To order pick-up food by using your computer or phone is nothing new. Food delivery companies such as FoodHero and Uber Eats along with many other around the world base their entire company idea around the food order and delivery process. For a company to stand out in such a vast market can sometimes be quite tricky. Sometimes your company needs a niche to stand out in the crowd. This project aims to create such a niche in an order system prototype based on voice-controlled systems and conversation. This prototype allows users to place food orders through only the use of natural speech and a voice assistant. The prototype utilizes products and services from both Amazon and Google to create the order system structure. The ordering system also takes advantage of the serverless architecture that both Amazon and Google provide. The end result of this project is a simple, convenient, and user-friendly prototype

Cloud computing has been given a great deal of attention during recent years. Almost all the technology market leaders and leading hosting service providers (like IBM, Microsoft and Verizon) have entered into the Cloud market as Cloud Providers. Cloud computing promises to provide highly available, secure, low cost, agile and highly scalable solution to the consumers. Scania is a global company and one of the world’s leading heavy vehicle manufacturers with 35,000+ employees. All the large organizations such as Scania, aim to constantly update themselves with the latest technology in order to meet their business requirements but, these organizations must always be convinced that there is a strong reason(s) to implement new technology. This research provides the method and criteria in relation to initiating Cloud computing. A number of Scania’s specific business requirements that it is possible to map to the Cloud are addressed in this thesis. The methodology of research is split in two parts. Firstly, the identification of business cases at Scania and their requirements with the Cloud and Secondly, the evaluation and comparison of the functionalities and capabilities of different vendors. The accumulated data is then compared and suitable vendors, according to those business requirements are suggested. This thesis also shares the experience of moving on premise applications to the Cloud. These are Scania specific applications which are currently being hosted in-house. The research also addresses the possibilities of portability between the Cloud providers. Although there is no standardization in relation to Cloud computing, some initiatives such as OpenStack are available and its current position and some application and data migration tools are also discussed. The thesis concludes with a general discussion, recommendations in relation to adapting Cloud computing and selecting the Cloud provider. This recommendation applies to every organization including Scania.

With the advent of cloud computing, resizable scalable infrastructures for data processing is now available to everyone. Software platforms and frameworks that support data intensive distributed applications such as Amazon Web Services and Apache Hadoop enable users to the necessary tools and infrastructure to work with thousands of scalable computers and process terabytes of data. However writing scalable applications that are run on top of these distributed frameworks is still a demanding and challenging task. The thesis aimed to advance the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large data sets, collectively known as “big data”. The term “big-data” in this thesis refers to large, diverse, complex, longitudinal and/or distributed data sets generated from instruments, sensors, internet transactions, email, social networks, twitter streams, and/or all digital sources available today and in the future. We introduced architectures and concepts for implementing a cloud-based infrastructure for analyzing large volume of semi-structured and unstructured data. We built and evaluated an application prototype for collecting, organizing, processing, visualizing and analyzing data from the retail industry gathered from indoor navigation systems and social networks (Twitter, Facebook etc). Our finding was that developing large scale data analysis platform is often quite complex when there is an expectation that the processed data will grow continuously in future. The architecture varies depend on requirements. If we want to make a data warehouse and analyze the data afterwards (batch processing) the best choices will be Hadoop clusters and Pig or Hive. This architecture has been proven in Facebook and Yahoo for years. On the other hand, if the application involves real-time data analytics then the recommendation will be Hadoop clusters with Storm which has been successfully used in Twitter. After evaluating the developed prototype we introduced a new architecture which will be able to handle large scale batch and real-time data. We also proposed an upgrade of the existing prototype to handle real-time indoor navigation data.

The expanding adoption of cloud computing services by businesses has transformed IT infrastructure and data management in the computing space. Cloud computing offers advantages such as availability, scalability, and cost-effectiveness, making it a favored choice for businesses of all sizes. The aim of this thesis is to compare private and public cloud computing services in terms of pricing and implementation effort as well as comparing the cloud providers to each other. The top three cloud providers that will be examined are Google GCP, Microsoft Azure, and Amazon AWS. The study examines different pricing models and evaluates their effectiveness in different business scenarios. In addition, the thesis also discusses the challenges associated with building and maintaining private infrastructure and the deployment of applications to cloud computing service are examined. The research methodology involves data collection, analysis, and a case study of developing and deploying a ticketing system application on different cloud platforms. The ticket system helps to provide a realistic example and investigation of the cloud providers. The findings will help companies make informed decisions regarding the selection of the most appropriate cloud computing service based on pricing models and implementation efforts. The thesis provides valuable information on private and public cloud computing and recommends appropriate pricing models for different scenarios. This study adds to existing knowledge by analyzing current pricing models and deployment concepts in cloud computing. The thesis does not propose new solutions but follows a structured format compiling information on private, and public cloud computing and a comprehensive review of cloud computing pricing models and marketing efforts. / Den växande adoptionen av molntjänster inom företag har förändrat IT-infrastrukturen och datahanteringen inom datorområdet. Molntjänster erbjuder fördelar såsom tillgänglighet, skalbarhet och kostnadseffektivitet, vilket gör det till ett populärt val för företag i alla storlekar. Syftet med denna avhandling är att jämföra privata och offentliga molntjänster med avseende på prissättning och implementeringsinsatser samt att jämföra molnleverantörerna med varandra. De tre främsta molnleverantörerna som kommer att undersökas är Google GCP, Microsoft Azure och Amazon AWS. Studien undersöker olika prismodeller och utvärderar deras effektivitet i olika affärsscenarier. Dessutom diskuterar avhandlingen också utmaningarna med att bygga och underhålla privat infrastruktur samt implementeringen av applikationer till molntjänster. Forskningsmetodologin omfattar datainsamling, analys och en fallstudie av utveckling och implementering av ett support system på olika molnplattformar. Supportsystemet hjälper till att ge ett realistiskt exempel och undersökning av molnleverantörerna. Resultaten kommer att hjälpa företag att fatta informerade beslut när det gäller valet av lämpligaste molntjänst baserat på prismodeller och implementeringsinsatser. Avhandlingen tillhandahåller värdefull information om privat och offentlig molntjänst och rekommenderar lämpliga prismodeller för olika scenarier. Denna studie bidrar till befintlig kunskap genom att analysera nuvarande prismodeller och implementeringskoncept inom molntjänster. Avhandlingen föreslår inga nya lösningar, men följer en strukturerad format genom att sammanställa information om privat och offentlig molntjänst samt en omfattande översikt av prismodeller och marknadsinsatser inom molntjänster.