what is cloud computing Definition – with real world examples


Definition of Cloud Computing with Real World Example

The curious question is: what is Cloud computing Definition with real world example? It is a disruptive and innovative model for enabling convenient, on-demand, and flexible access to a shared pool of computing resources such as networks, servers, storage, applications, and services which are configurable and can be quickly provisioned and de-provisioned with minimal or no management effort or service provider interaction.

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This model is composed of three service models, four deployment models, and five essential characteristics as per NIST.

On-demand self-service: Use credit card and use resources  such as compute, storage, database from cloud environment without any interaction with service providers or any delay due to workflows and permissions. It takes less than a MINUTE! And believe me, you are not day dreaming…It is a reality.

Go to http://aws.amazon.com/ and verify yourself

  • Real world example: SMS activation:
  • Prerequisite: Enough balance in the Mobile (Map it with Credit Card in Cloud environment )
  • Request Service by calling on *110# (Airtel) (Map it with http://aws.amazon.com in Cloud environment )
  • And you can easily use the services required

Balance/Usage: with *123*3# you know the available amount of SMS (In case of Cloud, All Cloud Service Provider gives Dashboard/ Management Console/ Cost Control Dashboard which gives you  information regarding your resource usage, cost and many more things.

Cloud Computing Billing (Source: Google)
Cloud Computing Billing (Source: Google)

Broad network access: Computing resources such as compute and storage capacity are available over network / on internet or intranet and they can be accessed via various devices such as smartphones, iPads, mobile phones, tablets, laptops, and workstations.

Real world example: Videos on Mobile Web – Applications, wallpapers and ringtones from Internet are used

Smartphone
Smartphone

The more suitable example can be the way we watch videos over internet; similarly we can use various resources on Cloud as well…

Youtube on Smartphone
Youtube on Smartphone

Resource pooling: A resource pool is a set of resource which is homogeneous with respect to some activity, action or context. Cloud Service provider create a pool of all computing resources to serve multiple consumers in a multi-tenant environment, where different physical and virtual resources are dynamically assigned and re-assigned according to demand.

Resource Pooling (Source - VMware)
Resource Pooling (Source – VMware)

Real world example: In software engineering, a connection pool is considered as a cache of database connections so that the connections can be reused in case of future requests to the database are required.

Purpose: To enhance performance of executing database commands

In case of cloud environment, resources are pooled together and its capacities are used in unified manner to enhance the performance and customer satisfaction.

Rapid elasticity: Application can expand on demand, across all its tiers  such as presentation layer, database layer,application layer – MVC).  It also implies that application components can grow independently from each other. So if you require more storage for database, you should be able to grow that tier without affecting the availabilityof that application, reconfiguration, or changing the other tiers.

Real world example: In physics, elasticity or stretchiness is the physical property of a material that returns to its original shape after the stress e.g. external forces (Consider Peak Hours as external forces in cloud environment)

 

Cloud Computing - Elasticity
Cloud Computing – Elasticity

Measured service: Cloud environment automatically control and optimize resource use by leveraging a metering or chargeback capacity. The basic value proposition of cloud computing is its utility based price model where you pay for what you use. Resource usage in cloud environment can be monitored, controlled, and notified via alerts, dashboards. It is a very basic feature which is essential in Cloud environment

Real world example: Consumers pay for electricity as they have used it

 

Cloud Computing - Measured Service
Cloud Computing – Measured Service

It’s not a Technology…It’s an innovation…Idea….

 

 

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VMware: How to get Full Screen in Virtual Machine Console (ESXi)


How to get Full Screen in Virtual Machine Console while working with VMware (ESXi)?
Full Screen in Virtual Machine
Full Screen in Virtual Machine

I want to install Oracle Client 32 BIT on Windows 2008 R2 VM but I can’t see the “Next” button in the installation steps due to small window 😦

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VMware - Virtual Machine Console
Virtual Machine Console

Lets Try Full Screen Mode:

VMware - Virtual Machine Console - Full Screen
Virtual Machine Console – Full Screen

But it doesn’t work as well…

So Now what??

