Open Source Cloud Research Articles

Resource Allocation Based on Predictive Load Balancing Approach in Multi Cloud Environment

Cloud computing gives a support to purchasers to lessen their internal establishment, and for providers to extend wages, using their own specific system. The best possible burden adjusting and dynamic asset provisioning improves cloud execution and draws in the cloud clients. In this paper, we propose a mechanized asset provisioning calculation, Speculation asset provisioning, inciting load adjusting through theoretical methodology in asset provisioning. As an endeavor to measure asset portion we utilize two level versatile expectation component to check the computational examples of past asset distribution to the future necessity. The structure ensures reasonable assets required for the application, by avoiding over or under-provisioning of asset and supports vitality effectiveness in asset distribution. We use estimation system to address the fluctuation in the verifiable information to adjust the theory overhead. We have passed on our proposed work in an open source cloud structure and differentiated our results and other AI draws near. Our Experimental outcomes show versatile asset designation over client driven help the executives under the quickly changing necessities of cloud computing.

Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment

A vibrant on demand service of today’s era is cloud computing where one can utilize computer resources without indirect active management by user where one can use computing resources to achieve coherence in economic scale. Since cloud computing feel like Everything as a service so there should be highly scalable and reliable mechanisms to distribute the load evenly across the VMs evenly. Innumerable cloudlet mapping policies are presented in various research articles to achieve the high performance, better QOS and minimized task execution time but maximum are conventional approaches. No unconventional realistic scheduling algorithms is available which can schedule the tasks in heterogeneous manner. Since cloudlet scheduling is crucial metrics of cloud computing that has to be heightened by combining the different parameters. This paper tried to provide effectiveness and improvement in task scheduling using nature inspired Particle Swarm optimization (PSO) strategy. A powerful nature inspired load balancing mechanism is proposed in this paper which optimized makespan and throughput in environment of varying cloudlets and virtual machines results as compared to other conventional approaches. Proposed (EPSO) algorithm is with four scheduling policies namely FCFS, Round Robin (RR) and Shortest Job First (SJF) and get near twice good throughput percentage and minimized makespan in two different environments. Author used Cloud sim toolkit and some Open Source cloud packages to simulate the results of various scheduling components. Experimental results of various components are tested and simulated on java based CloudSim toolkit framework.

Smart Tyres: An Environmental-Friendly Solution to Road Accidents

The absence of the tyres monitoring system on vehicle has caused difficulty for driver to check the pressure and temperature of the tyres in real time. Besides that, due to the large geographical area of rural area where the distribution of petrol station with air pump might not be equally distributed, certain area is hard to access air pump. The abnormal pressure and increases in temperature on tyre lead to longer braking distance, tyre blowouts and related issues. The paper describes the deployment of IoT sensors for monitoring application in tyres and data is accessible on mobile app. This monitoring system consists of two sensors to measure the temperature and pressure of the tyre using ESP32 microcontroller board and uploaded into the cloud platform using Wi-Fi technology. While Blynk the mobile app is designed to collect the informative data from the cloud platform and the data is represented in graphical representation using open source Cloud platform. It is made available for real-time monitoring data. Apart from that, this system also incorporates alert system to provide a scalable monitoring system as well as alerting the user for any abnormal reading of the tyre.

Design and Validation of a Low Cost Programmable Controlled Environment for Study and Production of Plants, Mushroom, and Insect Larvae

Study and optimization of biological processes involved in food production require the quantitative study of the environment’s influences on the organism and eventually the products and byproducts consumed and produced. Commercial growth chambers do not fully support such quantitative study due to the underlying limitations (cost, size, resource exchange, programmability, user interaction, etc.). This work presents a low cost programmable system designed to facilitate such studies in organisms such as plants, fungi, and insect larvae. The proposed system consists of modular units performing specific functions. A sensor cluster for measuring gas concentrations, air properties (temperature, humidity, pressure), and growing medium properties was implemented and tested. Actuators for resource exchange, air conditioning, and light spectrum adjustment are proposed. A three-tier hierarchical software framework consisting of open-source cloud platform for data aggregation and user interaction; microcontroller firmware; and an application development framework for test automation and experiment regime design is developed. Series of experiments and tests were performed using the designed hardware and software to evaluate its capabilities and limitations. This controlled environment was used to study photosynthesis in Ocimum basilicum and, in a second experiment, the evolution of the metabolic activity of Hermetia illucens larvae over its larval phase.

