What is Cloud computing? What are the types of cloud computing?

What is Cloud computing

• Uses Internet technologies to offer scalable and  elastic services. The term “elastic computing” refers to the ability of dynamically acquiring computing resources and  supporting a variable workload.
• The resources used for these services can be metered and the users can be charged only for the resources they used.
• The maintenance and security are ensured by service providers.
• The service providers can operate more efficiently due to specialization and centralization.
• Lower costs for the cloud service provider are past to the cloud users.
• Data is stored: ¨closer to the site where it is used. ¨ in a device and in a  location-independent manner.
• The data storage strategy can increase reliability, as well as security, and can lower communication costs.

Types of clouds

• Public Cloud - the infrastructure is made available to the general public or a large industry group and is owned by the organization selling cloud services.
• Private Cloud – the infrastructure is operated solely for an organization.
• Community Cloud - the infrastructure is shared by several organizations and supports a community that has shared concerns.
• Hybrid Cloud - composition of two or more clouds (public, private, or community) as unique entities but bound by standardized technology that enables data and application portability.

Introduction to Network centric computing and Network centric content. What are the advantages of Network centric computing and content?

Introduction to Network-centric computing

• Information processing can be done more efficiently on large farms of computing and storage systems accessible via the Internet.
• Grid computing – initiated by the National Labs in the early 1990s; targeted primarily at scientific computing.
• Utility computing – initiated in 2005-2006 by IT companies and targeted at enterprise computing.
• Grid computing is distributed system, a large number of loosely coupled, heterogeneous, and geographically dispersed systems in different administrative domains. The term grid computing is a metaphor of electric grid.
• The focus of utility computing is on the business model for providing computing services; it often requires a cloud-like infrastructure.
• Cloud computing is a path to utility computing embraced by major IT companies including: Amazon, HP, IBM, Microsoft, Oracle, and others.

Network-centric content

• Content: any type or volume of media, be it static or dynamic, monolithic or modular, live or stored, produced by aggregation, or mixed.
• The “Future Internet” will be content centric.
• The creation and consumption of audio and visual content is likely to transform the Internet to support increased quality in terms of resolution, frame rate, color depth, stereoscopic information.

Network-centric computing and content

• Data-intensive:  large scale simulations in science and engineering require large volumes of data. Multimedia streaming transfers large volume of data.
• Network-intensive:  transferring large volumes of data requires high bandwidth networks.
• Low-latency networks for data streaming, parallel computing, computation steering.
• The systems are accessed using thin clients (e.g., Google Chrome OS) running on systems with limited resources, e.g., wireless devices such as smart phones and tablets.
• The infrastructure should support some form of workflow management, i.e., complex computational tasks require coordination of several applications

Advantages of Network-centric computing and content

• Computing and communication resources (CPU cycles, storage, network bandwidth) are shared and resources can be aggregated to support data-intensive applications
• Data sharing facilitates collaborative activities
• Cost reduction: pay as you go model, eliminate initial investment, reduces significantly maintenance and operation costs User convenience and elasticity, accommodate very large peak-to-average ratios

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Difference between Data Mining and Data Analytics

Data Mining– Data mining is a systematic and sequential process of identifying and discovering hidden patterns and information in a large dataset. It is also known as Knowledge Discovery in Databases. It has been a buzz word since 1990’s


Data Analysis– Data Analysis, on the other hand, is a superset of Data Mining that involves extracting, cleaning, transforming, modeling and visualization of data with an intention to uncover meaningful and useful information that can help in deriving conclusion and take decisions. Data Analysis as a process has been around since the 1960's

Difference of Data Analytics and Machine Learning

Data Analysis

“Data science is to gain actionable insights from data”. The data that we deal with can be historical data or Real-time data. Actionable insights are important, because at the end, every data analytics project is performed with an aim to make a decision making process easier. Anything that helps to make the decision making process easier is an Actionable Insight.

Examples:

 1. Providing a CEO with a comprehensive dashboard to monitor the market performance of a new product, by visualization sales data.

Figure 1: Where Data Analytics located in the cross domains.

2. Predicting the sales of the new product in the next quarter. 

Machine Learning

Machine Learning can be thought of as a collection of techniques which enables computers to perform expertise tasks.

Examples: 

1. Our Previous example, predicting the sales of a product, is one of the most common use-case of Machine Learning termed as predictive modelling.

2. Image Classification - Google photos can identify the objects in a picture and even the occasion in which the picture was taken. Explore Computer Vision to know more.


Similarities There are many task, predictive modelling for instance,that involve both Data Analysis and Machine Learning. Data Analysis is performed to clean, transform the data, and extract the right information from the data (Feature Engineering) to input it to the Machine Learning technique. Machine learning model performs the expert task on the input, in case of predictive modelling to predict a value or classify each input record.

Difference In both the fields there are some cases in which the involvement of the other is minimum. For Instance, our first example of dashboard does not involve Machine Learning, because there no expert task involved.Likewise, our last example of computer vision, have a very minimum involvement of data analytics, and much of Machine Learning (read neural networks).

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