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ke muka langsung instanly glow gitu.. namanya HERBIGLOW!

mah dia tuh bukan cuma per skincare an tapi ada juga soap & lotion nya.

seneng banget sih karena meski bumil & busui kan kita juga harus kaga kulit kita dengan baik 😍

PARALLEL COMPUTATION

Nama : Tera Nurul Harfiah

Kelas : 4ia10

NPM : 56410863

PARALLEL COMPUTATION

a.     Parallelism Concept

NCAR's production supercomputers are clusters of symmetric multiprocessor (SMP) nodes. While these systems can run single processes on a single processor (CPU), high-performance computing requires having many CPUs work in parallel so compute-intensive programs can run to completion in a reasonable amount of wall-clock time. Clusters such as Geyser and Caldera include both CPUs and GPUs.

Unless you have hands-on experience with multiprocessor cluster systems, you may need to learn some new techniques before you can run parallel programs efficiently and economically. This introduction to parallel computing concepts will help prepare you to run your programs successfully on our systems. You may also want to review the reference materials identified at the end of this overview.

http://www2.cisl.ucar.edu/docs/parallel_concepts

b.     Distributed Processing

The first term used to describe the distribution of multiple computers throughout an organization in contrast to a centralized system. It started with the first minicomputers. Today, distributed processing is called "distributed computing." See also client/server.

http://www.yourdictionary.com/distributed-processing

c.      Architectural Parallel Computer

von Neumann Architecture

• Named after the Hungarian mathematician John von Neumann who first authored the general requirements for an electronic computer in his 1945 papers.
• Since then, virtually all computers have followed this basic design, differing from earlier computers which were programmed through "hard wiring".

Comprised of four main components: Memory Control Unit Arithmetic Logic Unit Input/Output Read/write, random access memory is used to store both program instructions and data Program instructions are coded data which tell the computer to do something Data is simply information to be used by the program Control unit fetches instructions/data from memory, decodes the instructions and then sequentially coordinates operations to accomplish the programmed task. Aritmetic Unit performs basic arithmetic operations Input/Output is the interface to the human operator

• So what? Who cares?
• Well, parallel computers still follow this basic design, just multiplied in units. The basic, fundamental architecture remains the same.

https://computing.llnl.gov/tutorials/parallel_comp/#Neumann

d.     Pengantar Thread Programming

In computer science, a thread of execution is the smallest sequence of programmed instructions that can be managed independently by an operating system scheduler. The scheduler itself is a light-weight process. The implementation of threads and processes differs from one operating system to another, but in most cases, a thread is contained inside a process. Multiple threads can exist within the same process and share resources such as memory, while different processes do not share these resources. In particular, the threads of a process share the latter's instructions (its code) and its context (the values that its variables reference at any given moment).

On a single processor, multithreading is generally implemented by time-division multiplexing (as in multitasking): the processor switches between different threads. This context switching generally happens frequently enough that the user perceives the threads or tasks as running at the same time. On a multiprocessor or multi-core system, threads can be truly concurrent, with every processor or core executing a separate thread simultaneously.

Many modern operating systems directly support both time-sliced and multiprocessor threading with a process scheduler. The kernel of an operating system allows programmers to manipulate threads via the system call interface. Some implementations are called a kernel thread, whereas a lightweight process (LWP) is a specific type of kernel thread that shares the same state and information.

Programs can have user-space threads when threading with timers, signals, or other methods to interrupt their own execution, performing a sort of ad hoc time-slicing.

http://en.wikipedia.org/wiki/Thread_%28computing%29

f. Pengantar Pemrograman CUDA GPU

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs) that they produce. CUDA gives program developers direct access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs.

Using CUDA, the GPUs can be used for general purpose processing (i.e., not exclusively graphics); this approach is known as GPGPU. Unlike CPUs, however, GPUs have a parallel throughput architecture that emphasizes executing many concurrent threads slowly, rather than executing a single thread very quickly.

The CUDA platform is accessible to software developers through CUDA-accelerated libraries, compiler directives (such as OpenACC), and extensions to industry-standard programming languages, including C, C++ and Fortran. C/C++ programmers use 'CUDA C/C++', compiled with "nvcc", NVIDIA's LLVM-based C/C++ compiler. and Fortran programmers can use 'CUDA Fortran', compiled with the PGI CUDA Fortran compiler from The Portland Group.

