Scalable Environment Research Articles

Critique: YEASTsim - A Matlab-based simulator for teaching process control in fed-batch yeast fermentations

One of the primary challenges in chemical engineering education lies in the practical application of theoretical knowledge. Contemporary society demands chemical engineers who are not only adept at critical thinking but also proficient in practical problem-solving. Historically, acquiring such practical experience has been heavily dependent on laboratory experiments. However, with the advent of advanced technology, there are now more accessible and varied opportunities for experiential learning. Simulation-based learning tools have proven to be powerful complements to traditional laboratory experiments. These tools enable students to investigate complex chemical processes, test hypotheses, and develop practical skills in a safe, cost-effective, and scalable environment. This critique aims to evaluate the educational potential of the YEASTsim simulator, positioning it as an interactive and innovative tool to enhance the study of core subjects such as Chemical Reaction Engineering, a fundamental component of chemical engineering practice.

Transforming business scalability and operational flexibility with advanced cloud computing technologies

In the rapidly evolving landscape of digital transformation, cloud computing has emerged as a pivotal technology enabling businesses to achieve unparalleled scalability and operational flexibility. This review explores the transformative impact of advanced cloud computing technologies on business operations, highlighting key innovations and their implications for organizational growth and efficiency. Cloud computing offers a dynamic and scalable environment where resources can be provisioned and managed on-demand, allowing businesses to respond swiftly to changing market conditions and customer demands. By leveraging cloud infrastructure, companies can scale their operations seamlessly without the need for significant upfront investments in physical hardware. This flexibility not only reduces capital expenditure but also enhances the ability to innovate and adapt in a competitive marketplace. Advanced cloud technologies, such as multi-cloud and hybrid cloud solutions, further augment operational flexibility by enabling organizations to optimize their IT environments. Multi-cloud strategies allow businesses to distribute workloads across multiple cloud providers, mitigating the risks associated with vendor lock-in and ensuring high availability and redundancy. Hybrid cloud solutions, which integrate on-premises infrastructure with public and private clouds, provide a balanced approach to managing sensitive data and workloads while benefiting from the scalability of the cloud. Moreover, cloud-native technologies like containerization and serverless computing have revolutionized application development and deployment. Containers encapsulate applications and their dependencies, ensuring consistency across different computing environments and facilitating rapid deployment. Serverless computing reviews the underlying infrastructure, allowing developers to focus solely on code, thus accelerating the development cycle and reducing operational overhead. The integration of advanced analytics and artificial intelligence (AI) with cloud computing further enhances business capabilities. Cloud platforms offer robust analytics tools and AI services that can process vast amounts of data in real-time, providing actionable insights and enabling predictive decision-making. This integration empowers businesses to optimize operations, improve customer experiences, and drive strategic initiatives with data-driven precision. In conclusion, advanced cloud computing technologies are instrumental in transforming business scalability and operational flexibility. By harnessing the power of the cloud, organizations can achieve greater agility, cost efficiency, and innovation, positioning themselves for sustained growth and competitive advantage in the digital age. As cloud technologies continue to evolve, their potential to redefine business operations and drive economic value will only expand, making cloud adoption a critical imperative for modern enterprises. Keywords: Operational Flexibility, Transforming, Business Scalability, Advanced, Cloud Computing Technologies.

Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation

Cloud computing has arisen as a shrewd and well known worldview for people and associations to work with the entrance and use of registering assets through the web.With the rapid growth of cloud computing technology, efficiently running big data applications within minimal time has become a significant challenge. In this dynamic and scalable environment, effective resource allocation and task scheduling of big data applications play pivotal roles in optimizing performance, enhancing efficiency, and ensuring cost-effectiveness. In environments involving remote computing, task scheduling is a crucial consideration. In order to effectively accomplish resource-optimal task scheduling and minimize overall task execution time, a novel technique called Multicriteria Generalized Regressive Neural Federated Learning (MGRNFL) is developed to address the particular issues in cloud systems. Tasks from several users arrive at the cloud server at the start of the procedure. The cloud server's job scheduler then uses Multicriteria Federated Learning to carry out resource-optimal task scheduling. A decentralized machine learning technique called federated learning (FL) enables model training across several tasks that are gathered from cloud computing customers. This decentralized approach primarily focuses on learning from datasets to obtain a global model by aggregating the results of local models. The proposed techniques involve two different steps: local training models and global aggregation models. In the local training model, the task scheduler determines the resource-optimal virtual machine in the cloud server using a Generalized Regression Neural Network (GRNN) based on multicriteria functions of the virtual machine, such as energy, memory, CPU, and bandwidth. Based on these objective functions, resource-efficient virtual machines are determined to schedule multiple user tasks. The locally updated models are then combined and fed into the global aggregation model. Calculated within the global aggregation model is the weighted total of locally updated findings. The algorithm iterates through this process till the maximum number of times. In order to schedule incoming tasks, the resource-optimal virtual machine is found. Various quantitative criteria are used for the experimental evaluation, including makespan, throughput in relation to the number of tasks, and task scheduling efficiency.

