An Empirical Study of the Impact of Cloud Patterns on Quality of Service (QoS)

International audienceCloud patterns are described as good solutions to recurring design problems in a cloud context. These patterns are often inherited from Service Oriented Architectures or Object-Oriented Architectures where they are considered good practices. However, there is a lack of studies that assess the benefits of these patterns for cloud applications. In this paper, we conduct an empirical study on a RESTful application deployed in the cloud, to investigate the individual and the combined impact of three cloud patterns (i.e., Local Database proxy, Local Sharding-Based Router and Priority Queue Patterns) on Quality of Service (QoS). We measure the QoS using the application's response time, average, and maximum number of requests processed per seconds. Results show that cloud patterns doesn't always improve the response time of an application. In the case of the Local Database proxy pattern, the choice of algorithm used to route requests has an impact on response time, as well as the average and maximum number of requests processed per second. Combinations of patterns can significantly affect the QoS of applications. Developers and software architects can make use of these results to guide their design decisions

Instinctive Calibrate based Container System Along with Protection and Database Optimization for Emphatic Cloud based software Testing

Innovative developments of cloud-based application the researchers must conduct cloud-based software tests to assess the reliability and completeness in order to ensure the high quality. Nonetheless, several scholars came up with research on testing technology applied to the cloud, in that there is no specific approach to follow for resource management, software integrity and database configure optimization in order to perform an effectual cloud-based software testing. Hence, the paper proposed a novel Emphatic Cloud Integration Testing with DBM’s Framework to support integration of remotely-hosted cloud testing tools in a strong secure and lossless data manner. To begin with reduction of waste resources, the frame work introduces Instinctive Calibrating based Container’s system, which performs the implementation of four level mechanism with instinctive calibrate service on containerized orchestration platform to control the calibrate-in/ calibrate-out of containers during work load fluctuation. Along with this for container security and integrity, Isolated Ratification with protection scrutinize Strategy is incorporates that conquer via separate validation to each compute node equipped with a single trusted platform module, and it enables integrity verification of both the host and running containers. At last due to the diverse database instances and query workloads, the framework commences with Tetrad Deep Method to optimize the configurations of database through end-to-end isolated database alteration with attempt-defect manner that overcome the shortcoming caused by regression, hence the proposed work highly reduced the time and space complexity at the occasion of major services as cloud-based software testing

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy-efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes that provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements

Quantifying the Impact of Replication on the Quality-of-Service in Cloud Databases

Cloud databases achieve high availability by automatically replicating data on multiple nodes. However, the overhead caused by the replication process can lead to an increase in the mean and variance of transaction response times, causing unforeseen impacts on the offered quality-of-service (QoS). In this paper, we propose a measurement-driven methodology to predict the impact of replication on Database-as-a-Service (DBaaS) environments. Our methodology uses operational data to parameterize a closed queueing network model of the database cluster together with a Markov model that abstracts the dynamic replication process. Experiments on Amazon RDS show that our methodology predicts response time mean and percentiles with errors of just 1% and 15% respectively, and under operational conditions that are significantly different from the ones used for model parameterization. We show that our modeling approach surpasses standard modeling methods and illustrate the applicability of our methodology for automated DBaaS provisioning

Integrasi Supplier, Produsen, Dan Pelanggan Pada UKM Keramik Dinoyo Dengan Cloud Computing

Globalization forces company as well as small medium enterprise (SME) to face broader market competition. Ceramics small medium enterprises at Dinoyo, Malang are categorized as popular and competitive SME in Indonesia. Ceramics SME Dinoyo have to do continous improvement in order to win the market. The improvement must do to the entire supply chain, including the order receive or forecast system, the production process, and the delivery process to customer. Supply chain approach will give advantages on the desicion of optimal inventories level, demand fulfillment, and materials to end products quality ensurance. The advantages will be achieved when there is collaboration among echelon of supply chain which consist of supplier, manufacturer / producer, and customer. The collaboration among supplier, producer, and customer needs an information system to integrate all informations from each of them. The integration information system will provide information about stock level or inventory level to supplier. Cloud Computing based Information System is a solution to build a database which gives an easier and faster access for supplier and customer. Cloud computing is a technology using internet service with a virtual server for data maintainance and aplication.This research aims to design software of information system using cloud computing techonolgy to simplify data and information processing for Ceramics SME Dinoyo. The cloud computing aplication integrate information from supplier, producer, and customer by using database sharing. The database sharing by producer facilitates the collaboration between supplier and producer as well as between producer and customer. This research shows that the cloud computing application takes lower cost compare to manual system applied by Ceramics SME Dinoyo

IoT single board computer to replace a home server

Home servers are popular among computer enthusiasts for hosting various applications, including Linux OS with web servers, database solutions, and private cloud services, as well as for VPN, torrent, file-sharing, and streaming. Single Board Computers (SBCs), once used for small projects, have now evolved and can be used to control multiple devices in the IoT space. SBCs have become more powerful and can run many of the same applications as traditional home servers. In light of the energy crisis, this study will examine the feasibility of replacing a conventional home server with an SBC while maintaining service quality and evaluating performance and availability. The power consumption of both solutions will be compared.info:eu-repo/semantics/publishedVersio

IoT Enabled Sensory Monitoring System for Fog Optimal Resource Provisioning Method in Health Monitoring System