Right Click on Desktop -> Screen Resolution

Windows 2008 - Screen Resolution
Windows 2008 – Screen Resolution

Select Higher Select Resolution -> Apply ->Ok

Windows - Select Resolution
Windows – Select Resolution

Now…

VMware - Virtual Machine Console - Full Screen
Virtual Machine Console – Full Screen

At first sight this look obvious but trust me none wants to waste time on this kind of issue because you tend to use Full Screen Option from View Menu which is not going to serve the purpose 🙂

NOSQL and Cloud Computing


Introduction

Cloud Computing is moving from being “IT buzzword” to reasonable yet reliable way of deploying applications in the Internet. IT managers within companies are considering deploying some applications within cloud. A cloud-related trend that developers have been paying attention is the idea of “NoSQL”, a set of operational-data technologies based on non-relational concepts. “NoSQL” is “a sea change” idea to consider data storage options beyond the traditional SQL-based relational database.

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Accordingly, a new set of open source distributed database is actively propping up to leverage the facilities and services provided through the cloud architecture. Thus, web applications and databases in cloud are undergoing major architectural changes to take advantage of the scalability provided by the cloud. This article is intended to provide insight on the NOSQL in the context of Cloud computing.

Face off ~ SQL, NOSQL & Cloud Computing

A key disadvantage of SQL Databases is the fact that SQL Databases are at a high abstraction level. This is a disadvantage because to do a single Statement, SQL often requires the data to be processed multiple times. This, of course, takes time and performance. For instance, multiple queries on SQL Data occur when there is a ‘Join’ operation. Cloud computing environments need high-performing and highly scalable databases.

NoSQL Databases are built without relations. But is it really that “good” to go for NoSQL Databases? A world without relations, no joins and pure scalability!  NoSQL databases typically emphasize horizontal scalability via partitioning, putting them in a good position to leverage the elastic provisioning capabilities of the cloud.

The general definition of a NOSQL data store is that it manages data that is not strictly tabular and relational, so it does not make sense to use SQL for the creation and retrieval of the data. NOSQL data stores are usually non-relational, distributed, open-source, and horizontally scalable.

If we look at the big Platforms in the Web like Facebook or Twitter, there are some Datasets that do not need any relations. The challenge for NoSQL Databases is to keep the data consistent. Imagine the fact that a user deletes his or her account. If this is hosted on a NoSQL Database, all the tables have to check for any data the user has produced in the past. With NoSQL, this has to be done by code.

A major advantage of NoSQL Databases is the fact that Data replication can be done more easily then it would be with SQL Databases.

As there are no relations, Tables don’t necessary have to be on the same servers. Again, this allows better “scaling” than SQL Databases. Don’t forget: scaling is one of the key aspects in Cloud computing environments.

Another disadvantage of SQL databases is the fact that there is always a schema involved. Over time, requirements will definitely change and the database somehow has to support this new requirements. This can lead to serious problems. “Just imagine” the fact that applications  need two extra fields to store data. Solving this issue with SQL Databases might get very hard. NoSQL databases support a changing environment for data and are a better solution in this case as well.

SQL Databases have the advantage over NoSQL Databases to have better support for “Business Intelligence”.

Cloud Computing Platforms are made for a great number of people and potential customers. This means that there will be millions of queries over various tables, millions or even billions of read and write operations within seconds. SQL Databases are built to serve another market: the “business intelligence” one, where fewer queries are executed.

This implies that the way forward for many developers is a hybrid approach, with large sets of data stored in, ideally, cloud-scale NoSQL storage, and smaller specialized data remaining in relational databases. While this would seem to amplify management overhead, reducing the size and complexity of the relational side can drastically simplify things.

However, it is up to the Use-Case to identify if you want a NoSQL approach or if you better stay with SQL.

“NOSQL” Databases for Cloud

The NoSQL (or “not only SQL”) movement is defined by a simple premise: Use the solution that best suits the problem and objectives.