Adaptive user-oriented fuzzy-based service broker for cloud services

This paper presents an adaptive fuzzy-based cloud service brokering algorithm (AFBSB). The proposed algorithm employs an adaptive fuzzy-based engine to select the most appropriate data center for user cloud service requests considering user preferences in terms of cost and performance. The algorithm is implemented using an open-source cloud computing simulation tool. The algorithm results are tested against the results of other existing techniques within two types of cloud environments. First, a performance-constrained environment in which performance improvement is the main objective for cloud users. Second, a preference-aware environment, in which cloud users have different cost and performance preferences. Simulation results show that the proposed algorithm can achieve significant performance improvement in performance-constrained environments. As for the preference-aware environment, they show that AFBSB can operate in user-oriented manner that guarantees performance/cost improvement, as compared to other algorithms.

Performance Evaluation and Social Optimization of an Energy-Saving Virtual Machine Allocation Scheme Within a Cloud Environment

Achieving greener cloud computing is non-negligible for the open-source cloud platform. In this paper, we propose a novel virtual machine allocation scheme with a sleep-delay and establish a corresponding mathematical model. Taking into account the number of tasks and the state of the physical machine, we construct a two-dimensional Markov chain and derive the average latency of tasks and the energy-saving degree of the system in the steady state. Moreover, we provide numerical experiments to show the effectiveness of the proposed scheme. Furthermore, we study the Nash equilibrium behavior and the socially optimal behavior of tasks and carry out an improved adaptive genetic algorithm to obtain the socially optimal arrival rate of tasks. Finally, we present a pricing policy for tasks to maximize the social profit when managing the network resource within the cloud environment.

Effectiveness of Open Source Cloud Computing in Developing Countries: Empirical Study

Effectiveness of Open Source Cloud Computing in Developing Countries: Empirical Study

Application of the open-source cloud platform FIWARE for future building energy management systems

The integration of volatile, decentralized energy into building energy systems requires intelligent algorithms that are capable of controlling the augmented complexity and the interactions of subsystems efficiently. Not only do the algorithms require computing capacity, but also a feasible information and communication infrastructure. One possibility to provide computing power on demand is cloud computing that forms together with an extended connectivity of physical devices the Internet of Things (IoT). We present a customizable open-source IoT platform setup using FIWARE. For the integration of existing building automation networks, we extend an OpenMUC gateway with BACnet functionality and connect it to our platform via MQTT. We successfully demonstrate the overall setup with a web-based visualization of high resolution energy monitoring and a control loop as basic requirements for the integration of more advanced control algorithms in future building energy management systems. We find our setup generally feasible for both use cases and, hence, will aim for the integration of more advanced control algorithms in the future using the presented platform components.

Research on Automatic Deployment of Simulation Test Environment Based on Cloud Platform

It is a future trend that SoS confrontation tests develop towards combining simulation tests with realistic exercises, so how to deploy a simulation test environment on demand conveniently, quickly and smartly becomes an urgent problem to be solved. Starting from the architecture and requirements of a simulation test environment, this paper analyzes the deployment solution of Cloudify and Docker open source cloud platform clusters, designs a cloud-based automatic deployment platform for the simulation test environment based on OpenStack, Cloudify and Docker, and realizes the automatic deployment of the simulation test environment with full software stack and full deployment process. The automatic deployment not only improves the efficiency of the deployment and maintenance, but also increases the utilization ratio of resources.