In addition to libraries, compiler directives, CUDA C/C++ and CUDA Fortran, the CUDA platform supports other computational interfaces, including the Khronos Group's OpenCL, Microsoft's DirectCompute, and C++ AMP. Third party wrappers are also available for Python, Perl, Fortran, Java, Ruby, Lua, Haskell, MATLAB, IDL, and native support in Mathematica.

In the computer game industry, GPUs are used not only for graphics rendering but also in game physics calculations (physical effects like debris, smoke, fire, fluids); examples include PhysX and Bullet. CUDA has also been used to accelerate non-graphical applications in computational biology, cryptography and other fields by an order of magnitude or more.

CUDA provides both a low level API and a higher level API. The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux. Mac OS X support was later added in version 2.0, which supersedes the beta released February 14, 2008. CUDA works with all Nvidia GPUs from the G8x series onwards, including GeForce, Quadro and the Tesla line. CUDA is compatible with most standard operating systems. Nvidia states that programs developed for the G8x series will also work without modification on all future Nvidia video cards, due to binary compatibility.

http://en.wikipedia.org/wiki/CUDA

QUANTUM COMPUTATION

Nama : Tera Nurul Harfiah

Kelas : 4ia10

NPM : 56410863

QUANTUM COMPUTATION

A quantum computer (also known as a quantum supercomputer) is a computation device that makes direct use of quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data. Quantum computers are different from digital computers based on transistors. Whereas digital computers require data to be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1), quantum computation uses qubits (quantum bits), which can be in superpositions of states. A theoretical model is the quantum Turing machine, also known as the universal quantum computer. Quantum computers share theoretical similarities with non-deterministic and probabilistic computers; one example is the ability to be in more than one state simultaneously. The field of quantum computing was first introduced by Yuri Manin in 1980 and Richard Feynman in 1982. A quantum computer with spins as quantum bits was also formulated for use as a quantum space–time in 1969.

As of 2014 quantum computing is still in its infancy but experiments have been carried out in which quantum computational operations were executed on a very small number of qubits. Both practical and theoretical research continues, and many national governments and military funding agencies support quantum computing research to develop quantum computers for both civilian and national security purposes, such as cryptanalysis.^a

Large-scale quantum computers will be able to solve certain problems much more quickly than any classical computer using the best currently known algorithms, like integer factorization using Shor's algorithm or the simulation of quantum many-body systems. There exist quantum algorithms, such as Simon's algorithm, which run faster than any possible probabilistic classical algorithm.  Given sufficient computational resources, however, a classical computer could be made to simulate any quantum algorithm; quantum computation does not violate the Church–Turing thesis.

QUANTUM ENTANGLEMENT

Quantum entanglement is a physical phenomenon that occurs when pairs or groups of particles are generated or interact in ways such that the quantum state of each particle cannot be described independently – instead, a quantum state may be given for the system as a whole.

Measurements of physical properties such as position, momentum, spin, polarization, etc. performed on entangled particles are found to be appropriately correlated. For example, if a pair of particles is generated in such a way that their total spin is known to be zero, and one particle is found to have clockwise spin on a certain axis, then the spin of the other particle, measured on the same axis, will be found to be counterclockwise. Because of the nature of quantum measurement, however, this behavior gives rise to effects that can appear paradoxical: any measurement of a property of a particle can be seen as acting on that particle (e.g. by collapsing a number of superimposed states); and in the case of entangled particles, such action must be on the entangled system as a whole. It thus appears that one particle of an entangled pair "knows" what measurement has been performed on the other, and with what outcome, even though there is no known means for such information to be communicated between the particles, which at the time of measurement may be separated by arbitrarily large distances.

