An Introduction to Cloud Computing .

What is Cloud Computing ?

Cloud Computing is an umbrella term used to refer to Internet based development and services. The cloud is a metaphor for the Internet. A number of characteristics define cloud data, applications services and infrastructure:

• Remotely hosted: Services or data are hosted on someone else’s infrastructure.
• Ubiquitous: Services or data are available from anywhere.
• Commodified: The result is a utility computing model similar to traditional that of traditional utilities, like gas and electricity. You pay for what you would like.

Software as a Service ( SaaS )

SaaS is a model of software deployment where an application is hosted as a service provided to customers across the Internet. SaaS is generally used to refer to business software rather than consumer software, which falls under Web 2.0. By removing the need to install and run an application on a user’s own computer it is seen as a way for businesses to get the same benefits as commercial software with smaller cost outlay.SaaSalso alleviates the burden of software maintenance and support but users relinquish control over software versions and requirements. They other terms that are used in this sphere include Platform as a Service (PaaS) and Infrastructure as a Service (IaaS).

Cloud Storage

Several large Web companies (such as Amazon and Google) are now exploiting the fact that they have data storage capacity which can be hired out to others. This approach, known as ‘cloud storage’ allows data stored remotely to be temporarily cached on desktop computers, mobile phones or other Internet-linked devices. Amazon’s Elastic Compute Cloud (EC2) and Simple Storage Solution (S3) are well known examples.

Data Cloud

Cloud Services can also be used to hold structured data. There has been some discussion of this being a potentially useful notion possibly aligned with the Semantic Web, though concerns, such as this resulting in data becoming undifferentiated, have been raised.

Opportunities and Challenges

The use of the cloud provides a number of opportunities:

• It enables services to be used without any understanding of their infrastructure.
• Cloud Computing works using economies of scale. It lowers the outlay expense for start up companies, as they would no longer need to buy their own software or servers. Cost would be by on-demand pricing. Vendors and Service providers claim costs by establishing an ongoing revenue stream.
• Data and services are stored remotely but accessible from ‘anywhere’.

In parallel there has been backlash against Cloud Computing:

• Use of Cloud Computing means dependence on others and that could possibly limit flexibility and innovation. The ‘others’ are likely become the bigger Internet companies like Google and IBM who may monopolise the market. Some argue that this use of supercomputers is a return to the time of mainframe computing that the PC was a reaction against.
• Security could prove to be a big issue. It is still unclear how safe outsourced data is and when using these services ownership of data is not always clear.
• There are also issues relating to policy and access. If your data is stored abroad whose FOI policy do you adhere to? What happens if the remote server goes down? How will you then access files? There have been cases of users being locked out of accounts and losing access to data.

Privacy

The cloud model has been criticised by privacy advocates for the greater ease in which the companies hosting the cloud services control, thus, can monitor at will, lawfully or unlawfully, the communication and data stored between the user and the host company. Instances such as the secret NSA program, working with AT&T, and Verizon, which recorded over 10 million phone calls between American citizens, causes uncertainty among privacy advocates, and the greater powers it gives to telecommunication companies to monitor user activity. While there have been efforts (such as US-EU Safe Harbor) to “harmonise” the legal environment, providers such as Amazon still cater to major markets (typically the United States and the European Union) by deploying local infrastructure and allowing customers to select “availability zones.”

Security

As Cloud Computing is achieving increased popularity, concerns are being voiced about the security issues introduced through adoption of this new model. The effectiveness and efficiency of traditional protection mechanisms are being reconsidered as the characteristics of this innovative deployment model differ widely from those of traditional architectures.

The relative security of Cloud Computing services is a contentious issue that may be delaying its adoption. Issues barring the adoption of Cloud Computing are due in large part to the private and public sectors unease surrounding the external management of security-based services. It is the very nature of cloud computing-based services, private or public, that promote external management of provided services. This delivers great incentive among Cloud Computing service providers in producing a priority in building and maintaining strong management of secure services. Security issues have been categorised into sensitive data access, data segregation, privacy, bug exploitation, recovery, accountability, malicious insiders, management console security, account control, and multi-tenancy issues. Solution to various cloud security issues vary through cryptography, particularly public key infrastructure (PKI), use of multiple cloud providers, standardisation of APIs, improving virtual machine support and legal support.

