org) that allows researchers and scientists to browse and search public data from NSF to understand the research terrain (including information about research on specific topics and researchers active in the area). There are many challenges associated with developing and using such a cyberinfrastructure, but also many potential advantages for practitioners, researchers, and policymakers. In this paper we discuss the research opportunities provided by DIA2 and present the research framework guiding the DIA2 project--a description of the three major themes/areas of research for the study. The paper summarizes the research questions and research activities corresponding to each of the themes, presents next steps, and based on our findings, highlights the value of DIA2 to members of the STEM education community. These concentrated efforts can not only help us better understand the impact and landscape of STEM education research, but this study can also serve as a framework for other large scale cyber-enabled research projects. [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]

The method of two-phase locking (2PL) is still used in concurrency access in the global transaction management. It has its advantages in distributed database management systems (DDBMS), conflicted events. The paper views modeling algorithms of Centralized, Distributed and Primary copy Two-version Two-phase locking. The presented algorithms are models of the corresponding variants of the 2PL protocols for DDBMS using two versions. As in concurrency control of distributed transactions in DDBMS deadlocks are possible to appear, one of the ways for their preventing is the usage of two-version architecture of the data in a distributed database (DDB). The lack of algorithms published as well as results from investigations of such algorithms imposes the development of simulation models and their investigation. In detail the paper considers such models of Two-version two-phase locking (2V2PL): Centralized 2V2PL, Primary copy 2V2PL and Distributed 2V2PL. The simulations of the 2V2PL algorithms and the results reported are achieved by the system of imitation modeling GPSS Studio. GPSS Studio is the latest extension (shell) of the GPSS World Imitation Modeling Environment with the following new features: automatic text alignment; contextual hints; general management of the study (models, experiments and results) through a special project structure; Using libraries from pre-created standard composite or elementary blocks; Design of interactive forms for analysis of modeling results; Automatically create a virtual report of the study, etc. The main characteristics of transaction processing in distributed database management systems such as throughput, response time and probability service are presented. [ABSTRACT FROM AUTHOR]

For a significant reduction of time spent on research to obtain meaningful results in scientific fields, the computing resources of the Joint Institute for Nuclear Research (JINR) and some organizations of its Member States were integrated into a distributed information and computing environment (DICE). The technical possibility for running tasks in the environment was implemented for users of the BM@N, MPD and Baikal-GVD collaborations. The resources not occupied by computational tasks within the main scientific fields of JINR are used to conduct studies on the SARS-CoV-2 virus that causes the COVID-19 disease. In addition, the paper gives a description of the DICE technical implementation, lists the participating organizations and provides some statistics on the use of their resources. [ABSTRACT FROM AUTHOR]

Choosing the appropriate architecture and technologies for a big data project is a difficult task, which requires extensive knowledge in both the problem domain and in the big data landscape. The paper analyzes the main big data architectures and the most widely implemented technologies used for processing and persisting big data. Clouds provide for dynamic resource scaling, which makes them a natural fit for big data applications. Basic cloud computing service models are presented. Two architectures for processing big data are discussed, Lambda and Kappa architectures. Technologies for big data persistence are presented and analyzed. Stream processing as the most important and difficult to manage is outlined. The paper highlights main advantages of cloud and potential problems. [ABSTRACT FROM AUTHOR]

In the present paper, the control design of Heating, Ventilation and Air Conditioning (HVAC) systems is investigated. In large-scale buildings - e.g. hotels or hospitals - the high dimension of the control design problem precludes a solution with reasonable computational effort. In this paper, a distributed control strategy is proposed, where interacting agents are operating sub-systems; interaction between these agents can ensure that an optimum solution can be obtained. A novel method to distributed control tis introduced based on data-driven modeling where the strategy is not based on explicit optimization, but on weighted learning of the control rules; two examples of the addressed system are formulated. A significant advantage of the proposed approach consists in minimal assumptions on the addressed system and the most significant disadvantage is the need of sufficiently rich data-sets. [ABSTRACT FROM AUTHOR]

Data acquisition systems are a key component for successful data taking in any experiment. The DAQ is a complex distributed computing system and coordinates all operations, from the data selection stage of interesting events to storage elements. For the High Luminosity upgrade of the Large Hadron Collider, the experiments at CERN need to meet challenging requirements to record data with a much higher occupancy in the detectors. The DAQ system will receive and deliver data with a significantly increased trigger rate, one million events per second, and capacity, terabytes of data per second. An effective way to meet these requirements is to decouple real-time data acquisition from event selection. Data fragments can be temporarily stored in a large distributed key-value store. Fragments belonging to the same event can be then queried on demand, by the data selection processes. Implementing such a model relies on a proper combination of emerging technologies, such as persistent memory, NVMe SSDs, scalable networking, and data structures, as well as high performance, scalable software. In this paper, we present DAQDB (Data Acquisition Database) — an open source implementation of this design that was presented earlier, with an extensive evaluation of this approach, from the single node to the distributed performance. Furthermore, we complement our study with a description of the challenges faced and the lessons learned while integrating DAQDB with the existing software framework of the ATLAS experiment. [ABSTRACT FROM AUTHOR]

High Performance Computing (HPC) supercomputers are expected to play an increasingly important role in HEP computing in the coming years. While HPC resources are not necessarily the optimal fit for HEP workflows, computing time at HPC centers on an opportunistic basis has already been available to the LHC experiments for some time, and it is also possible that part of the pledged computing resources will be offered as CPU time allocations at HPC centers in the future. The integration of the experiment workflows to make the most efficient use of HPC resources is therefore essential. This paper describes the work that has been necessary to integrate LHCb workflows at a specific HPC site, the Marconi-A2 system at CINECA in Italy, where LHCb benefited from a joint PRACE (Partnership for Advanced Computing in Europe) allocation with the other Large Hadron Collider (LHC) experiments. This has required addressing two types of challenges: on the software application workloads, for optimising their performance on a many-core hardware architecture that differs significantly from those traditionally used in WLCG (Worldwide LHC Computing Grid), by reducing memory footprint using a multi-process approach; and in the distributed computing area, for submitting these workloads using more than one logical processor per job, which had never been done yet in LHCb. [ABSTRACT FROM AUTHOR]

The WLCG Web Proxy Auto Discovery (WPAD) service provides a convenient mechanism for jobs running anywhere on the WLCG to dynamically discover web proxy cache servers that are nearby. The web proxy caches are general purpose for a number of different http applications, but different applications have different usage characteristics and not all proxy caches are engineered to work with the heaviest loads. For this reason, the initial sources of information for WLCG WPAD were the static configurations that ATLAS and CMS maintain for the Conditions data that they read through the Frontier Distributed Database system, which is the most demanding popular WLCG application for web proxy caches. That works well for use at traditional statically defined WLCG sites, but now that usage of commercial clouds is increasing, there is also a need for web proxy caches to dynamically register themselves as they are created. A package called Shoal had already been created to manage dynamically created web proxy caches. This paper describes the integration of the Shoal package into the WLCG WPAD system, such that both statically and dynamically created web proxy caches can be located from a single source. It also describes other improvements to the WLCG WPAD system since the last CHEP publication. [ABSTRACT FROM AUTHOR]