If the data structure is more appropriately accessed through key-value pairs, then the best solution is likely a dedicated key value pair database.

If the objective is to quickly find connections within data containing objects and relationships, then the best solution is a graph database that can get results without any need for translation (O/R mapping).

Today’s availability of numerous technologies that finally support this simple premise are helping to simplify the application environment and enable solutions that actually exceed the requirements, while also supporting performance and scalability objectives far into the future.  Many cloud web applications have expanded beyond the sweet spot for these relational database technologies. Many applications demand availability, speed, and fault tolerance over consistency.

Although the original emergence of NOSQL data stores was motivated by web-scale data, the movement has grown to encompass a wide variety of data stores that just happen to not use SQL as their processing language. There is no general agreement on the taxonomy of NOSQL data stores, but the categories below capture much of the landscape.

Tabular / Columnar Data Stores

Storing sparse tabular data, these stores look most like traditional tabular databases. Their primary data retrieval paradigm utilizes column filters, generally leveraging hand-coded map-reduce algorithms.

BigTable is a compressed, high performance, and proprietary database system built on Google File System (GFS), Chubby Lock Service, and a few other Google programs;

HBase is an open source; non-relational, distributed database modeled after Google’s BigTable and is written in Java. It runs on top of HDFS, providing a fault-tolerant way of storing large quantities of sparse data.

Hypertable is an open source database inspired by publications on the design of Google’s BigTable. Hypertable runs on top of a distributed file system such as the Apache Hadoop DFS, GlusterFS, or the Kosmos File System (KFS). It is written almost entirely in C++ for performance.

VoltDB is an in-memory database. It is an ACID-compliant RDBMS which uses a shared nothing architecture. VoltDB is based on the academic HStore project. VoltDB is a relational database that supports SQL access from within pre-compiled Java stored procedures.

Google Fusion Tables is a free service for sharing and visualizing data online. It allows you to upload and share data, merge data from multiple tables into interesting derived tables, and see the most up-to-date data from all sources.

Document Stores

These NOSQL data sources store unstructured (i.e., text) or semi-structured (i.e., XML) documents. Their data retrieval paradigm varies highly, but documents can always be retrieved by unique handle. XML data sources leverage XQuery. Text documents are indexed, facilitating keyword search-like retrieval.

Apache CouchDB, commonly referred to as CouchDB, is an open source document-oriented database written in the Erlang programming language. It is designed for local replication and to scale vertically across a wide range of devices.

MongoDB is an open source, scalable, high-performance, schema-free, document-oriented database written in the C++ programming language.

Terrastore is a distributed, scalable and consistent document store supporting single-cluster and multi-cluster deployments. It provides advanced scalability support and elasticity feature without loosening the consistency at data level.

Graph Databases

These NOSQL sources store graph-oriented data with nodes, edges, and properties and are commonly used to store associations in social networks.

Neo4j is an open-source graph database, implemented in Java. It is “embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs.

AllegroGraph is a Graph database. It considers each stored item to have any number of relationships. These relationships can be viewed as links, which together form a network, or graph.

FlockDB is an open source distributed, fault-tolerant graph database for managing data at webscale. It was initially used by Twitter to build its database of users and manage their relationships to one another. It scales horizontally and is designed for on-line, low-latency, high throughput environments such as websites.

VertexDB is a high performance graph database server that supports automatic garbage collection. It uses the HTTP protocol for requests and JSON for its response data format and the API are inspired by the FUSE file system API plus a few extra methods for queries and queues.

Key/Value Stores

These sources store simple key/value pairs like a traditional hash table. Their data retrieval paradigm is simple; given a key, return the value.

Dynamo is a highly available, proprietary key-value structured storage system. It has properties of both databases and distributed hash tables (DHTs). It is not directly exposed as a web service, but is used to power parts of other Amazon Web Services

Memcached is a general-purpose distributed memory caching system. It is often used to speed up dynamic database-driven websites by caching data and objects in RAM to reduce the number of times an external data source must be read.

