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In my opinion, cloud security architecture is effective only if the correct defensive implementations are in place. An efficient cloud security architecture should recognize the issues that will arise with security management. The security management addresses these issues with security controls. These controls are put in place to safeguard any weaknesses in the system and reduce the effect of an attack. While there are many types of controls behind a cloud security architecture, they can usually be found in one of the following categories:
Deterrent controls:
These controls are intended to reduce attacks on a cloud system. Much like a warning sign on a fence or a property, deterrent controls typically reduce the threat level by informing potential attackers that there will be adverse consequences for them if they proceed. (Some consider them a subset of preventive controls.)
Preventive controls:
Preventive controls strengthen the system against incidents, generally by reducing if not actually eliminating vulnerabilities. Strong authentication of cloud users, for instance, makes it less likely that unauthorized users can access cloud systems, and more likely that cloud users are positively identified.
Detective controls:
Detective controls are intended to detect and react appropriately to any incidents that occur. In the event of an attack, a detective control will signal the preventative or corrective controls to address the issue. System and network security monitoring, including intrusion detection and prevention arrangements, are typically employed to detect attacks on cloud systems and the supporting communications infrastructure.
Corrective controls:
Corrective controls reduce the consequences of an incident, normally by limiting the damage. They come into effect during or after an incident. Restoring system backups in order to rebuild a compromised system is an example of a corrective control.

Mainframe computing - Flexible infrastructure that can support virtualized environments; scalable support for big data analytics and new applications; simple migration processes; and reduced total cost of ownership compared to a private cloud. The advantage of mainframe computing is higher reliability. If an internet connection goes down, or if the host servers go down, the client will be unable to access its data.
Cloud Computing - Scattered across many individual servers; cloud can be accessed from anywhere, businesses can increase access to the server as they grow simply by adding further clients. They can even extend this access to mobile devices. On cloud service provider, you're effectively putting your trust in that service provider that your data will be safe, as it will be kept on servers that are owned by them.

While I agree that cloud computing has the potential to displace a large portion of the server industry, I do not believe that it will ever be able to fully replace dedicated servers. This is because dedicated servers provide privacy and security that cloud servers cannot match. Cloud services will be widely regarded as the top option for small and medium-sized enterprises. However, businesses that deal with sensitive information, such as banks, government organizations (including military and police), and multinational corporations, will always choose dedicated server hosting. Many businesses now use ""hybrid servers"" for this same reason. Since cloud servers are more practical in terms of price, storage requirements, and ease of access, they are destined to gain market share. Hosting your own server on a dedicated server may be rather expensive. But they also come with their own advantages that cloud servers are unable to match just yet.

Hi,
I feel really good to see lots of interesting questions to answer one after another. Thanks to you for asking this.
In next five years, you will see companies alloting very tiny space for their server room, SMEs and startup companies they will be completely engrossed in their product improvement and business development and what about their IT infrastructure? Everything will be taken care by cloud computing providers for sure. The Iaas, Paas model will support their applications and Saas product with salable, 100% up time even way better performance.
Companies won't be worried about even any downtime because DevOps and automation will take care this headache with Docker container model combination with Python, Jason, chef script.
The big data, SAP HANA will rule the world in order to a large scale of analytic and no SQL model. It will be far easy to analysis large Logs even 1 TB data analysis. AI/ Machine learning, IoT will bring a completely new paradigm shift. You and I will have maybe 1 GBPS internet connection to connect our gigantic infrastructure and manage without any system admin because everything will be server less and orchestration model.

Cloud computing is a model for on-demand provisioning of computer resources (including servers, storage, databases, and applications) through the Internet, based on a client-server architecture. Because of its many benefits to businesses, such as lower overhead and more productivity, efficiency, performance, data backups, disaster recovery, and security, cloud computing is quickly becoming a mainstream alternative. Another name for grid computing is "distributed computing. " It connects several computers and other devices (such as servers, workstations, and storage devices) so that they may be accessed collectively. The primary benefits of grid computing are increased user productivity due to improved visibility into and control over available resources, and faster overall task completion.

Cloud Computing is the course in which they learn about cloud computing software, common set of core techniques, algorithm and design philosophies. Some of these concepts include clouds, MapReduce, key-value/NoSQL stores about C+ Program.
For learning cloud computing, here are few best books in the market.
1. Cloud Computing Concept, Technology and Architecture by Thomas Erl. The book is published by Pearson Education and one of the highest selling book for Cloud Computing.
2. Cloud Computing Principle and paradigm By Rajkumar Buyya and James Broberg.
3. Cloudonomics: The Business Value of Cloud Computing by Joe Weinman.
4. Cloud Computing Design Patterns.
5. Cloud Computing Black Book by Kailash Jayaswal, Donald J. Houde.
6. Cloud Computing Bible Book By Barrie Sosinsky.
7. Cloud Computing From Beginning to End by MR Ray J Rafaels.
8. Cloud Computing: Theory and Practice
by then C. Marinescu.
9. Architecting the Cloud: Design Decisions for Cloud Computing Service Models (SaaS, PaaS, & IaaS) By Michael J Kavis and published by international publisher Wiley.
10. Building the Infrastructure for Cloud Security by Raghuram Yeluri.