Such phenomena were the subject of a 1935 paper by Albert Einstein, Boris Podolsky and Nathan Rosen, describing what came to be known as the EPR paradox, and several papers by Erwin Schrödinger shortly thereafter. Einstein and others considered such behavior to be impossible, as it violated the local realist view of causality (Einstein referred to it as "spooky action at a distance"),and argued that the accepted formulation of quantum mechanics must therefore be incomplete. Later, however, the counterintuitive predictions of quantum mechanics were verified experimentally. Experiments have been performed involving measuring the polarization or spin of entangled particles in different directions, which – by producing violations of Bell's inequality – demonstrate statistically that the local realist view cannot be correct. This has been shown to occur even when the measurements are performed more quickly than light could travel between the sites of measurement: there is no lightspeed or slower influence that can pass between the entangled particles. Recent experiments have measured entangled particles within less than one part in 10,000 of the light travel time between them. According to the formalism of quantum theory, the effect of measurement happens instantly. It is not possible, however, to use this effect to transmit classical information at faster-than-light speeds(see Faster-than-light → Quantum mechanics).

Quantum entanglement is an area of extremely active research by the physics community, and its effects have been demonstrated experimentally with photons, electrons, molecules the size of buckyballs, and even small diamonds. Research is also focused on the utilization of entanglement effects in communication and computation.

QUBIT OPERATION

While a classical three-bit state and a quantum three-qubit state are both eight-dimensional vectors, they are manipulated quite differently for classical or quantum computation. For computing in either case, the system must be initialized, for example into the all-zeros string, , corresponding to the vector (1,0,0,0,0,0,0,0). In classical randomized computation, the system evolves according to the application of stochastic matrices, which preserve that the probabilities add up to one (i.e., preserve the L1 norm). In quantum computation, on the other hand, allowed operations are unitary matrices, which are effectively rotations (they preserve that the sum of the squares add up to one, the Euclidean or L2 norm). (Exactly what unitaries can be applied depend on the physics of the quantum device.) Consequently, since rotations can be undone by rotating backward, quantum computations are reversible. (Technically, quantum operations can be probabilistic combinations of unitaries, so quantum computation really does generalize classical computation. See quantum circuit for a more precise formulation.)

Finally, upon termination of the algorithm, the result needs to be read off. In the case of a classical computer, we sample from the probability distribution on the three-bit register to obtain one definite three-bit string, say 000. Quantum mechanically, we measure the three-qubit state, which is equivalent to collapsing the quantum state down to a classical distribution (with the coefficients in the classical state being the squared magnitudes of the coefficients for the quantum state, as described above), followed by sampling from that distribution. Note that this destroys the original quantum state. Many algorithms will only give the correct answer with a certain probability. However, by repeatedly initializing, running and measuring the quantum computer, the probability of getting the correct answer can be increased.

For more details on the sequences of operations used for various quantum algorithms, see universal quantum computer, Shor's algorithm, Grover's algorithm, Deutsch-Jozsa algorithm, amplitude amplification, quantum Fourier transform, quantum gate, quantum adiabatic algorithm and quantum error correction.

QUANTUM GATES

In quantum computing and specifically the quantum circuit model of computation, a quantum gate (or quantum logic gate) is a basic quantum circuit operating on a small number of qubits. They are the building blocks of quantum circuits, like classical logic gates are for conventional digital circuits.

Unlike many classical logic gates, quantum logic gates are reversible. However, classical computing can be performed using only reversible gates. For example, the reversible Toffoli gate can implement all Boolean functions. This gate has a direct quantum equivalent, showing that quantum circuits can perform all operations performed by classical circuits.

Quantum logic gates are represented by unitary matrices. The most common quantum gates operate on spaces of one or two qubits, just like the common classical logic gates operate on one or two bits. This means that as matrices, quantum gates can be described by 2 × 2 or 4 × 4 unitary matrices.

SHOR’S ALGORYTHM

Shor's algorithm, named after mathematician Peter Shor, is a quantum algorithm (an algorithm that runs on a quantum computer) for integer factorization formulated in 1994. Informally it solves the following problem: Given an integer N, find its prime factors.

On a quantum computer, to factor an integer N, Shor's algorithm runs in polynomial time (the time taken is polynomial in log N, which is the size of the input). Specifically it takes time O((log N)³), demonstrating that the integer factorization problem can be efficiently solved on a quantum computer and is thus in the complexity class BQP. This is substantially faster than the most efficient known classical factoring algorithm, the general number field sieve, which works in sub-exponential time — about O(e^{1.9 (log N)1/3 (log log N)2/3}). The efficiency of Shor's algorithm is due to the efficiency of the quantum Fourier transform, and modular exponentiation by repeated squarings.