Cloud-Based Solutions for Video Streaming and Big Data Testing

In the era of digital transformation, cloud-based solutions have revolutionized the video streaming industry and big data testing methodologies. As consumer demand for high-quality, on-demand video content surges, traditional on-premises infrastructure often falls short in meeting scalability, performance, and cost-efficiency requirements. Cloud-based platforms, with their scalable resources and flexibility, have become essential in addressing these challenges. This paper explores the benefits and implementation strategies of cloud-based solutions for video streaming and big data testing, focusing on how these technologies enhance performance, scalability, and cost-effectiveness Video streaming services require a robust infrastructure capable of handling large volumes of data and delivering seamless user experiences across diverse devices and network conditions. Cloud-based video streaming solutions offer several advantages, including the ability to dynamically scale resources based on user demand, optimize content delivery through content delivery networks (CDNs), and provide a global reach without the need for extensive physical infrastructure. By leveraging cloud services, streaming platforms can reduce latency, enhance video quality, and improve overall user satisfaction. Traditional testing methods often struggle to cope with the complexity and volume of big data. Cloud-based testing platforms provide scalable environments where testers can simulate various data scenarios, execute performance tests, and analyze results with greater efficiency. The cloud's elasticity allows for on-demand provisioning of testing resources, making it easier to handle the dynamic nature of big data and conduct comprehensive testing without investing in expensive hardware.

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.

An extended Attribute-based access control with controlled delegation in IoT

Access control is one of the main concerns for securing the Internet of Things (IoT) due to its inherent features. It is particularly challenging to manage access policies and authorization (rights) propagation in a highly dynamic and scalable environment with a potentially unbound number of connected objects. This paper proposes an Attribute-Capability-Based Access Control (ACapBAC) system for the IoT. ACapBAC enables flexible and dynamic access control with fine-grained controlled delegation. The reliability of the proposed solution is highlighted through both efficiency and security analysis.

Temporal fusion transformer-based prediction in aquaponics

Aquaponics offers a soilless farming ecosystem by merging modern hydroponics with aquaculture. The fish food is provided to the aquaculture, and the ammonia generated by the fish is converted to nitrate using specialized bacteria, which is an essential resource for vegetation. Fluctuations in the ammonia levels affect the generated nitrate levels and influence farm yields. The sensor-based autonomous control of aquaponics can offer a highly rewarding solution, which can enable much more efficient ecosystems. Also, manual control of the whole aquaponics operation is prone to human error. Artificial Intelligence-powered Internet of Things solutions can reduce human intervention to a certain extent, realizing more scalable environments to handle the food production problem. In this research, an attention-based Temporal Fusion Transformers deep learning model was proposed and validated to forecast nitrate levels in an aquaponics environment. An aquaponics dataset with temporal features and a high number of input lines has been employed for validation and extensive analysis. Experimental results demonstrate significant improvements of the proposed model over baseline models in terms of MAE, MSE, and Explained Variance metrics considering one-hour sequences. Utilizing the proposed solution can help enhance the automation of aquaponics environments.

A Lightweight Blockchain-Based Remote Mutual Authentication for AI-Empowered IoT Sustainable Computing Systems

Internet of Things (IoT) has led to significant advancements in communication technologies, specifically, concerning IoT Based Sustainable Information Systems. Lately, Industry-Academic communities have made great strides for the development of security in IoT-based applications, such as traffic management, industrial automation systems, military surveillance systems, transportation, parking, etc. The sustainable IoT converges AI and blockchain technologies for enhancing quality of individual’s life. As a result, emerging IoT applications operate a distributed ledger technology to provide robust-level of encryption and execution for contractual agreement that resolves interoperability and security issues. Thus, this paper proposes a blockchain-based remote mutual authentication (B-RMA) that considers smart devices and cloud networks to offer security and privacy. The proposed B-RMA can coexist with IoT based smart environment to decentralize the processing of user authentication requests. The prominence of the proposed strategies including security efficiency and privacy protection is evaluated using informal security analysis. Moreover, a run-time platform "Node.js" was used to analyze the communication metrics such as execution time, throughput, and overhead ratio over the concurrent requests. The investigation results prove that the B-RMA achieves a scalable environment, accordingly.

