Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

Many of the currently existing solutions for cloud cost optimisation are aimed at cloud infrastructure providers, and they often deal only with specific types of application services, leaving the providers of cloud applications without the suitable cost optimization solution, especially concerning the wide range of different application types. In this paper, we present an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, with the aim of minimising the operational cost of the cloud service, while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, the knowledge is gained about the effects that the cloud application context parameters have on the service cost and quality of service, which is then used to determine the optimised service deployment option. The service models are validated using cloud application services deployed in laboratory conditions, and the optimisation method is validated using the simulations based on proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service, and use this information in the optimisation aimed at reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain useful insights regarding service deployment decision without acquiring extensive datasets for the analysis.

OPTIMIZATION OF MICROSOFT OFFICE 365 FOR BLENDED LEARNING-BASED LEARNING IN NATURAL SCIENCE SUBJECTS

Blended learning is one of the innovative learning models that integrates technology in line with the demands of learning in the 21st century and is relevant to learning in the pandemic era. Microsoft Office 365 is an office cloud application from Microsoft such as the desktop version of Microsoft Office that allows users to access e-mail, documents, contacts, calendars and collaborate anywhere and using various devices. This technology is one of the technologies that can help the learning process run with the Blended Learning model. The purpose of this study is to review the optimization of Microsoft Office 365 in Blended Learning-based learning. The type of research used is research with literature study method.

Design of Centralized Intelligent Expert System and Contamination Detection of Tissue Cultured Sugarcane Crop

This paper presents the design a cloud based IoT enabled smart agriculture application for Hi-Tech tissue cultured sugarcane crop entitled “Design of Centralized Intelligent Expert System and Contamination Detection of Tissue Cultured Sugarcane Crop”. This expert system comprises of Raspberry Pi-4 (RPi), Arduino-Mega, GSM-Modem (Sim900) and sensor-modules for monitoring and control of essential parameters of laboratory for monitoring the physical parameters. The parameters monitored are temperature, humidity and light intensity of the tissue culture growth rooms with artificial day light timing and control, however, AI-based health prediction suggests the image processing for detection of culture contamination of sugarcane crop inside the growth-room. In addition, fire-smoke sensor and methane gas sensor are incorporated for fire protection and to avoid any disastrous situation. Three numbers of webcams are attached to the RPi for monitoring growth and health of explants. An AI-Model / weight was developed for detection of contamination that predicts the for health of Tissue Cultured Sugarcane Crop. Moreover, image enhancement was covered applying Generative Adversarial Networks (GAN)”. In this system, the RPi reads sensor's data through Arduino and convert it to data-frame with timestamp and geo-tag. The data along with the captured images are sent to a centralize cloud application for applying data mining and Artificial Intelligence; however, the model of contamination detection has been applied at edge device. This is to get meaningful insights of data for future decision making in maximizing crop yield and quality. Due to the great need of sugarcane crop in Pakistan, the Plant Tissue Culture (PTC) technology has been incorporated with Artificial Intelligence, the proposed system is aimed to be installed at established PTC-growth-rooms for sugarcane crop so the experts of field can be connected to the cloud application for its monitoring, control and data analytics. In addition, the use of telepresence through cloud application will enable PTC-experts to provide assistance to the remote user and resolve their issues timely, thus extending PTC technology all over the country which will eventually lead to increased crop yield with quality products in affordable price.

An Experimental Performance Evaluation of Cloud-API-Based Applications

Cloud Application Programming Interfaces (APIs) have been developed to link several cloud computing applications together. API-based applications are widely used to provide flexible and reliable services over cloud platforms. Recently, a huge number of services have been attached to cloud platforms and widely utilized during a very short period of time. This is due to the COVID-19 lockdowns, which forced several businesses to switch to online services instantly. Several cloud platforms have failed to support adequate services, especially for extended and real-time-based applications. Early testing of the available platforms guarantees a level of suitability and reliability for the uploaded services. In this work, we first selected two different API-based applications from education and professional taxonomies, the two most recently used applications that have switched to the cloud environment. Then, we aimed to evaluate the performance of different API-based applications under different cloud platforms, in order to measure and validate the ability of these platforms to support these services. The advantages and drawbacks of each platform were experimentally investigated for each application.

Load balancing method for KDN-based data center using neural network

The growth of cloud application services delivered through data centers with varying traffic demands unveils limitations of traditional load balancing methods. Aiming to attend evolving scenarios and improve the overall network performance, this paper proposes a load balancing method based on an Artificial Neural Network (ANN) in the context of Knowledge-Defined Networking (KDN). KDN seeks to leverage Artificial Intelligence (AI) techniques for the control and operation of computer networks. KDN extends Software-Defined Networking (SDN) with advanced telemetry and network analytics introducing a so-called Knowledge Plane. The ANN is capable of predicting the network performance according to traffic parameters paths. The method includes training the ANN model to choose the path with least load. The experimental results show that the performance of the KDN-based data center has been greatly improved.

A Multi-Objective Differential Evolutionary Optimization Method for Performance Optimization of Cloud Application

Due to the limited search space in the existing performance optimization ap-proaches at software architectures of cloud applications (SAoCA) level, it is difficult for these methods to obtain the cloud resource usage scheme with optimal cost-performance ratio. Aiming at this problem, this paper firstly de-fines a performance optimization model called CAPOM that can enlarge the search space effectively. Secondly, an efficient differential evolutionary op-timization algorithm named MODE4CA is proposed to solve the CAPOM model by defining evolutionary operators with strategy pool and repair mechanism. Further, a method for optimizing performance at SAoCA level, called POM4CA is derived. Finally, two problem instances with different sizes are taken to conduct the experiments for comparing POM4CA with the current representative method under the light and heavy workload. The ex-perimental results show that POM4CA method can obtain better response time and spend less cost of cloud resources.

Literature Review on Cloud Application

The software that runs its processing logic is a cloud application. In this the data is stored between two systems: client-side and server-side. End-users local hardware and remote server is also a part where some processing is done. However, most data storage exists on a remote server which is one of the major perk of using cloud application. In some cases a local device with no storage space is built with cloud application. Using web browser cloud application interacts with its users; this facility makes the organizations to switch their infrastructure to the cloud for gaining the benefit of digital transformations. In cloud applications it is easier for the clients to move or manage their data safely and it also provides the flexibility required for the emerging organizations to sustain in the digital market. As the cloud applications are emerged with sophistication many papers were employed on its branches. This research paper emphasizes on the evolution and long-term trends of cloud applications. Findings from the paper enable the enterprise with perplexity to decide on adopting cloud.

Development and Analysis of Low-Cost IoT Sensors for Urban Environmental Monitoring

The accelerated pace of urbanization is having a major impact over the world’s environment. Although urban dwellers have higher living standards and can access better public services as compared to their rural counterparts, they are usually exposed to poor environmental conditions such as air pollution and noise. In order for municipalities and citizens to mitigate the negative effects of pollution, the monitoring of certain parameters, such as air quality and ambient sound levels, both in indoor and outdoor locations, has to be performed. The current paper presents a complete solution that allows the monitoring of ambient parameters such as Volatile Organic Compounds, temperature, relative humidity, pressure, and sound intensity levels both in indoor and outdoor spaces. The presented solution comprises of low-cost, easy to deploy, wireless sensors and a cloud application for their management and for storing and visualizing the recorded data.