Fog is data management and analytics service. In this paper gains and most effective novel approach to provide IoT enabled services in healthcare application using Fog Computing. In this research the data is collected from Google Scholar, Science Director and MEDLINE database. IoT based Fog Computing techniques are proposed for delivering quality of services to the user. Optimal Resource Provisioning method is proposed to find edges, service level agreements and administration services for IoT client. The DeepQ residue information processing technique is applied for connecting data centre of the cloud and computing paradigms technique is finding the depth reference of Fog levels. The proposed Optimal resource provisioning algorithm is examining the dataset and TensorFlow tool is used for simulating environment. Fog computing layer consist of IoT sensor data inputs, data centres for the cloud and connected layers for simulations. The Deep belief network is generated based on above inputs using 256 X 256 X 3 layer system and 5000 trained data, 1000 test data are taken for simulations. Each dataset simulation is recording using supervised and unsupervised learning methods. Based on above results IoT enable Fog Computing data management and analytics systems provided 95% accuracy and the compared with existing computing techniques our proposed systems shows better efficiency with respect to safety and convenience

Mobile Cloud Computing and Its Effectiveness in Business Organizations

E-commerce business organizations aim at achieving the goals and the mission effectively and efficiently so as to satisfy the diverse interests of the stakeholders. MCC is an ICT concept that enables the organizations to enhance the performance when serving the important stakeholders, who include customers, staff, managers, shareholders, and industry regulators. MCC involves the integration of the mobile devices to enable the sharing of the cloud infrastructure. The integration is done via a network and between the computer devices that operate remotely. The Internet is the most common network that enables the mobile devices to utilize the data and information stored in a cloud database. E-commerce businesses prefer the cloud infrastructure because it has a large data storage capacity and high processing speeds. Also, the cloud service providers invest substantial financial, human, and technological resources in ensuring the security of the effective management of the data resources. The main benefit of MCC is that it reduces the businesses expenses. For example, it enables the companies to offer products and services in the international market via the e-commerce infrastructure. Amazon.com is an example of a Multinational Corporation that is successful in offering high-quality services and products to customers in different countries using the website and the mobile app applications that are supported by the cloud infrastructure. Keywords: mobile cloud computing, cloud computing, E-commerce. DOI: 10.7176/IKM/9-1-0

Considerations for a cloud-based system for IoT data acquisition from heterogeneous sensors

Air pollution is a rising concern, demanding for low cost air quality monitoring systems. In this paper, we describe the back-end of an air quality monitoring system, developed in the context of the NanoSen-AQM [3, 4] project, a project with the goal of creating a real-time system that allows for a cost-effective, distributed and ubiquitous air quality monitoring. In particular, we describe the Data Aggregation Module. The NanoSen-AQM [3, 4] project is focused in the air quality monitoring using low-cost nanosensors, developed in the context of the project. The system will have at its core a cloud system that supports a mobile application, a web application and third party platforms. The cloud system starts from receiving the data in cloud system and adding its database, so the data can be monitored by the web application. The cloud system can be divided in two modules: 1) the Central System; and 2) the Data Aggregation Modules. The Data Aggregation Modules collect data from the sensors, acting as a buffer for the messages to be inserted in central system database. The Central System is responsible for storage, processing and data access. The web, mobile and third party applications fetch data from the aforementioned module. The Data Aggregation Modules receives data from the sensors, which then sends it to Central System to be stored. This module can be further divided in two sub-modules: 1) the Data Input; and 2) the Data Publishing Service. The Data Input Module receives data from the RESTFul[1] and MQTT[2] protocols. MQTT is a protocol developed for sensors and IoT thinking how the sensors are exposed to low quality connections. In cases where sensors have low resources and can’t handle the MQTT library, RESTFul is considered the best alternative. In both protocols, there is a Message Authentication Code (MAC) that validates each message integrity. The application that receives the message from the sensors, will also receive an hash. After the message is received in the application, the message is processed and accepted only if the hash is valid. The message then reaches the data publishing service that serves as a buffer to hold the messages before being inserted in the database. Meanwhile, in data publishing service, the messages need to be processed so they can be inserted in the database. The data publishing service uses Apache Kafka with Kafka Streams in order to serve as a buffer and data processing, respectively

Quality of Experience Framework for Cloud Computing (QoC)

Cloud computing provides platform for pay per use services such as software (e.g., database, data processing, application servers, etc.), hardware (e.g., GPUs, CPUs, storage, etc.) and platforms (e.g., Linux, Microsoft Windows and Apple macOS). Previous cloud frameworks use fix policies that do not have the functionality to upgrade services on demand when the user do not receive services according to Service Level Agreement (SLA). Also, there was a lack of functionality to monitor external network and client device resources. This paper presents Quality of experience framework for Cloud computing (QoC) for monitoring the Quality of Experience (QoE) of the end user using video streaming services in the cloud computing environment. The management platform is used for administration purpose in QoC framework that provides facility to easily manage the cloud environment and provide services according to SLA via runtime policy change. The objective QoE/QoS section will automatically monitor the Quality of Service (QoS) data. It will also compare and analyze the subjective QoE submitted by the users and objective QoS data collected by agent based framework for accurate QoE prediction and proper management. The proposed QoC framework has new features of real time network monitoring, client device monitoring and allows changing policy in runtime environment which to our knowledge is currently not provided by existing frameworks