Open Source Cloud Research Articles

Performance Analysis of Open Source Cloud Computing Architectures

Organizations prioritize cloud computing as the primary solution for ensuring secure and, most importantly, uninterrupted delivery of data. In today's landscape, there are various architectural frameworks offering cloud computing infrastructures to choose from. It is crucial to understand how advantageous the performance offered by a chosen architecture is when making a selection. This study aims to comprehend how effective open-source cloud computing architectures are in specific usage scenarios, provide a comparative perspective, and assist organizations in making informed decisions when selecting their IT infrastructures. In line with the study's objectives, the network, disk, and processor performances of OpenStack, OpenNebula, and Apache CloudStack architectures on the same hardware are examined based on data-driven methods. The Iperf3 tool is used for network performance measurement, the dd (Linux) tool for disk performance measurement, and the Sysbench tool for processor performance measurement. After conducting performance measurements with these tools, it was observed that the OpenNebula architecture outperformed the other architectures in terms of overall performance.

Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach

Vegetation cover degradation is often a complex phenomenon, exhibiting strong correlation with climatic variation and anthropogenic actions. Conservation of biodiversity is important because millions of people are directly and indirectly dependent on vegetation (forest and crop) and its associated secondary products. United Nations Sustainable Development Goals (SDGs) propose to quantify the proportion of vegetation as a proportion of total land area of all countries. Satellite images form as one of the main sources of accurate information to capture the fine seasonal changes so that long-term vegetation degradation can be assessed accurately. In the present study, Multi-Sensor, Multi-Temporal and Multi-Scale (MMM) approach was used to estimate vulnerability of vegetation degradation. Open source Cloud computing system Google Earth Engine (GEE) was used to systematically monitor vegetation degradation and evaluate the potential of multiple satellite data with variable spatial resolutions. Hotspots were demarcated using machine learning techniques to identify the greening and the browning effect of vegetation using coarse resolution Normalized Difference Vegetation Index (NDVI) of MODIS. Rainfall datasets of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) for the period 2000–2022 were also used to find rainfall anomaly in the region. Furthermore, hotspot areas were identified using high-resolution datasets in major vegetation degradation areas based on long-term vegetation and rainfall analysis to understand and verify the cause of change whether anthropogenic or climatic in nature. This study is important for several State/Central Government user departments, Universities, and NGOs to lay out managerial plans for the protection of vegetation/forests in India.

Towards Open-Source Cloud Adoption : Exploring the Determinants

The trends in cloud computing development have become fundamental building blocks to many business information system models and innovations, the architects are designing cloud systems to be as effective and beneficial as possible. In this paradigm, open-source cloud platforms are part of the design philosophy that drives innovation in cloud services. The adoption of open-source cloud has become pervasive in the modern enterprise; this has been accelerated by perceived benefits of openness. The power in the community of developers and openness fosters development of hardened, secure and reliable solutions. These features enable a collaborative source code, modification and customization geared towards innovation with positive impact on the design, aligning perfectly with the dynamic nature of open-source cloud solutions. However, the slow adoption rate in the modern enterprises has been attributed to a lack of understanding of open-source cloud adoption. This study explores the determinants of open-source cloud adoption in the context of higher learning institutions. A deductive thematic analysis technique was utilized in the study.

A Model for Adoption of Open-Source Cloud Computing: Platform as a Service and Software as a Service

A Model for Adoption of Open-Source Cloud Computing: Platform as a Service and Software as a Service

Analysis of long-term changes in algal bloom pattern and their association with Ocean, atmosphere, and land-based processes across the northern Indian Ocean

Algal blooms are global phenomena in marine and fresh waters. An understanding of the impacts of climate change and anthropogenic activities on marine algal blooms is essential for protecting marine ecosystems. We have studied the long-term changes in the spatial patterns of algal blooms over the northern Indian Ocean and assessed the influence of the ocean, atmosphere, and land-based processes on them. We have analyzed multi-source satellite data (2003 – 2020) of various parameters such as algal concentration, temperature, salinity, sea level, particulate organic carbon, precipitation, wind speed, and river discharge on an open-source cloud computing platform. The seasonality of variables is assessed using a harmonic model, and their spatial trend is estimated using the non-parametric Mann-Kendall test. The spatial relationship between various geophysical parameters and algal bloom concentration is assessed by the cross-correlation test, and the prediction is done using a multivariate autoregression model. Results suggest that each variable exhibits distinguishable seasonal patterns. Algal bloom duration showed a significant increase over time. However, algal bloom coverage showed a significant decline in the coastal waters in all seasons except post-monsoon. The cross-correlation test unveils that lower temperatures, salinity, and sea level promote algal bloom occurrences in coastal waters rather than open waters. Furthermore, the statistical model with a time lag of 2 months shows better performance for prediction of bloom intensity (r2 = 0.86).

Brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

Beyond Food: using smart pest management in IoT greenhouses to foster environmental sustainability

Higher yields and year-round crop cultivation are provided by greenhouse farming, but there are drawbacks as well, such as the need to maintain ideal environmental conditions and safeguard crops from pests. Conventional techniques frequently use chemical pesticides, which raises issues for the environment and human health. Through the integration of smart pest management within an Internet of Things (IoT)-powered greenhouse, the “IoT GREEN” project provides a sustainable solution. This creative system monitors important environmental variables including temperature, humidity, light, and soil moisture using wireless sensors and a Raspberry Pi. Farmers are given the ability to remotely monitor their crops thanks to the transmission of real-time data to an open-source cloud server and its visualization via a local web server. Importantly, “IoT GREEN” incorporates smart pest detection, which goes beyond simple monitoring. By identifying possible insect infestations early on and using sophisticated algorithms and picture recognition, this technology enables targeted interventions. “IoT GREEN” reduces dependency on hazardous pesticides by using non-chemical techniques like biocontrol agents or pheromone traps, promoting a more sustainable agricultural ecosystem. In addition to protecting crops, this all-encompassing strategy also fosters biodiversity, lowers environmental impact, and guarantees food safety.

FaaS for IoT: Evolving Serverless towards Deviceless in I/Oclouds

The burgeoning paradigms of Fog and Edge computing propose delegating Cloud-related tasks to the network’s periphery, thus placing computational resources closer to data producers. This shift promises to boost the performance of IoT-based services, providing swift response times while conserving bandwidth. Despite their potential, the current Edge/Fog computing platforms must provide the required flexibility for dynamic service orchestration within a data-oriented context. Addressing this gap, the Function-as-a-Service (FaaS) model emerges as an exceptional strategy for Edge/Fog deployments. Its ability to manage an ever-expanding ecosystem of devices with remarkable flexibility and efficiency holds considerable promise. This paper articulates a novel approach to enhancing the adaptability of IoT Edge/Fog deployments. We propose an innovative extension to OpenStack, an open-source Cloud management system, which pushes its functionality towards the network Edge. Our approach empowers OpenStack to facilitate FaaS services within a distributed IoT infrastructure, thus infusing unprecedented adaptability and efficiency into the Edge/Fog computing paradigms.

AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization.

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subject's skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

A novel open-source cloud control platform with application to tracking control under disturbance

This paper presents a novel rapid control prototyping (RCP) framework for cloud control systems (CCSs). This new architecture consists of human–computer interaction (HCI) client, a cloud server, and an edge controller. It is designed based on open-source hardware and software, with the advantages of low cost, high flexibility, and easy replication. It can offer a reference design for better and further research of CCSs. Then, a cloud-based maglev control system (CMCS) is developed, and a case study on tracking control with disturbance is performed. Furthermore, a dual-structural controller based on equivalent-input-disturbance (EID) and networked predictive control (NPC) approach is devised to enhance the disturbance rejection performance for CCSs with communication constraints. The system is divided into two subsystems, and the separation theorem is applied to simplify the design. A discrete-time EID (DEID) estimator is introduced for one subsystem to compensate for the time delay and the exogenous disturbance. For another subsystem, a time-event-driven strategy-based NPC method is devised to increase the dynamical performance of handling packet loss. Finally, experimental results verified the effectiveness and superiority of the proposed framework and the DEID-based dual-structural control algorithm.

Fault tolerance systems in open source cloud computing environments–A systematic review

Considering that there are several distinct cloud computing environments and several suggested approaches for the treatment of fault tolerance for such environments, the objective of the study presented here is a systematization of fault tolerance proposals that results in a survey and the generation of a guided consultation environment for reading the relevant techniques for each case. With the systematization of proposed solutions, it is intended to obtain a document that administrators of cloud computing systems can use. This work points out which techniques apply to which problems, including the advantages and disadvantages of each technique, and facilitates the support process for these administrators in handling the failures. Finally, with the information obtained, a website will be generated to store some of this information. This virtual environment is a prototype of a recommendation environment for cloud fault tolerance. At first, the recommendation will occur through guided search so that administrators of cloud computing systems can have better conditions to handle failures in their environments

A novel autonomous container-based platform for cybersecurity training and research.

Cyberattacks, particularly those targeting systems that store or handle sensitive data, have become more sophisticated in recent years. To face increasing threats, continuous capacity building and digital skill competence are needed. Cybersecurity hands-on training is essential to upskill cybersecurity professionals. However, the cost of developing and maintaining a cyber range platform is high. Setting up an ideal digital environment for cybersecurity exercises can be challenging and often need to invest a lot of time and system resources in this process. In this article, we present a lightweight cyber range platform that was developed under the open-source cloud platform OpenStack, based on Docker technology using IaC methodology. Combining the advantages of Docker technology, DevOps automation capabilities, and the cloud platform, the proposed cyber range platform achieves the maximization of performance and scalability while reducing costs and resources.

Stack bricolage and infrastructural impermanence in financial machine-learning modelling

ABSTRACT Hoping that the promises of machine-learning can be realised in financial markets, investment management and trading firms increasingly employ machine-learning techniques to extract exploitable informational edge from large datasets. In addition to heavy investments in technology and the human resources capable of manipulating it, this development has led to increased use of open-source machine-learning and data-management resources. Drawing on 44 interviews with developers and users of machine-learning techniques in finance, we explore how such platforms and other open-source resources are understood and used by said practitioners. Building on work in the Social Studies of Finance (SSF) on financial modelling and platformisation, we argue that these users of machine learning in finance engage in what we term stack bricolage activities, when they reuse disparate open-source resources in their modelling work. We argue that stack bricolage creates dependencies on open-source cloud resources characterised by infrastructural impermanence, which is a result of their substitutability and maintenance sensitivity. Our study contributes to the emerging SSF literature on machine-learning modelling cultures and debates in Science and Technology Studies and adjacent fields on the reuse of data and software in platformised cloud infrastructures.

Personal Cloud Storage using Raspberry PI

Developing a Raspberry Pi personal cloud server that can serve as your very own personal cloud storage is the goal of the project "Personal Cloud Using Raspberry Pi." Moving your data to private cloud storage is a preferable solution because privacy protection is a major concern today. The Raspberry Pi 3 model B module that houses the cloud server is configured. The server is linked to the internet by the router. For greater data storage, the server offers an optional USB hard disc. The Raspberry Pi server's wireless network card or adapter is used to link it to Personal Cloud, an open-source cloud computing platform. The Raspberry Pi can be configured remotely from any device, such as a smartphone, using a local tunnel.Numerous services, including SSH, HTTP, and VNC, are configured on and managed by the local tunnel. Access to the Personal Cloud account is restricted to the admin. Through the local tunnel, an administrator can provide users a variety of services. You can store your data for free on our project as needed. With tight system control, this project provides you the freedom to use space efficiently.

Smart Agriculture using IoT

Greenhouse Environment or Smart agriculture, used to grow plants under controlled climatic conditions for efficient production, forms an important part of the green house and horticulture sector .To create an optimal environment the main parameters such as temperature, humidity, light intensity ,ground water ,etc. needs to be controlled. The main objective of this project work is to design an automated greenhouse which is purely sensor based system .The system inputs from various sensors and displays output .The developed system is simple, cost efficient and easily installable. The results show that the system could be more efficient in man power saving and raising the economic value of products. This asset allows the green houseer to improve the cultivation in a way the plants need. It leads to higher crop yield, prolonged production period, better quality, and less use of protective chemicals. For continuous monitoring and controlling, we are using wireless sensor network. Here the greenhouse parameters are send through the internet to the open source cloud server. The parameters can be controlled by another web-server hosted locally. This project mainly focused on user friendly UI design and automatic system.

Cloud–edge microservices architecture and service orchestration: An integral solution for a real-world deployment experience

After some years in which information management has been centralized in cloud platforms, the need to implement architectures that perform more efficient and intelligent management of such information has become essential. In addition, the growth of Smart City solutions, which involve the deployment of large-scale network infrastructures, has revealed the benefits of processing data as close as possible to the source, a key principle of Edge Computing. However, the benefits of service orchestration in real-world applications have not been demonstrated to date given the novelty and difficulties related to large-scale deployments. To overcome this barrier, this work presents the design, deployment, and validation of a cloud–edge microservice-based architecture for service orchestration on top of a real-world network consisting of 175 nodes. These nodes were deployed as part of a regional network of touristic terminals to raise tourism’s potential in the region of Badajoz, Spain. The integration of the openBalena open-source cloud platform into the architecture enables remote and centralized management. The experiments carried out revealed an orchestration latency time to deploy new applications from scratch throughout the entire region between 22 s and 292 s depending on the size of the application image. In addition, CPU load and memory usage have been monitored and the lack of suitability of virtualized environments for interactive applications has been overcome, and a step-by-step guide from a successful deployment is provided. As a result, this integral solution contributes valuable insights into how to bring the benefits of orchestration to reality.