The Future

Many of the activities loosely grouped together under Cloud Computing have already been happening and centralised computing activity is not a new phenomena: Grid Computing was the last research-led centralised approach. However there are concerns that the mainstream adoption of Cloud Computing could cause many problems for users. Whether these worries are grounded or not has yet to be seen.

A Beginner’s Guide to Big O Notation

Big O notation is used in Computer Science to describe the performance or complexity of an algorithm. Big O specifically describes the worst-case scenario, and can be used to describe the execution time required or the space used (e.g. in memory or on disk) by an algorithm.

Anyone who’s read Programming Pearls or any other Computer Science books and doesn’t have a grounding in Mathematics will have hit a wall when they reached chapters that mention O(N log N) or other seemingly crazy syntax. Hopefully this article will help you gain an understanding of the basics of Big O and Logarithms.

As a programmer first and a mathematician second (or maybe third or fourth) I found the best way to understand Big O thoroughly was to produce some examples in code. So, below are some common orders of growth along with descriptions and examples where possible.

O(1)

O(1) describes an algorithm that will always execute in the same time (or space) regardless of the size of the input data set.

bool IsFirstElementNull(String[] strings)
{
	if(strings[0] == null)
	{
		return true;
	}
	return false;
}

O(N)

O(N) describes an algorithm whose performance will grow linearly and in direct proportion to the size of the input data set. The example below also demonstrates how Big O favours the worst-case performance scenario; a matching string could be found during any iteration of the for loop and the function would return early, but Big O notation will always assume the upper limit where the algorithm will perform the maximum number of iterations.

bool ContainsValue(String[] strings, String value)
{
	for(int i = 0; i < strings.Length; i++)
	{
		if(strings[i] == value)
		{
			return true;
		}
	}
	return false;
}

O(N²)

O(N²) represents an algorithm whose performance is directly proportional to the square of the size of the input data set. This is common with algorithms that involve nested iterations over the data set. Deeper nested iterations will result in O(N³), O(N⁴) etc.

bool ContainsDuplicates(String[] strings)
{
	for(int i = 0; i < strings.Length; i++)
	{
		for(int j = 0; j < strings.Length; j++)
		{
			if(i == j) // Don't compare with self
			{
				continue;
			}

			if(strings[i] == strings[j])
			{
				return true;
			}
		}
	}
	return false;
}

O(2^N)

O(2^N) denotes an algorithm whose growth will double with each additional element in the input data set. The execution time of an O(2^N) function will quickly become very large.

Logarithms

Logarithms are slightly trickier to explain so I’ll use a common example:

Binary search is a technique used to search sorted data sets. It works by selecting the middle element of the data set, essentially the median, and compares it against a target value. If the values match it will return success. If the target value is higher than the value of the probe element it will take the upper half of the data set and perform the same operation against it. Likewise, if the target value is lower than the value of the probe element it will perform the operation against the lower half. It will continue to halve the data set with each iteration until the value has been found or until it can no longer split the data set.

This type of algorithm is described as O(log N). The iterative halving of data sets described in the binary search example produces a growth curve that peaks at the beginning and slowly flattens out as the size of the data sets increase e.g. an input data set containing 10 items takes one second to complete, a data set containing 100 items takes two seconds, and a data set containing 1000 items will take three seconds. Doubling the size of the input data set has little effect on its growth as after a single iteration of the algorithm the data set will be halved and therefore on a par with an input data set half the size. This makes algorithms like binary search extremely efficient when dealing with large data sets.

This article only covers the very basics or Big O and logarithms. For a more in-depth explanation take a look at their respective Wikipedia entries:
 Big O Notation, 
Logarithms.