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Biological neural networks are often modeled as systems of coupled, nonlinear, ordinary or partial differential equations. The number of differential equations used to model a network increases with the size of the network and the level of detail used to model individual neurons and synapses. As one scales up the size of the simulation, it becomes essential to utilize powerful computing platforms. While many tools exist that solve these equations numerically, they are often platform-specific. Further, there is a high barrier of entry to developing flexible platform-independent general-purpose code that supports hardware acceleration on modern computing architectures such as GPUs/TPUs and Distributed Platforms. TensorFlow is a Python-based open-source package designed for machine learning algorithms. However, it is also a scalable environment for a variety of computations, including solving differential equations using iterative algorithms such as Runge-Kutta methods. In this article and the accompanying tutorials, we present a simple exposition of numerical methods to solve ordinary differential equations using Python and TensorFlow. The tutorials consist of a series of Python notebooks that, over the course of five sessions, will lead novice programmers from writing programs to integrate simple one-dimensional ordinary differential equations using Python to solving a large system (1000’s of differential equations) of coupled conductance-based neurons using a highly parallelized and scalable framework. Embedded with the tutorial is a physiologically realistic implementation of a network in the insect olfactory system. This system, consisting of multiple neuron and synapse types, can serve as a template to simulate other networks.

Blockchain Empowered Smart Home: A Scalable Architecture for Sustainable Smart Cities

Emerging growth in technology has rapidly changed our homes and cities. Present homes and cities will be upgraded to smart homes and smart cities in the near future. Various solutions used to build the smart-city network demand a scalable and decentralized solution. This study proposes a blockchain-empowered decentralized and scalable solution for a sustainable smart-city network. The Internet of Things (IoT), fog nodes, permissioned trust chain, smart contract, blockchain, and InterPlanetary file system (IPFS) are deployed to construct a scalable and decentralized solution for a sustainable smart city. Three main public sector departments, i.e., electricity, water supply, and health care, are studied over the proposed solution. The proposed solution is implemented over constrained application protocol (CoAP) and Ethereum blockchain. The performance of the proposed model is evaluated for 1500 devices and over 10,000 records. A total 77.44% improvement is registered during performance evaluation over a scalable environment. The performance evaluation of each case study and collaborative performance evaluation concludes the improvised performance of the proposed solution for scalable and distributed applications. Better performance, scalability, and the distributed nature of the presented model make it suitable for the sustainable smart-city network.

Towards Open-Source Web-Based 3D Reconstruction for Non-Professionals

Structure-from-motion and multi-view stereo can be used to create 3D models from image sequences, but are not widely adopted by consumers. We study how to address two challenges of such systems for non-professional users: 1) their technical complexity, and, 2) the computational demand needed for processing. To this end, we embed an open-source pipeline in a scalable cloud environment and create a user interface aimed at non-professional users of photogrammetry systems. Finally, we evaluate both the cloud-based infrastructure and the user interface and demonstrate its usability.

Application of virtualisation environment for data security in operational data processing systems

This paper presents a concept, developed and tested by the authors, of a virtualisation environment enabling the protection of aggregated data through the use of high availability (HA) of IT systems. The presented solution allows securing the central database system and virtualised server machines by using a scalable environment consisting of physical servers and disk arrays. The authors of this paper focus on ensuring the continuity of system operation and on minimising the risk of failures related to the availability of the operational data analysis system.

Cost-Effective Active Sparse Urban Sensing: Adversarial Autoencoder Approach

The ever-expanding applications of mobile crowdsensing have made scalable environment sensing possible by exploiting the power of ubiquitous smart devices. Nevertheless, the implementation of sensing applications in urban scale meets serious challenges in terms of sensing costs and quality. For example, data sparsity will arise due to sensing ability and cost, and the measurements could contain noise or errors. Therefore, missing data inference with low-quality measurements is critical. To tackle these challenges, we design a low-cost crowdsensing system by missing data inference incorporating with active sensing grids selection. Specifically, an adversarial autoencoder (AAE)-based scheme is proposed for missing data inference. This model applies VAE to learn latent variables and generates full data and further utilizes the adversarial nets to play a min-max game with the autoencoder. Furthermore, an active learning-based method is designed to iteratively select sensing grids to further reduce the cost. The proposed scheme can handle large missing rate, both random and block missing patterns, and is robust against measurement noise. Comprehensive experiments based on three data sets are conducted to evaluate the effectiveness of the proposed system.