An Integrated Model of IoT and Cloud for Detecting Green House Gas Emissions

Chennai is one of the oldest metropolitan city in India which consists of 15 zones and more than 200 wards. Chennai has the population of more than 8 million people and more than 20 tons of garbage collected and dumped into the two of the dumpyards Kodunkaiyur and Perungudy. These two dumpyards are catering the needs of the garbage and landfills collected from these zones, in which they are used to burn and disposed. The burning of waste is resulted into emission of higher amount of CO2. The decayed waste is one of the source for generating Methane and other greenhouse gases. This is dangerous for human lives and affects the environment. There is no such system has available to detect the CO2 and CH4 level automatically at these dumpyards. If a system automatically detects the level of CO2 and CH4 and make it available in a public domain, it will be helpful to the city administration to take the precautionary steps. Therefore, the need of the hour is to design a cloud enabled IoT based system to monitor the level of CO2 and CH4 and upload these data in the cloud for further processing. The sensor is installed in 20 of different places of these two dumpyard for monitoring the level of CO2 and CH4. The data is collected and uploaded in the cloud. The open source cloud platform Openstack is used to store all the recorded data into the cloud. The webpage www.chennaighgemissions.in is designed and the sensed data is available for public use. The system is successfully implemented and tested, and the test result is promising.

Lightweight Accountable Privacy-Preserving Protocol Allowing the Cloud Client to Audit the Third-Party Auditor for Malicious Activities

Cloud computing is reserving its position in the market as the next disruptive utility paradigm. It is found on the pay-as-you-use model. Cloud computing is changing the way information technology (IT) operates for individuals as well as for companies. Cloud computing comes with different offerings to accommodate diverse applications. It comes with many successful adoption stories and a few unfortunate ones that are related to security breaches. Security concerns are what is making many companies reluctant to fully embrace the cloud realm. To enhance trust and entice adoption between cloud clients (CC) and cloud service providers (CSP), a new paradigm of depending on involving a third-party auditor (TPA) has been introduced. Hence, implementing a solution with a TPA comes with its toll in terms of trust and processing overhead. A lightweight security protocol to give the CC extra control with tools to audit the TPA and the CSP is paramount to the solution. In this paper, we are introducing a novel protocol: the lightweight accountable privacy-preserving (LAPP) protocol. Our proposed protocol is lightweight in terms of processing and communication costs. It is based on a newly introduced mathematical model along with two algorithms. We have conducted simulation experiments to measure the impact of our method. We have compared LAPP to the most eminent privacy-preserving methods in the cloud research field, using the open source cloud computing simulator GreenCloud. Our simulation results showed superiority in performance for LAPP in regard to time complexity, accuracy, and computation time on auditing. The aim of the time complexity and computation time on auditing simulations is to measure the lightweight aspect of our proposed protocol as well as to improve the quality of service.

Efficient Provisioning of Security Service Function Chaining Using Network Security Defense Patterns

Network functions virtualization intertwined with software-defined networking opens up great opportunities for flexible provisioning and composition of network functions, known as network service chaining. In the cloud, this allows providers to create service chains tuned to each application type while optimizing resources’ utilization. This is particularly useful to accommodate different tenants’ applications with different security needs. However, considering security provisioning from the single perspective of resources optimization may lead to deployment solutions that do not comply with well-known security-related best practices and recommendations. In this paper, we propose network security defense patterns (NSDP) aimed at leveraging the best practice and know-how from the security experts and at capturing various security constraints to efficiently select compliant security functions’ deployment options. The placement problem being a NP-Hard problem to solve, we also propose a scalable networking and computing resources aware optimization framework to efficiently provision different NSDPs. We further show the feasibility of implementing NSDPs in the cloud infrastructure through the integration of our approach into an open source cloud framework, namely OpenStack, in our test laboratory. The simulation results show the effectiveness of our approach in selecting an optimal placement of the security functions for large data centers with hundreds of thousands of computing nodes, while complying with the predefined security constraints and improving the scalability compared to the current placement algorithms.