If a quantum computer with a sufficient number of qubits could operate without succumbing to noise and other quantum decoherence phenomena, Shor's algorithm could be used to break public-key cryptography schemes such as the widely used RSA scheme. RSA is based on the assumption that factoring large numbers is computationally infeasible. So far as is known, this assumption is valid for classical (non-quantum) computers; no classical algorithm is known that can factor in polynomial time. However, Shor's algorithm shows that factoring is efficient on an ideal quantum computer, so it may be feasible to defeat RSA by constructing a large quantum computer. It was also a powerful motivator for the design and construction of quantum computers and for the study of new quantum computer algorithms. It has also facilitated research on new cryptosystems that are secure from quantum computers, collectively called post-quantum cryptography.

In 2001, Shor's algorithm was demonstrated by a group at IBM, who factored 15 into 3 × 5, using an NMR implementation of a quantum computer with 7 qubits.  However, some doubts have been raised as to whether IBM's experiment was a true demonstration of quantum computation, since no entanglement was observed. Since IBM's implementation, several other groups have implemented Shor's algorithm using photonic qubits, emphasizing that entanglement was observed. In 2012, the factorization of 15 was repeated. Also in 2012, the factorization of 21 was achieved, setting the record for the largest number factored with a quantum computer. In April 2012, the factorization of 143 was achieved, although this used adiabatic quantum computation rather than Shor's algorithm. 

 www.wikipedia.com

CLOUD COMPUTING

Nama : Tera Nurul Harfiah

Kelas  : 4ia10

NPM  : 56410863

Tugas 2 - CLOUD COMPUTING

1.      Cloud computing in general can be portrayed as a synonym for distributed computing over a network, with the ability to run a program or application on many connected computers at the same time. It specifically refers to a computing hardware machine or group of computing hardware machines commonly referred as a server connected through acommunication network such as the Internet, an intranet, a local area network (LAN) or wide area network (WAN) and individual users or user who have permission to access the server can use the server's processing power for their individual computing needs like to run an application, store data or any other computing need. Therefore, instead of using a personal computer every-time to run the application, the individual can now run the application from anywhere in the world, as the server provides the processing power to the application and the server is also connected to a network via internet or other connection platforms to be accessed from anywhere [30]. All this has become possible due to increasing computer processing power available to humankind with decrease in cost as stated in Moore's law.

In common usage, the term "the cloud" is essentially a metaphor for the Internet. Marketers have further popularized the phrase "in the cloud" to refer to software, platforms and infrastructure that are sold "as a service", i.e. remotely through the Internet. Typically, the seller has actual energy-consuming servers which host products and services from a remote location, so end-users don't have to; they can simply log on to the network without installing anything. The major models of cloud computing service are known as software as a service, platform as a service, and infrastructure as a service.These cloud services may be offered in a public, private or hybrid network. Google, Amazon, IBM, Oracle Cloud, Rackspace, Salesforce, Zoho and Microsoft Azure are some well-known cloud vendors.

Network-based services, which appear to be provided by real server hardware and are in fact served up by virtual hardware simulated by software running on one or more real machines, are often called cloud computing. Such virtual servers do not physically exist and can therefore be moved around and scaled up or down on the fly without affecting the end user, somewhat like a cloud becoming larger or smaller without being a physical object.

2.      Advantages of Cloud Computing is relies on sharing of resources to achieve coherence and economies of scale, similar to a utility (like the electricity grid) over a network. At the foundation of cloud computing is the broader concept of converged infrastructure and shared services.

The cloud also focuses on maximizing the effectiveness of the shared resources. Cloud resources are usually not only shared by multiple users but are also dynamically reallocated per demand. This can work for allocating resources to users. For example, a cloud computer facility that serves European users during European business hours with a specific application (e.g., email) may reallocate the same resources to serve North American users during North America's business hours with a different application (e.g., a web server). This approach should maximize the use of computing power thus reducing environmental damage as well since less power, air conditioning, rackspace, etc. are required for a variety of functions. With cloud computing, multiple users can access a single server to retrieve and update their data without purchasing licenses for different applications.

The term "moving to cloud" also refers to an organization moving away from a traditional CAPEX model (buy the dedicated hardware and depreciate it over a period of time) to theOPEX model (use a shared cloud infrastructure and pay as one uses it).