Cassandra is an open source distributed database management system. It is designed to handle very large amounts of data spread out across many commodity servers while providing a highly available service with no single point of failure. It is a NoSQL solution that was initially developed by Facebook and powers their Inbox Search feature.

Amazon SimpleDB is a distributed database written in Erlang by Amazon.com. It is used as a web service in concert with EC2 and S3 and is part of Amazon Web Services.

Voldemort is a distributed key-value storage system. It is used at LinkedIn for certain high-scalability storage problems where simple functional partitioning is not sufficient.

Kyoto Cabinet is a library of routines for managing a database. The database is a simple data file containing records; each is a pair of a key and a value. There is neither concept of data tables nor data types. Records are organized in hash table or B+ tree.

Scalaris is a scalable, transactional, distributed key-value store. It can be used for building scalable Web 2.0 services.

Riak is a Dynamo-inspired database that is being used in production by companies like Mozilla.

Object and Multi-value Databases

These types of stores preceded the NOSQL movement, but they have found new life as part of the movement. Object databases store objects (as in object-oriented programming). Multi-value databases store tabular data, but individual cells can store multiple values. Examples include Objectivity, GemStone and Unidata. Proprietary query languages are used.

Miscellaneous NOSQL Sources

Several other data stores can be classified as NOSQL stores, but they don’t fit into any of the categories above. Examples include: GT.M, IBM Lotus/Domino, and the ISIS family.

Sources for further Reading

http://news.cnet.com/8301-13846_3-10412528-62.html#ixzz1DGORTRBP   http://cloudcomputing.blogspot.com/2010/03/nosql-is-not-sql-and-thats-problem.html

http://news.cnet.com/8301-13846_3-10412528-62.html http://www.readwriteweb.com/cloud/2010/07/cassandra-predicting-the-futur.php

http://cloudvane.wordpress.com/tag/nosql/

http://www.rackspacecloud.com/blog/2010/02/25/should-you-switch-to-nosql-too/ http://pro.gigaom.com/2010/03/what-cloud-computing-can-learn-from-nosql/ http://www.drdobbs.com/database/224900500

http://cloudcomputing.blogspot.com/2010/04/disruptive-cloud-computing-startups-at.html

http://www.informationweek.com/cloud-computing/blog/archives/2010/04/nosql_needed_fo.html

http://www.elance.com/s/cloudcomputing/

http://www.thesavvyguideto.com/gridblog/2009/11/a-look-at-nosql-and-nosql-patterns/

http://blogs.forrester.com/application_development/2010/02/nosql.html

http://www.yafla.com/dforbes/Getting_Real_about_NoSQL_and_the_SQL_Isnt_Scalable_Lie/

http://arstechnica.com/business/data-centers/2010/02/-since-the-rise-of.ars/2

Life Science Application, FDA and Cloud Computing


Life science application comes under Life sciences industry.

Life sciences consist of all fields of science that involve the scientific study of living organisms such as human beings, plants, and animals. The study of behaviour of organisms is only included in as much as it involves a clearly biological aspect. Biology and medicine remain main parts of the life sciences, having said that technological advances in molecular biology and biotechnology have directed it to a burgeoning of specializations and new interdisciplinary fields.

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R&D process in the life science can be a long and expensive undertaking. The product development process follows basic steps at a very high level as described below:

• Phase 1 – recognition of the particle, initial testing, and toxicology studies

• Phase 2 – more development, formulation, and human testing

• Phase 3 – double blind clinical trials to test efficacy and submission for FDA approval

Life science industry operates under the regulatory guidelines put forward by the Food & Drug Administration (FDA).

Food and Drug Administration is a federal agency in the Department of Health and Human Services. It is established to regulate the release of new foods and health related products.

The IT organizations in life science companies must adhere to the FDA guidelines put forth in the Code for Federal Regulations 21 Part 11 (CFR 21 Part 11). It defines how systems managing electronic records in life science firms must be validated and verified to ensure that the operation of and the information in these systems can be trusted.

