CLOUD: A Computing Infrastructure on Demand

Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the data centers that provide those services. A cloud computing platform dynamically provisions, configures, reconfigures, and deprovisions servers as needed. Cloud applications are those that are extended to be accessible through the Internet. The datacenter hardware and software is what we will call a Cloud. Cloud computing is changing the way we provision hardware and software for on-demand capacity fulfillment and changing the way we develop web applications and make business decisions. Definition: Cloud computing is a computing paradigm in which tasks are assigned to a combination of connections, software and services accessed over a network. This network of servers and connections is collectively known as "the cloud." Computing at the scale of the cloud allows users to access supercomputer-level power. Users can access resources as they need them.

Cloud computing describes how computer programs are hosted and operated over the Internet. The key feature of cloud computing is that both the software and the information held in it live on centrally located servers rather than on an end-user's computer. How does cloud computing

works? The concept is fairly simple. First, consider the traditional means of running application: and application appears to run on a dumb terminal or, these days your PC; practicality, this is only front-end of the application. Your computer is connected to a server that actually runs the

program or application and returns information to your personal computer. The server constitutes the backend and it can be located in the same building as you are or not. With cloud computing application program runs somewhere within the cloud; ideally the user concern only with applications that are available and need not to be aware of the underlying technology or the physical location of the Application's computer. User desktop is connected via internet to a server farm, a collection of remote servers that runs many, many applications at once. Which server or servers an application runs on is determined by the 􀁪pplication program already running on the machines; there IS an attempt to balance the load so that all of the programs run optimally.

A carbon 2.0 framework based on cloud computing

One remarkable news in 2009 was the United Nations Climate Change Conference (COP15) held in Copenhagen. Although many were disappointed with COP15's main output, the summit did introduce a new kind of dynamics in global climate policy, in addition to the Copenhagen accord. Specifically, considering the climate change all over the planet, we must pay close attention to this severe situation and take action as soon as possible. Under this new trend, all organizations, even households and individuals, are expected to set their agendas to manage their carbon

footprint (CFP) below a certain level in the near future. This new practice needs appropriate IT systems to help tackle challenges in measuring, analyzing and managing carbon emissions in a cost-effective manner . Currently, several carbon management systems are being developed to facilitate enterprises to calculate and manage their CFP, such as ESS's GHG/Carbon management SolutionTM , U.S. Army's Enviance system ,IBM/EIM's GreenCertTM, etc. However, their IT architectures are less efficient compared to the latest IT technologies. In addition, a number of CFP tools are being developed to fulfill households and individuals’ carbon

related needs, such as UK Carbon Footprint Ltd's CFP calculator , the Nature Conservancy's CFP calculator , etc. These CFP tools are based on B/S model, and it can only calculate user's CFP over the Internet. Therefore, it is still a challenging issue on how to take advantage of the latest IT technologies to improve carbon systems.We focus on the next-generation carbon systems for both enterprises and households use and present a carbon 2.0 framework based on cloud computing. This new framework is massively scalable and can dynamically configure/deliver

carbon-related services on demand. Moreover, it is a real low-carbon framework itself!

The term "carbon 2.0" was first introduced by Qindong Liu and Jan Stallaert, who attempted to unveil the architecture and functionality of future carbon systems for enterprises. Cloud computing is a new computing paradigm and it can make good use of

economies of scale, and dynamically deliver almost any IT related resources as a service on demand, such as computing power, storage capacity, network service, integrated management, with dynamically configurable interface and the economy model of charging for capacity.

Simulation

Simulation is the imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviours of a selected physical or abstract system.Simulation is used in many contexts, including the modeling of natural systems or human systems in order to gain insight into their functioning.
There are two types of simulation:-
Physical simulation refers to simulation in which physical objects are substituted for the real thing (some circles use the term for computer simulations modelling selected laws of physics, but this article doesn't). These physical objects are often chosen because they are smaller or cheaper than the actual object or system.
Interactive simulation is a special kind of physical simulation, often referred to as a human in the loop simulation, in which physical simulations include human operators, such as in a flight simulator or a driving simulator.
Human in the loop simulations can include a computer simulation as a so-called synthetic environment.