Proponents claim that cloud computing allows companies to avoid upfront infrastructure costs, and focus on projects that differentiate their businesses instead of infrastructure. Proponents also claim that cloud computing allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and enables IT to more rapidly adjust resources to meet fluctuating and unpredictable business demand. Cloud providers typically use a "pay as you go" model. This can lead to unexpectedly high charges if administrators do not adapt to the cloud pricing model.

3. 4 Basic Principle of Cloud Computing

Cloud computing is one of the best ways to reduce your IT cost and complexity all while helping to optimize your workload and provide the highest possible availability to your user base. Cloud computing utilizes a dynamic infrastructure that is specifically designed to provide more services and capacity while using fewer server resources. One of the best ways to implement a cloud computing solution is with IBM Power Systems.

In fact, IBM Power Systems is in an ideal solution for cloud environments.  There are a number of ways in which IBM Power Systems can be the foundation for your cloud, and a number of options to get you there.

a.      The Right Kind of Workload Optimization 

At the core of cloud computing is this idea of optimizing the workload. This allows you to make the most of your IT resources while increasing your overall flexibility. Power Systems use technology like IBM’s New Intelligent Threads to switch between processor threading dynamically. The Power Systems TurboCore mode lets you provide the most performance per core for things like database or transaction workloads. Active Memory expansion lets you expand your physical memory logically by as much as 100 percent for memory-intensive workloads like SAP.

b.      Limitless Virtualization

With PowerVM, the virtualization component to IBM Power Systems, you can virtualize not just processor resources, but memory and I/O resources as well. You can use PowerVM to adjust capacity in a dynamic fashion, to move workloads between servers, and to maximize availability. This kind of virtualization even allows you to prevent planned downtime.

c.       Automated Management

Being able to provision resources within the cloud is key to maximizing utilization and efficiency. It also helps to reduce your TCO and management costs. Utilizing IBM Systems Director Enterprise for Power Systems, you have a way to manage physical as well as virtual servers in an automated fashion. These tools are cross-platform, too. This means that, no matter what your environment, the Power Systems cloud can provision virtual machine images and effectively allocate resources, all while providing you with an accurate picture of how your systems are operating. 

d.      Solutions of All Kinds

No matter the shape, size or composition of your cloud, IBM Power Systems has a possible solution. Here are a few of the specific offerings:

·         IBM CloudBurst. CloudBurst lets the data center quickly create and implement a private cloud environment. It’s a cloud computing quickstart aimed at a defined portion of the data center.

·         IBM WebSphere CloudBurst Appliance. This offering lets you deploy and manage your SOA foundation in a cloud computing environment, and easily deploys WebSphere virtual images to your Power Systems partitions.

·         IBM Smart Business Development and Test Cloud. This solution lets you create a private cloud environment for the purposes of development and testing, reducing your operating costs and your test cycle times.

4. Characteristics of Cloud Computing

Cloud computing exhibits the following key characteristics:

·         Agility improves with users' ability to re-provision technological infrastructure resources.

·         Application programming interface (API) accessibility to software that enables machines to interact with cloud software in the same way that a traditional user interface (e.g., a computer desktop) facilitates interaction between humans and computers. Cloud computing systems typically use Representational State Transfer (REST)-based APIs.

·         Cost: cloud providers claim that computing costs reduce. A public-cloud delivery model converts capital expenditure to operational expenditure. This purportedly lowersbarriers to entry, as infrastructure is typically provided by a third party and does not need to be purchased for one-time or infrequent intensive computing tasks. Pricing on a utility computing basis is fine-grained, with usage-based options and fewer IT skills are required for implementation (in-house). The e-FISCAL project's state-of-the-art repository contains several articles looking into cost aspects in more detail, most of them concluding that costs savings depend on the type of activities supported and the type of infrastructure available in-house.

·         Device and location independence enable users to access systems using a web browser regardless of their location or what device they use (e.g., PC, mobile phone). As infrastructure is off-site (typically provided by a third-party) and accessed via the Internet, users can connect from anywhere

·         Virtualization technology allows sharing of servers and storage devices and increased utilization. Applications can be easily migrated from one physical server to another.

·         Multitenancy enables sharing of resources and costs across a large pool of users thus allowing for:

§  centralization of infrastructure in locations with lower costs (such as real estate, electricity, etc.)