The role of MSPs (and Cloud Service Providers/CSPs) is becoming more prominent with each passing year. The likelihood of legislation being written which impacts MSPs is high. In fact, such laws have already been enacted in China, requiring the assistance of technology companies in assisting the Chinese government with terror-related investigations.
Look for this type of law in other jurisdictions around the world. Increased cloud-based adoptions from the enterprises and heavy competition in today's world add to the pressure of having the best IT infrastructure by enterprises. Having a good IT infrastructure without a properly managed service diminishes the profits it has on offer.
Moreover, the enterprise loses on its core competency. Managing the physical as well as the software infrastructure inside the data centers is instead offered to the more skilled Managed Services vendors in the market. Traditional, as well as cloud-based hosting is managed by the vendors.

Firstly, I'd be cautious to make a claim that C++ is better than Java. There are pros and cons to both sides.
C++ is basically more power to the programmer (since one can break past all sorts of abstractions), and runs natively instead of on a virtual environment (and hence is also platform dependent). A C++ application can manage its own memory in more finer ways than a Java app can. In C++, one can overload operators, access physical addresses and override values there. One can throw and catch any type (.) and do some nasty stuff around all that. On the other hand, in Java, one can sit back and basically just deal with OO abstractions and code them up in one-way, regardless of the underlying platform. Fine-grained memory management is harder. One can play around with GC params for optimizations and get really far this way, but anything more is not really achievable. In C++, you can easily get as deep as you want to.
But, also remember, with great power comes great responsibility. An irresponsible Java developer would be less harmful to your software, than an irresponsible C++ developer would be. In that sense, Java is a relatively easier and simpler language to work with, than C++ is. With C++, a lot of responsibility and burden is on the shoulders of the programmer and with Java, a lot of this, is taken care of, by the system.
There is also a popular performance myth with C++ and Java. Since C++ runs natively, it used to run faster in most platforms than Java does, given the VM is basically emulated. However, this is increasingly being rendered moot, as JVMs end up being very powerful, and processor speeds and architectures have evolved a lot over time.

Hello,
In the IT world, there is an excessive demand of those programmers who are expert in the field of R programming. There is no specific eligibility criteria or any prerequisite to learn R programming at all. This is the easiest computer language. If you are smart enough, then you can even learn this language while just reading books and watching the tutorials on YouTube.
This programming is in demand, because R language can handle the enormous amount of business data. It is much possible that system may get out of order, because of storage incapacity. R programming can easily solve this problem, because this language can handle the petabytes of business data. Another big feature or advantage of learning this is that it is stastical application more than just a language. Hence, it supports embedding compiled code in other language like FORTRAN or C.
So, this makes the R Programming an essential elements for all the programmers to learn.

Hello,
Itis an extensive usable tool for industrial expansion measurement. This tool is not just used by the computer science or Information Technology expert, but with the advancement of Technology, this tool is now useful for Mechanical engineers as well. Hence, its utility is increasing day by day. MATLAB (MATrix LABoratory) is a tool for numerical computation and visualization. The basic data element is a matrix, so if you need a program that manipulates array-based data, it is generally fast to write and run in MATLAB, unless you have very large arrays or lots of computations, in which case you are better off using C. You need to be graduate in the field of Computer Science or Information Technology stream, in order to learn the functions and operations of MATLAB. But apart from that, PGDS -Post graduate Diploma in Statics is most important Professional degree that are demanded by the companies for MATLAB coding expert job. Salary prospective is dynamic. It depends upon your qualification, company and etc. On the average, the average salary of MATLAB expert is between Rs. 4 lacs pa to Rs. 5 lacs pa.

Hi,
Glad to see that you are opting for the right technology at the right time. NoSQL is in high demand because of its structure and design and it goes perfectly with Big Data to quick integration with available API.
Mongo DB, AWS Dynamo DB and Google Cloud, Azure these are some well-known NoSQL services which will rule in the DB world without any doubts. I hope you are getting the scope and career progress in NoSQL. I would further advise getting familiar with basic RDBMS functions and designs which would help you to have a quick understanding of the differences between SQL and NoSQL. You may go through the below link to know more insights of mongodb.
http://e-gain.co.in/mongodb.php

Perl is a programming language developed by Larry Wall, specially designed for text processing. It stands for Practical Extraction and Report Language. It runs on a variety of platforms, such as Windows, Mac OS, and the various versions of UNIX. This tutorial provides a complete understanding of Perl. Prior knowledge of C/C++ or UNIX shell programming is preferred for Perl programming. Courses for Perl developers may include computer systems, algorithms, data structures, machine architecture, software engineering, linear algebra, and discrete math. Because Perl is a dynamic programming language that differs fundamentally from languages such as C/C++ and Java. There are many online courses available for Perl on Udemy, Lynda, Coursera etc. Check the below link for institutes offering Perl courses - https://www.shiksha.com/it-software/colleges/perl-colleges-india