Free and open source cloud technology based on the type Software as a Service

The aim of this study is to design and implement the cloud computing technology with types of the services Software as a Service (SaaS). Objectives of the study is to analyze the free and open source cloud technology based on the type Software as a Service. The object of research is the process of using of the cloud computing technology with types of the services Software as a Service in education. The subject of research is the use of free cloud computing technology with types of the services Software as a Service in education for students of university. This article covers the free and open source cloud computing technology and its application in Web-based IT education. One of the technologies that can be used in teaching online IT courses is cloud computing. In this work the analysis and systematization of research on the use of free cloud-based ICT in education, research and organizational activities. Results of the study is planned to use for the design of methods e-learning education with the free and open source cloud technology.

A Topology Based Spatio-Temporal Map Algebra for Big Data Analysis

Continental and global datasets based on earth observations or computational models challenge the existing map algebra approaches. The available datasets differ in their spatio-temporal extents and their spatio-temporal granularity, which makes it difficult to process them as time series data in map algebra expressions. To address this issue we introduce a new map algebra approach that is topology based. This topology based map algebra uses spatio-temporal topological operators (STTOP and STTCOP) to specify spatio-temporal operations between topological related map layers of different time-series data. We have implemented several topology based map algebra tools in the open source geoinformation system GRASS GIS and its open source cloud processing engine actinia. We demonstrate the application of our topology based map algebra by solving real world big data problems using a single algebraic expression. This included the massively parallel computation of the NDVI from a series of 100 Sentinel2A scenes organized as earth observation data cubes. The processing was performed and benchmarked on a many core computer setup and in a distributed container environment. The design of our topology based map algebra allows us to deploy it as a standardized service in the EU Horizon 2020 project openEO.

Loss of Significance and Its Effect on Point Normal Orientation and Cloud Registration

Point normal calculation and cloud registration are two of the most common operations in point cloud processing. However, both are vulnerable to issues of numerical precision and loss of significance. This paper documents how loss of significance in the open-source Point Cloud Library can create erroneous point normals and cause cloud registration to fail. Several test clouds are used to demonstrate how the loss of significance is caused by tight point spacing and clouds being shifted far from the origin of their coordinate system. The results show that extreme loss of significance can cause point normals to be calculated with a random orientation, and cause meters of error during cloud registration. Depending on the structure of the point cloud, loss of significance can occur when the cloud is at hundreds or even tens of meters from the origin of its coordinate system. Shifting to larger data types (e.g., from 32-bit “floats” to 64-bit “doubles”) can alleviate the problem but will not solve it completely. Several “best practice” recommendations for avoiding this issue are proposed. But the only solution guaranteed to eliminate loss of significance is de-meaning the entire cloud, or clusters of points before processing.

Towards understanding bugs in an open source cloud management stack: An empirical study of OpenStack software bugs

Cloud management stack (CMS) provides convenience for organizations in managing their cloud platforms. CMS software is complex and bugs in it can cause serious damage to the cloud environment. Therefore, an in-depth understanding of CMS bugs can help developers detect and fix them. In this paper, we conduct a thorough empirical study of several key characteristics of bugs in OpenStack — the most popular open source CMS. Our study computes general statistics for about 50K OpenStack bugs, including the evolution of bugs, the distribution of bugs, and the duration of bugs. We then selected 579 bugs for an in-depth study. In particular, we study the input factors for triggering the bugs, the consequences of the bugs, and how the bugs are fixed. The findings of this study provide a set of lessons learned and guidance to aid practitioners and researchers to better handle bugs in CMS software.