Feature-aware aggregation network for remote sensing image cloud detection

ABSTRACT Cloud detection is a key pre-processing step for optical satellite remote sensing images. Convolutional neural networks (CNNs) are the most widely used and powerful cloud detection techniques in recent years, featuring the fusion of different levels of feature maps. However, existing methods use feature cascading, element addition, or element multiplication to fuse directly, ignoring the similarities and differences between features and their contribution to the detected cloud regions, resulting in a large amount of noise being introduced into the fusion process. To solve this problem, this paper proposes a feature-aware aggregation network (FAANet), which can make full use of valuable information for cloud detection through feature enhancement and feature aggregation. The feature enhancement module (FEM) consists of a high-level feature enhancement module (HFEM) and a low-level feature enhancement module (LFEM). HFEM is used to enhance the advanced semantic features to supplement the cloud features lost during the recovery of high-resolution feature maps; LFEM is used to enhance the image spatial detail features to further refine the predicted cloud maps. In addition, a feature aggregation module (FAM) is designed to selectively aggregate coded block features, decoded block features, and enhanced semantic features through a new aggregation mechanism to compliment the features diluted by up-sampling and improve the integrity of the generated cloud maps. Experimental results on three open-source cloud detection datasets and self-built Landsat-8 cloud detection dataset with higher spatial resolution show that the proposed method achieves competitive performance under different evaluation metrics. The advantages of the method proposed in this paper for saliency image segmentation are verified and provide a reference for other computer vision tasks. Code is available at https://github.com/HaiLei-Fly/FAANet.

Raspberry Pi as a Personal Cloud Server with Next-cloud

This research will show steps on how to build personal cloud storage by using Raspberry Pi (minicomputer), which will help the user to enable cloud storage mode to their external hard drive. Software solutions like Next-cloud, own-cloud are open-source cloud storage applications that allow users to store and access files, photos, and other digital assets from any device with internet access. The main objective of our project is to give a user personal Cloud Storage of his own in a cost-efficient manner which will be much more reliable than any other material cloud service.

Parallel Processing of Sensor Data in a Distributed Rules Engine Environment through Clustering and Data Flow Reconfiguration.

An emerging reality is the development of smart buildings and cities, which improve residents' comfort. These environments employ multiple sensor networks, whose data must be acquired and processed in real time by multiple rule engines, which trigger events that enable specific actuators. The problem is how to handle those data in a scalable manner by using multiple processing instances to maximize the system throughput. This paper considers the types of sensors that are used in these scenarios and proposes a model for abstracting the information flow as a weighted dependency graph. Two parallel computing methods are then proposed for obtaining an efficient data flow: a variation of the parallel k-means clustering algorithm and a custom genetic algorithm. Simulation results show that the two proposed flow reconfiguration algorithms reduce the rule processing times and provide an efficient solution for increasing the scalability of the considered environment. Another aspect being discussed is using an open-source cloud solution to manage the system and how to use the two algorithms to increase efficiency. These methods allow for a seamless increase in the number of sensors in the environment by making smart use of the available resources.

BIOM-28. AN EXOSOME BASED LIQUID BIOPSY DIAGNOSTIC FOR GLIOMA

Abstract BACKGROUND Liquid biopsy has gained popularity of late in the diagnosis and management of several cancers. This study aims to diagnose suspected lesions suspected of being gliomas on neuro-imaging by obtaining a peripheral venous blood sample- with a specificity and sensitivity that approaches a tissue biopsy. MATERIALS AND METHODS After clearance from Ethics Committees, specimens of the tumour tissue along with blood from a peripheral or vein was collected from all 80 adult patients with a suspected diagnosis of glioma. Clinical details along with the neurological status, the functional score, the extent of resection, pathological diagnosis and survival data were also collected. The tumour tissue transported in RNA-later and stored at -80C. Following exosome isolation, RNA-seq was performed on the exosomal sample obtained from the serum and the tumour sample. Exome analysis was performed on the tumour tissue (somatic mutations) and the WBC (for germline mutations) to a depth of 100. RESULTS The analysed data was quality checked for all glioma associated genes including but not limited to ATRX, H3F3A, IDH1, MGTM and TERT driver genes related to DNA repair, cytoskeleton and cellular proliferation-related genes EGFR, FLT1/(VEGFR), BRAF, GFAP, MKi67, NES, OLIG2, PIK3CA, SMAD3, S1001A and VIM and tumour suppressor genes SMARCB1/INI1 and TP53. Open-source cloud based genomic analysis platforms were used for analysis. Machine learning clustering algorithms identified serum exosome based clusters that corresponded to already identified glioma tissue based genomic subgroups, with specificity and sensitivity approaching 95%. CONCLUSION Liquid biopsies provide a reliable and effective way of diagnosing gliomas in patients who present with an intracranial lesion on radiology.