Title 21 CFR Part 11 of the Code of Federal Regulations deals with the Food and Drug Administration (FDA) guidelines on electronic records and electronic signatures in the United States. CFR Part 11, as it is called, defines the criteria under which electronic records and signatures are considered to be reliable and equivalent to paper records.

Part 11 requires drug makers, manufacturers, biologics developers, biotech companies, and other FDA-regulated industries to implement controls such as audits, system validations, audit trails, electronic signatures, and documentation for software and systems involved in processing electronic data that are (a) required to be maintained by the FDA predicate rules or (b) used to demonstrate compliance to a predicate rule with some specific exceptions.

The actual Part 11 compliance process for any application includes software, hardware, and operational environment for the system itself. This allows an IT Team to answer the questions.

To prove these things the system validation process has three primary components, the Installation Qualification (IQ), the Operational Qualification (OQ), and Performance Qualification scripts. Organizations manage IT environment separately for the life science applications and with proper controls placed.

CFR does not ask organization on How to do it? but it states What needs to be done.

It all comes to convincing the FDA auditor whether the Cloud environment conforms to the FDA compliance requirements or not.

Cloud computing can improve and speed up process by reducing IT complexity and cost while allowing R&D organizations to focus on the ‘what’ of the R&D process in stead of the ‘how’.

But, how Cloud Computing and FDA can be brought on a same table is the biggest issue because:

Audit / Track of following items are needed.

Ø      Hardware serial number

Ø      System configuration

Ø      Equipment location

Ø      Exact versions off all installed software

FDA compliance in Public Cloud is impossible till now because you must be aware about the detailed information on the hardware and software that your system will be running on and even the exact physical location of the resources as well.

In Private Cloud, owner has control over all resources (Hardware, Software) and thus it is still possible.

In a nutshell, public cloud model just does not fit for the current practices for validation in FDA regulated organizations. However private cloud environment could be leveraged to provide life science companies with a short cut to completing overall system validation Public Cloud’s benefit “Economy of Scale” will be out of reach in this case.

A community cloud may be established where several organizations have similar requirements and seek to share infrastructure so as to realize some of the benefits of cloud computing. With the costs spread over less users than a public cloud (but more than a single tenant) this option is more expensive but may offer a higher level of privacy, security and/or policy (FDA) compliance.

What’s latest?

Amazon EC2 Dedicated Instances

Dedicated Instances are Amazon EC2 instances launched within your Amazon Virtual Private Cloud (Amazon VPC) that run hardware dedicated to a single customer.

NOTE: hardware dedicated to a single customer

Dedicated Instances let you take full advantage of the benefits of Amazon VPC and the AWS cloud – on-demand elastic provisioning, pay only for what you use, and a private, isolated virtual network, all while ensuring that your Amazon EC2 compute instances will be isolated at the hardware level.

You can easily create a VPC that contains dedicated instances only, providing physical isolation for all Amazon EC2 compute instances launched into that VPC, or you can choose to mix both dedicated instances and non-dedicated instances within the same VPC based on application-specific requirements

To get started using Dedicated Instances within an Amazon VPC, perform the following steps:

  • Open and log into the AWS Management Console
  • Create an Amazon VPC if you do not already have one on the Amazon VPC tab
  • Click on Launch Instance from the EC2 Dashboard
  • Select Launch Instances Into Your Virtual Private Cloud
  • Modify the instance tenancy from Default to Dedicated in the Request Instances Wizard
  • Start using your instance with the knowledge it will not share hardware with instances launched by other customers

Dedicated Instances certainly does help in building a case for FDA compliance and step in a RIGHT direction.
Reference:

http://www.hpcinthecloud.com/blogs/Cloud-Infrastructure-and-FDA-Compliance-92894189.html

http://www.hpcinthecloud.com/blogs/The-Possibilities-of-Cloud-in-the-Life-Sciences-Industry-91439669.html

http://www.hpcinthecloud.com/blogs/Negotiating-IT-in-the-FDA-Regulatory-Environment-92093279.html

http://aws.amazon.com/dedicated-instances/

http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm