Notes for BSc CSIT

  Applications that can benefit from cloud computing 1) Scientific Applications Scientific applications have been run on both traditional high-performance computing (HPC) systems such as supercomputers and clusters, as well as high throughput computing (HTC) platforms such as Grids, for many years. Many businesses have employed classical HPC to assist tackle a range of problems since they have access to enormous quantities of computer power. Although these systems were often built to solve a specific problem, a growing number of SMEs and even university departments began to use general-purpose HPC systems. Scientists, engineers, system administrators, and developers have been investigating HPC Cloud environments to take advantage of what Cloud Computing has to offer them as new growing technology. Running complex scientific applications has become more accessible to the research community thanks to the popularity of Cloud Computing, which allows researchers to access on-demand compute

 More Uses of Cloud Computing 1. Scalable Usage:  Through different subscription arrangements, cloud computing provides scalable resources. This means you will only be charged for the computer resources you utilize. This allows for the management of demand spikes without the need to invest in computer hardware on a long-term basis. Netflix, for example, takes advantage of cloud computing's capabilities. It experiences substantial spikes in server load during peak periods due to its on-demand streaming service. The shift to the cloud from on-premises data centers allowed the firm to dramatically grow its client base without having to engage in costly infrastructure setup and upkeep. 2. Chatbots:  We can save information about customer preferences thanks to the cloud's increased computational power and capacity. Users' behavior and preferences may be leveraged to give personalized solutions, messages, and goods. The cloud-based natural-language intelligent bots Siri, Alexa, a

 Applications of Cloud Computing 1) Scientific Applications Scientific applications have been run on both traditional high-performance computing (HPC) systems such as supercomputers and clusters, as well as high throughput computing (HTC) platforms such as Grids, for many years. Many businesses have employed classical HPC to assist tackle a range of problems since they have access to enormous quantities of computer power. Although these systems were often built to solve a specific problem, a growing number of SMEs and even university departments began to use general-purpose HPC systems. Scientists, engineers, system administrators, and developers have been investigating HPC Cloud environments to take advantage of what Cloud Computing has to offer them as new growing technology. Running complex scientific applications has become more accessible to the research community thanks to the popularity of Cloud Computing, which allows researchers to access on-demand compute resources in minutes

 The architecture of the Aneka-based computing cloud  The Aneka-based computing cloud is a collection of physical and virtualized resources that are linked via a network, which can be the Internet or a private intranet. Each of these resources contains an instance of the Aneka Container, which represents the runtime environment in which the distributed apps operate. The container offers the fundamental management features of a single node and leverages all other activities on the services that it hosts. The services are divided into three categories: fabric, foundation, and execution. Fabric services provide hardware profiling and dynamic resource provisioning by interacting directly with the node via the Platform Abstraction Layer (PAL). The fundamental system of the Aneka middleware is identified by foundation services, which provide a collection of fundamental capabilities that allow Aneka containers to conduct specialized sets of jobs. Execution services deal directly with the sche

  Aneka Manjrasoft is dedicated to developing revolutionary software solutions that facilitate the development and deployment of applications on private or public Clouds. Their product, Aneka, serves as an Application Platform as a Service for Cloud Computing. Aneka provides a variety of programming paradigms such as Task Programming, Thread Programming, and MapReduce Programming, as well as tools for quick application development and smooth deployment on private or public Clouds to distribute applications. Aneka is a cloud-based platform and framework for constructing distributed applications. It makes use of the idle CPU cycles of a heterogeneous network of desktop PCs, servers, and data centers. Aneka provides a comprehensive set of APIs for developers to transparently use such resources and express the business logic of apps using the chosen programming abstractions. To monitor and govern the deployed infrastructure, system administrators might use a set of tools. This can be a pub

 Benefits of Hadoop Scalability: Unlike traditional systems, which have storage limitations, Hadoop is scalable since it functions in a distributed environment. This enabled data architects to create early Hadoop data lakes.  Resilience: The Hadoop Distributed File System (HDFS) is intrinsically robust. To prepare for the risk of hardware or software failures, data stored on each node of a Hadoop cluster is also duplicated on other nodes of the cluster. This design is purposely redundant to ensure failure tolerance. If one node fails, there is always a backup of the data in the cluster. Flexibility: Unlike typical relational database management systems, Hadoop allows you to store data in any format, including semi-structured or unstructured data. Hadoop allows organizations to readily access new data sources and access various sorts of data.  Challenges with Hadoop Architectures Complexity: Hadoop is a low-level, Java-based platform that might be too complicated to deal with for end u

 Apache Hadoop Apache Hadoop is a Java-based open-source software framework for scalable and distributed computing that manages data processing and storage in large data applications. Hadoop distributes big data sets and analytical jobs among nodes in a computing cluster, breaking them down into smaller tasks that may be handled concurrently. Hadoop can process both organized and unstructured data and scale up from a single server to thousands of computers in a reliable manner. The Apache Hadoop software library provides a platform for distributed processing of massive data volumes across computer clusters using simple programming techniques. It is intended to grow from a single server to thousands of computers, each of which provides local computing and storage. Rather than relying on hardware to provide high availability, the library is designed to identify and manage problems at the application layer, giving a highly available service on top of a cluster of machines, each of which m

