Cloud Based IoT Architecture

The Internet of Things (IoT) and cloud computing have grown in popularity over the past decade as the internet becomes faster and more ubiquitous. Cloud platforms are well suited to handle IoT systems as they are accessible and resilient, and they provide a scalable solution to store and analyze large amounts of IoT data. IoT applications are complex software systems and software developers need to have a thorough understanding of the capabilities, limitations, architecture, and design patterns of cloud platforms and cloud-based IoT tools to build an efficient, maintainable, and customizable IoT application. As the IoT landscape is constantly changing, research into cloud-based IoT platforms is either lacking or out of date. The goal of this thesis is to describe the basic components and requirements for a cloud-based IoT platform, to provide useful insights and experiences in implementing a cloud-based IoT solution using Microsoft Azure, and to discuss some of the shortcomings when combining IoT with a cloud platform

When to Utilize Software as a Service

Cloud computing enables on-demand network access to shared resources (e.g., computation, networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort. Cloud computing refers to both the applications delivered as services over the Internet and the hardware and system software in the data centers. Software as a service (SaaS) is part of cloud computing. It is one of the cloud service models. SaaS is software deployed as a hosted service and accessed over the Internet. In SaaS, the consumer uses the provider‘s applications running in the cloud. SaaS separates the possession and ownership of software from its use. The applications can be accessed from any device through a thin client interface. A typical SaaS application is used with a web browser based on monthly pricing. In this thesis, the characteristics of cloud computing and SaaS are presented. Also, a few implementation platforms for SaaS are discussed. Then, four different SaaS implementation cases and one transformation case are deliberated. The pros and cons of SaaS are studied. This is done based on literature references and analysis of the SaaS implementations and the transformation case. The analysis is done both from the customer‘s and service provider‘s point of view. In addition, the pros and cons of on-premises software are listed. The purpose of this thesis is to find when SaaS should be utilized and when it is better to choose a traditional on-premises software. The qualities of SaaS bring many benefits both for the customer as well as the provider. A customer should utilize SaaS when it provides cost savings, ease, and scalability over on-premises software. SaaS is reasonable when the customer does not need tailoring, but he only needs a simple, general-purpose service, and the application supports customer‘s core business. A provider should utilize SaaS when it offers cost savings, scalability, faster development, and wider customer base over on-premises software. It is wise to choose SaaS when the application is cheap, aimed at mass market, needs frequent updating, needs high performance computing, needs storing large amounts of data, or there is some other direct value from the cloud infrastructure.Siirretty Doriast

A Business Intelligence Solution, based on a Big Data Architecture, for processing and analyzing the World Bank data

The rapid growth in data volume and complexity has needed the adoption of advanced technologies to extract valuable insights for decision-making. This project aims to address this need by developing a comprehensive framework that combines Big Data processing, analytics, and visualization techniques to enable effective analysis of World Bank data. The problem addressed in this study is the need for a scalable and efficient Business Intelligence solution that can handle the vast amounts of data generated by the World Bank. Therefore, a Big Data architecture is implemented on a real use case for the International Bank of Reconstruction and Development. The findings of this project demonstrate the effectiveness of the proposed solution. Through the integration of Apache Spark and Apache Hive, data is processed using Extract, Transform and Load techniques, allowing for efficient data preparation. The use of Apache Kylin enables the construction of a multidimensional model, facilitating fast and interactive queries on the data. Moreover, data visualization techniques are employed to create intuitive and informative visual representations of the analysed data. The key conclusions drawn from this project highlight the advantages of a Big Data-driven Business Intelligence solution in processing and analysing World Bank data. The implemented framework showcases improved scalability, performance, and flexibility compared to traditional approaches. In conclusion, this bachelor thesis presents a Business Intelligence solution based on a Big Data architecture for processing and analysing the World Bank data. The project findings emphasize the importance of scalable and efficient data processing techniques, multidimensional modelling, and data visualization for deriving valuable insights. The application of these techniques contributes to the field by demonstrating the potential of Big Data Business Intelligence solutions in addressing the challenges associated with large-scale data analysis