Abstract 1303: AI driven single cell analysis of drug action in solid tumor material: An entry point to functional precision medicine

Abstract Introduction: The paradigm of precision oncology is the selection of optimal therapy for individual patients. However, disease complexity prevents most cases from having a clear correlation between genetics and response, and programs using driver mutations to stratify patients into treatment arms have shown lackluster results (Middleton et al 2020). In contrast, prior work by us and colleagues demonstrated that functional analysis of drug action on single cells in unmanipulated primary tumor tissues with automated microscopy and advanced image analysis can identify effective therapies for patients with hematological cancers. In a prospective interventional trial (EXALT1 NCT03096821 Snijder et al 2017 N=17, Staber et al 2020 ASH N=56) patients treated with drugs ranked by differential ex vivo response resulted in a 55% ORR and >1.3-fold PFS improvement compared to the prior therapy in 54%. However, the scope has been limited to hematological cancers and solid tumor ascites/effusions.Adapting analysis to tumor biopsies required major advances in wet lab and computation, including robust quantification of 3D confocal images. Here, we present first biological evidence from a newly developed pipeline for the ex vivo treatment and single cell interrogation of singular cell + microaggregate suspensions from ovarian cancer biopsies. Methods and results: Viable tissue from debulking surgery was obtained after consent and dissociated. The resulting cells and microaggregates were treated with >85 small molecules in 7 concentrations in triplicate for 72h, fixed, and stained for cancer surface markers. Cells were imaged with automated confocal microscopy at 20x across multiple 384 well plates, with z-resolution of <17 planes. Images were segmented with a modular analysis pipeline with custom neural networks in a scalable cloud environment, yielding precise characterization (e.g. viability, size, marker intensity) and localization of each cell, elucidating the complexity of biopsies. Single cell data in turn was used to determine the ability of drugs to induce targeted cytotoxicity and modify the immune system/pathways, cell morphology, and the 3D environment. Conclusions: High content single cell phenotypic analysis of solid tumors can enable the study of drug action in primary tissues. Compared to organoids where cells are outgrown, working with primary cells in short term incubation enables analysis of drug activity prior to culture adaptation, and the presence of immune cells enables the study of I/O drugs, in a close-to-patient setting. A challenge to achieve single cell resolution of complex mixtures of aggregate/single and adherent/non-adherent cells was overcome by confocal microscopy and AI driven 3D image analysis. The screening and follow-up data indicates this could support preclinical research, biomarker discovery, and precision medicine. Citation Format: Valentin Aranha, Diogo Tomaz, Irene Gutierrez Perez, Florian Rohrer, Joost Van Ham, Lukas Hefler, Laudia Hadjari, Judith Lafleur, Nikolaus Krall, Robert Sehlke, Bojan Vilagos, Gregory Ian Vladimer. AI driven single cell analysis of drug action in solid tumor material: An entry point to functional precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1303.

An Open-Source Many-Scenario Approach for Power System Dynamic Simulation on HPC Clusters

In this paper we introduce an approach to accelerate many-scenario (i.e., hundreds to thousands) power system simulations which is based on a highly scalable and flexible open-source software environment. In this approach, the parallel execution of simulations follows the single program, multiple data (SPMD) paradigm, where the dynamic simulation program is executed in parallel and takes different inputs to generate different scenarios. The power system is modeled using an existing Modelica library and compiled to a simulation executable using the OpenModelica Compiler. Furthermore, the parallel simulation is performed with the aid of a message-passing interface (MPI) and the approach includes dynamic workload balancing. Finally, benchmarks with the simulation environment are performed on high-performance computing (HPC) clusters with four test cases. The results show high scalability and a considerable parallel speedup of the proposed approach in the simulation of all scenarios.

Denial of wallet—Defining a looming threat to serverless computing

Serverless computing is the latest paradigm in cloud computing, offering a framework for the development of event driven, pay-as-you-go functions in a highly scalable environment. While these traits offer a powerful new development paradigm, they have also given rise to a new form of cyber-attack known as Denial of Wallet (forced financial exhaustion). In this work, we define and identify the threat of Denial of Wallet and its potential attack patterns. Also, we demonstrate how this new form of attack can potentially circumvent existing mitigation systems developed for a similar style of attack, Denial of Service. Our goal is twofold. Firstly, we will provide a concise and informative overview of this emerging attack paradigm. Secondly, we propose this paper as a starting point to enable researchers and service providers to create effective mitigation strategies. We include some simulated experiments to highlight the potential financial damage that such attacks can cause and the creation of an isolated test bed for continued safe research on these attacks.