 The core modules of Hadoop include: Hadoop Common: The common utilities that support the other Hadoop modules. Hadoop Distributed File System (HDFS): A distributed file system that provides high-throughput access to application data. Hadoop Yet Another Resource Negotiator (YARN): A framework for job scheduling and cluster resource management. Hadoop MapReduce: A YARN-based system for parallel processing of large data sets. Hadoop Ozone: An object store for Hadoop Examples of Popular Hadoop-related Software Popular Hadoop packages that are not strictly a part of the core Hadoop modules, but that is frequently used in conjunction with them, include: Apache Hive is data warehouse software that runs on Hadoop and enables users to work with data in HDFS using a SQL-like query language called HiveQL. Examples of Popular Hadoop-related Software  Popular Hadoop packages that are not strictly a part of the core Hadoop modules, but that are frequently used in conjunction with them, include: 

 Azure for DR and Backup Azure is used by certain businesses for data backup and disaster recovery. Azure may also be used as a substitute for a company's own data center. Instead of investing in local servers and storage, many businesses choose to operate some or all of their business apps in Azure. Microsoft has Azure data centers placed all around the world to ensure availability. Microsoft Azure services are offered in 55 regions and 140 countries as of January 2020. Regrettably, not all services are accessible in every location. As a result, Azure customers must verify that their workloads and data storage locations adhere to any applicable compliance standards or other regulations. Privacy Privacy is a big worry for cloud customers due to data security issues and regulatory compliance obligations. To alleviate these concerns, Microsoft established the online Trust Center, which gives thorough information on the company's security, privacy, and compliance operations. Accor

 Azure Products and Services Microsoft categorizes Azure cloud services into more than two dozen groups, which include: 1. Compute: These services allow users to deploy and manage virtual machines, containers, and batch tasks, as well as enable remote application access. Depending on whether the resource needs to be available to the outside world, compute resources established in the Azure cloud can be configured with either public or private IP addresses. 2. Mobile: T hese solutions aid developers in the creation of cloud apps for mobile devices by offering notification services, back-end task support, tools for creating application program interfaces (APIs), and the ability to combine geographic context with data.  3. Web: These services aid in the creation and deployment of web applications. They also provide search, content delivery, API administration, alerting, and reporting tools.  4. Storage: T his service category offers scalable cloud storage for organized and unstructured d

Uses of Microsoft Azure Microsoft Azure's use cases are quite diversified because it comprises multiple service offerings. Running virtual machines or containers in the cloud is one of Microsoft Azure's most common uses. These computational resources can run infrastructure components including domain name system (DNS), Windows Server services like Internet Information Services (IIS), or third-party apps. Third-party operating systems, such as Linux, are also supported by Microsoft. Azure is also extensively utilized as a platform for cloud-based database hosting. Microsoft provides serverless relational databases like Azure SQL as well as non-relational databases like NoSQL.  Furthermore, the platform is commonly used for disaster recovery and backup. Many businesses utilize Azure storage as an archive to satisfy their long-term data preservation obligations.

 Google App Engine (Gae) Google App Engine (GAE) or just 'App Engine is a cloud computing platform as a service that allows you to create and run web applications in Google-managed data centers. Applications are sandboxed and run across multiple servers. App Engine provides automatic scaling for web applications, which means that when the number of requests for an application rises, App Engine automatically assigns more resources to the web application to accommodate the increased demand. Google App Engine is a Google Cloud Platform service that helps build highly scalable applications on a fully managed serverless platform. It generally supports apps written in Go, PHP, Java, Python, Node.js, .NET, and Ruby, but it can also support additional languages via "custom runtimes." The service is free up to a particular number of consumed resources, and it is only available in a regular environment, not a flexible environment. Fees are levied for additional storage, bandwidth,

Different categories of  Google Cloud Platform  Al and Machine Learning, API Management, Compute, Containers, Data Analytics, Databases, Developer Tools, Healthcare and Life Sciences, Hybrid and Multicloud, Internet of Things (IoT), Management Tools, Media and Gaming, Migration, Networking, Operations, Security and Identity, and Serverless Computing and Storage are the categories of Google Platform. Under these categories,GCP encompasses 100of products. Those products are listed here and some of the most important products are briefly described. AI and Machine Learning VERTEX AI Vertex AI Unified platform for training, hosting, and managing ML models.  Deep Learning VM Image Preconfigured VMs for deep learning applications. Notebooks An enterprise notebook service to get projects up and running in minutes. Deep Learning Containers Preconfigured and optimized containers for deep learning environments.  Vertex Data Labeling Managed annotation for high-quality model training data. TensorF