Parallel programming paradigms and frameworks in big data era

With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines-ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data-typically of heterogeneous nature-in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.Peer ReviewedPostprint (author's final draft

Migrating From SQL to NoSQL Database: Practices and Analysis

Most of the enterprises that are dealing with big data are moving towards using NoSQL data structures to represent data. Converting existing SQL structures to NoSQL structure is a very important task where we should guarantee both better Performance and accurate data. The main objective of this thesis is to highlight the most suitable NoSQL structure to migrate from relational Database in terms of high performance in reading data. Different combinations of NoSQL structures have been tested and compared with SQL structure to be able to conclude the best design to use.For SQL structure, we used the MySQL data that is stored in five tables with different types of relationships among them. For NoSQL, we implemented three different MongoDB structures. We considered combinations of different levels of embedding documents and reference relationships between documents. Our experiments showed that using a mix of one level embedded document with a reference relationship with another document is the best structure to choose. We have used a database that contains five tables with a variety of relationships many-to-one, and many-to-many. Also the huge amount of data stored in all the structures about 2 millions record/document. The research compares clearly between the performances of retrieving data from different MongDB representation of data and the result shows that in some cases using more than one collection to represent huge data with complex relationships is better than keeping all the data in one document

On Implementing Autonomic Systems with a Serverless Computing Approach: The Case of Self-Partitioning Cloud Caches

The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving control path is critical. We show that serverless computing cloud services can be leveraged to solve the complex optimization problems that arise during self-tuning loops and can be used to optimize cloud caches for free. To illustrate that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDiS), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. A cost analysis shows that the serverless computing approach can lead to significant cost savings: The cost of running the controller as a serverless microservice is 0.85% of the cost of the always-on alternative. Through this case study, we make a strong case for implementing the controller of autonomic systems using a serverless computing approach

Extending a methodology for migration of the database layer to the cloud considering relational database schema migration to NoSQL

The advances in Cloud computing and in modern Web applications have raised the need for highly available and scalable distributed databases to accommodate the big data being created and consumed. Along with the explosion in data growth comes the necessity to rapidly evolve databases and schemas to meet user demands for new functionality. A special attention is being paid to the vast amounts of semi-structured and un-structured data, and the data management tools should reflect the support for these needs. This has lead to the development of new Cloud serving systems such as "Not Only" SQL (NoSQL) databases. NoSQL databases were driven by the scalability needs of the big companies, such as Google, Facebook, Amazon, and Yahoo. While the demands of these key players are different from those of small and medium enterprises in terms of scalability, the core problem is the same - storage arrays are not scalable and force you into expensive, forklift upgrades. These facts combined with changes in how IT resources are delivered and consumed through the Cloud computing paradigm, projects adopting NoSQL solutions are not a hype anymore. NoSQL databases are being offered as a service by the big Cloud providers, such as Google, Amazon, Microsoft, but by smaller vendors as well. In this master thesis we investigate the possibilities and limitations of mapping relational database schemas to NoSQL schemas when migrating the database layer to the Cloud. Based on literature research we provide recommendations and guidelines with regard to schema transformation and discuss the implications at other application architecture layers, such as business logic and data access layer. We extend an existing data migration tool and methodology for incorporating the migration guidelines and hints. Moreover, we validate our work based on a chosen sub-set of relational and NoSQL databases by using example data from the established TPC-H benchmark

Enterprise Search Technology Using Solr and Cloud

Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world\u27s largest internet sites. Databases and Solr have complementary strengths and weaknesses. SQL supports very simple wildcard-based text search with some simple normalization like matching upper case to lower case. The problem is that these are full table scans. In Solr all searchable words are stored in an inverse index , which searches orders of magnitude faster. Solr is a standalone/cloud enterprise search server with a REST-like API. You put documents in it (called indexing ) via XML, JSON, CSV or binary over HTTP. You query it via HTTP GET and receive XML, JSON, CSV or binary results. The project will be implemented using Amazon/Azure cloud, Apache Solr, Windows/Linux, MS-SQL server and open source tools