Secure Ring Signature based privacy preserving of Public Auditing mechanism for outsourced data in cloud computing paradigm

Cloud computing offers a high scalable environment for data outsourcing in the cloud. This allows a user to access all the cloud applications of any complexity, without thinking about the limitations of the local system infrastructure. It also provide medium to share the data among others. However originality and authenticity of data that is stored in cloud is an open challenge as it has large provision for data lose and date correction. In order to prevent the outsourced against various challenges, many tradition data auditing mechanism on employing third party auditor has been provided in the literatures with computation and communication capabilities. A portable data management system to will be able to perform auditing process, so that we can ensure the accuracy of the data which is stored on a unreliable third party server. This process is done without even, retrieving all the data. Despite of various advantage of public auditing, preserving the identity privacy from third party auditor has become vital task for the current research. In this paper, we have proposed a reliable and secure ring signature for preserving the privacy of data that is outsourced and shared, using public auditing in the computing paradigm. In this work, ring signature is exploited to privacy preserving of the data auditing of common outsourced data. Further the idea of ring-signatures has to be used to build the authenticator on each block of the data that is outsourced, while keeping maintaining the authenticity of the shared-data to be audited. Finally this model can support multi auditing task simultaneously. Experimental analysis on the proposed system proves that it is more secure and highly efficient against data based attacks. Evaluation the performance of the proposed model has been validated that it outperforms state of arts approaches on computation cost and time.

Line Chart Understanding with Convolutional Neural Network

Visual understanding of the implied knowledge in line charts is an important task affecting many downstream tasks in information retrieval. Despite common use, clearly defining the knowledge is difficult because of ambiguity, so most methods used in research implicitly learn the knowledge. When building a deep neural network, the integrated approach hides the properties of individual subtasks, which can hinder finding the optimal configurations for the understanding task in academia. In this paper, we propose a problem definition for explicitly understanding knowledge in a line chart and provide an algorithm for generating supervised data that are easy to share and scale-up. To introduce the properties of the definition and data, we set well-known and modified convolutional neural networks and evaluate their performance on real and synthetic datasets for qualitative and quantitative analyses. In the results, the knowledge is explicitly extracted and the generated synthetic data show patterns similar to human-labeled data. This work is expected to provide a separate and scalable environment to enhance research into technical document understanding.

Una aplicación basada en ontologías del perfil básico del estándar ISO/IEC 29110 empleando Protégé

Objetive: provide a scalable environment to represent the concepts of the basic profile of the ISO / IEC 29110 standard for Small and Medium-sized Enterprises (SMEs) and their relationships. Methodology: In this paper we propose a novel approach for generating an ontology related to the ISO/IEC 29110 basic profile. We follow some steps: (i) modeling the domain by using executable pre-conceptual schemas; (ii) creating equivalences between pre-conceptual schemas and ontology elements; (iii) translating such equivalences into Protégé, an ontology-based environment; and (iv) creating rules for inferring knowledge from the ontology in order to overcome the aforementioned problems. Results: We create an ontology related to concepts of the ISO/IEC 29110 and their relations in its basic profile. We also answer questions in order to ease the implementation process of the ISO/IEC 29110 basic profile. Conclusions: The resulting ontology serves as support for VSEs and academics when implementing or teaching the basic profile of the ISO/IEC 29110.

An efficient apriori algorithm for frequent pattern mining using mapreduce in healthcare data

The development for data mining technology in healthcare is growing today as knowledge and data mining are a must for the medical sector. Healthcare organizations generate and gather large quantities of daily information. Use of IT allows for the automation of data mining and information that help to provide some interesting patterns which remove manual tasks and simple data extraction from electronic records, a process of electronic data transfer which secures medical records, saves lives and cuts the cost of medical care and enables early detection of infectious diseases. In this research paper an improved Apriori algorithm names Enhanced Parallel and Distributed Apriori (EPDA) is presented for the health care industry, based on the scalable environment known as Hadoop MapReduce. The main aim of the work proposed is to reduce the huge demands for resources and to reduce overhead communication when frequent data are extracted, through split-frequent data generated locally and the early removal of unusual data. The paper shows test results, whereby the EPDA performs in terms of the time and number of rules generated with a database of healthcare and different minimum support values.