Energy-aware VM placement algorithms for the OpenStack Neat consolidation framework

One of the main challenges in cloud computing is an enormous amount of energy consumed in data-centers. Several researches have been conducted on Virtual Machine(VM) consolidation to optimize energy consumption. Among the proposed VM consolidations, OpenStack Neat is notable for its practicality. OpenStack Neat is an open-source consolidation framework that can seamlessly integrate to OpenStack, one of the most common and widely used open-source cloud management tool. The framework has components for deciding when to migrate VMs and for selecting suitable hosts for the VMs (VM placement). The VM placement algorithm of OpenStack Neat is called Modified Best-Fit Decreasing (MBFD). MBFD is based on a heuristic that handles only minimizing the number of servers. The heuristic is not only less energy efficient but also increases Service Level Agreement (SLA) violation and consequently cause more VM migrations. To improve the energy efficiency, we propose VM placement algorithms based on both bin-packing heuristics and servers’ power efficiency. In addition, we introduce a new bin-packing heuristic called a Medium-Fit (MF) to reduce SLA violation. To evaluate performance of the proposed algorithms we have conducted experiments using CloudSim on three cloud data-center scenarios: homogeneous, heterogeneous and default. Workloads that run in the data-centers are generated from traces of PlanetLab and Bitbrains clouds. The results of the experiment show up-to 67% improvement in energy consumption and up-to 78% and 46% reduction in SLA violation and amount of VM migrations, respectively. Moreover, all improvements are statistically significant with significance level of 0.01.

IOT based Halitosis Detection

Halitosis is also termed as chronic bad breath. The major cause is diabetics mellitus. The doctor often must take two tests to differentiate between ketoacidosis and halitosis. The difference between these two are ketoacidosis has acetone and ketone bodies has biomarker whereas halitosis has volatile Sulphur compounds. Designing a breath analyser with raspberry pi which as inbuilt IoT to transfer the measured readings to open source cloud and in mobile app. Through this simple device the physicians is always connected to the patient and able to monitor the subject.

Reliability of ROS Networked Mobile Robots

When working remotely with mobile robotics, a reliable wireless communication network becomes essential, especially in large operating regions. As most teleoperated robots rely on standard Wi-Fi communication, network behavior has a crucial effect on autonomous robot control. The main goal of this research is to measure and diagnose the system reliability, roaming issues, and bottlenecks of such data transmission. To study these significant factors, two measurement scenarios were conducted. Measurements consist of two Wi-Fi access points (AP) and a TurtleBot II robot used in two different system set-up configurations. In the first configuration, two APs are connected in bridge mode (LAN connection) and in the second configuration the APs are connected in WDS bridge (Wireless Data Distribution) mode. This article presents the results of Robot Operating System (ROS) IEEE 802.11 network measurements in roaming mode, in wireless bridge mode, and in an extended coverage area employed in WDS mode. Results of data transmission measurements, configurations, and evaluation of the entire system are also presented. All the measurements utilize the Open Cloud Robotic Platform (OpenCRP)1 an open-source cloud robotics ecosystem based on service-oriented PaaS architecture using the Ubuntu Linux operating system.

A Safe, Open Source, 4G Connected Self-Flying Plane With 1 Hour Flight Time and All Up Weight (AUW) <300 g: Towards a New Class of Internet Enabled UAVs

Here, we present an open-source cloud (4G) connected and controlled self-flying airplane based on a secure, encrypted flight stack with all up weight of <;300 g as an example of a new class of unmanned aerial vehicles (UAVs). The avionics portion (including the flight controller, 4G cellular data modem, and on-board companion Linux computer) weighs only 40 g, which is about 10 times lighter than previous avionics package achieving the same mission: 4G secure encrypted robust self-healing Internet connectivity for video and telemetry, autonomous flight capability without the need for a pilot in local proximity to the UAV, and GPS positioning. With a flight time of 1 h, this represents the lightest, longest flight time 4G UAV ever demonstrated. Enabled by the convergence of advances in multiple hardware and software systems and disciplines, this effectively defines a new class of the UAVs, distinct from larger multirotors for aerial surveys and photography and smaller nano-quadcopters with short flight times and ranges, defined by the characteristics of inherently stable aerodynamic flight in case of hardware failure (such as motor failure), inherently long flight times, inherently long range due to the efficiency of a wing over quadrotor, and Internet connectivity for control from any point on earth to virtually any point on earth.