Wireless communication

The term wireless is normally used to refer to any type of electrical or electronic operation which is accomplished without the use of a "hard wired" connection. Wireless communication is the transfer of information over a distance without the use of electrical conductors or "wires ".The distances involved may be short (a few meters as in television remote control) or very long (thousands or even millions of kilometers for radio communications). When the context is clear the term is often simply shortened to "wireless". Wireless communications is generally considered to be a branch of telecommunication .
Wireless operations permits services, such as long range communications, that are impossible or impractical to implement with the use of wires. The term is commonly used in the telecommunications industry to refer to telecommunications systems (e.g., radio transmitters and receivers, remote controls, computer networks, network terminals, etc.) which use some form of energy to transfer information without the use of wires. Information is transferred in this manner over both short and long distances.
The term "wireless" has become a generic and all-encompassing word used to describe communications in which electromagnetic waves or RF (rather than some form of wire) carry a signal over part or the entire communication path.
Wireless networking (i.e. the various flavors of unlicensed 2.4 GHz WiFi devices) is used to meet a variety of needs. Perhaps the most common use is to connect laptop users who travel from location to location. Another common use is for mobile networks that connect via satellite. A wireless transmission method is a logical choice to network a LAN segment that must frequently change locations.

Neural Networks

In general a biological neural network is composed of a group or groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. Connections, called synapses , are usually formed from axons to dendrite , though dendrodendritic microcircuits and other connections are possible. Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling. As such, neural networks are extremely complex. Whilst a detailed description of neural systems is nebulous, progress is being charted towards a better understanding of basic mechanisms.

Simplified view of an artificial neural network

Artificial intelligence and cognitive modelling try to simulate some properties of neural networks. While similar in their techniques, the former has the aim of solving particular tasks, while the latter aims to build mathematical models of biological neural systems.

In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition,image analysis and adaptive control , in order to construct software agents(in computer and video games) or autonomous robots . Most of the currently employed artificial neural networks for artificial intelligence are based on statistical estimation, optimization and control theory.

The cognitive modelling field involves the physical or mathematical modeling of the behaviour of neural systems; ranging from the individual neural level (e.g. modelling the spike response curves of neurons to a stimulus), through the neural cluster level (e.g. modelling the release and effects of dopamine in the basal ganglia) to the complete organism (e.g. behavioural modelling of the organism's response to stimuli).

Telecommunication

Telecommunication is the assisted transmission of signals a distance for the purpose of communication. In earlier times, this may have involved the use of smoke signals, drums, semaphores,flags, or heliograph . In modern times, telecommunication typically involves the use of electronic transmitters such as the telephone, television, radio,computer. Early inventors in the field of telecommunication include antonio meucci ,Alexander Graham Bell,Guglielmo marconi and john logie baird . Telecommunication is an important part of the world economy and the telecommunication industry's revenue has been placed at just under 3 percent of the gross world product .

Artificial Learning

Much effort has been devoted to understanding learning and reasoning in artificial intelligence, giving rise to a wide collection of models. For the most part, these models focus on some observed characteristic of human learning, such as induction or analogy, in an effort to emulate (and possibly exceed) human abilities. We propose seven desirable properties for artificial learning systems: incrementality, non-monotonicity, inconsistency and conflicting defaults handling, abstraction, self-organization, generalization, and computational tractability.We examine each of these properties in turn and show how their (combined) use can improve learning and reasoning, as well as potentially widen the range of applications of artificial learning systems.
Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems.

If features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods provide solid support via empirical studies, theoreical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted.

All papers describe the supporting evidence in ways that can be verified or replicated by other researchers. The papers also detail the learning component clearly and discuss assumptions regarding knowledge representation and the performance task.

In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent discoveries in neurology , a new mathematical theory of information,an understanding of control and stability called cybernetics , and above all, by the invention of the digital computer , a machine based on the abstract essence of mathematical reasoning