§  peak-load capacity increases (users need not engineer for highest possible load-levels)

§  utilisation and efficiency improvements for systems that are often only 10–20% utilised.

·         Reliability improves with the use of multiple redundant sites, which makes well-designed cloud computing suitable for business continuity and disaster recovery.

·         Scalability and elasticity via dynamic ("on-demand") provisioning of resources on a fine-grained, self-service basis in near real-time (Note, the VM startup time varies by VM type, location, os and cloud providers), without users having to engineer for peak loads.

·         Performance is monitored, and consistent and loosely coupled architectures are constructed using web services as the system interface.

·         Security can improve due to centralization of data, increased security-focused resources, etc., but concerns can persist about loss of control over certain sensitive data, and the lack of security for stored kernels.^[51] Security is often as good as or better than other traditional systems, in part because providers are able to devote resources to solving security issues that many customers cannot afford to tackle. However, the complexity of security is greatly increased when data is distributed over a wider area or over a greater number of devices, as well as in multi-tenant systems shared by unrelated users. In addition, user access to security audit logs may be difficult or impossible. Private cloud installations are in part motivated by users' desire to retain control over the infrastructure and avoid losing control of information security.

·         Maintenance of cloud computing applications is easier, because they do not need to be installed on each user's computer and can be accessed from different places.

The National Institute of Standards and Technology's definition of cloud computing identifies "five essential characteristics":

On-demand self-service. A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider.

Broad network access. Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations).

Resource pooling. The provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. 

Rapid elasticity. Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear unlimited and can be appropriated in any quantity at any time.

Measured service. Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. National Institute of Standards and Technology.

5. Cloud Computing Security

Cloud security is an evolving sub-domain of computer security, network security, and, more broadly, information security. It refers to a broad set of policies, technologies, and controls deployed to protect data, applications, and the associated infrastructure of cloud computing. Cloud security is not to be confused with security software offerings that are cloud-based such as security as a service.

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 can differ widely from those of traditional architectures. An alternative perspective on the topic of cloud security is that this is but another, although quite broad, case of "applied security" and that similar security principles that apply in shared multi-user mainframe security models apply with cloud security.

The relative security of cloud computing services is a contentious issue that may be delaying its adoption.^] Physical control of the Private Cloud equipment is more secure than having the equipment off site and under someone else's control. Physical control and the ability to visually inspect data links and access ports is required in order to ensure data links are not compromised. 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 to cloud computing service providers to prioritize 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. Solutions to various cloud security issues vary, from cryptography, particularly public key infrastructure (PKI), to use of multiple cloud providers, standardisation of APIs, and improving virtual machine support and legal support.

Cloud computing offers many benefits, but is vulnerable to threats. As cloud computing uses increase, it is likely that more criminals find new ways to exploit system vulnerabilities. Many underlying challenges and risks in cloud computing increase the threat of data compromise. To mitigate the threat, cloud computing stakeholders should invest heavily in risk assessment to ensure that the system encrypts to protect data, establishes trusted foundation to secure the platform and infrastructure, and builds higher assurance into auditing to strengthen compliance. Security concerns must be addressed to maintain trust in cloud computing technology.

Data breach is a big concern in cloud computing. A compromised server could significantly harm the users as well as cloud providers. A variety of information could be stolen. These include credit card and social security numbers, addresses, and personal messages. The U.S. now requires cloud providers to notify customers of breaches. Once notified, customers now have to worry about identify theft and fraud. While providers, have to deal with federal investigations, lawsuits, and bad reputation. Customer lawsuits and settlements have resulted in over $1 billion in losses to cloud providers.

6. Similar Systems and Concepts

Cloud Computing is the result of evolution and adoption of existing technologies and paradigms. The goal of cloud computing is to allow users to take benefit from all of these technologies, without the need for deep knowledge about or expertise with each one of them. The cloud aims to cut costs, and help the users focus on their core business instead of being impeded by IT obstacles.

The main enabling technology for cloud computing is virtualization. Virtualization generalizes the physical infrastructure, which is the most rigid component, and makes it available as a soft component that is easy to use and manage. By doing so, virtualization provides the agility required to speed up IT operations, and reduces cost by increasing infrastructure utilization. On the other hand, autonomic computing automates the process through which the user can provision resources on-demand. By minimizing user involvement, automation speeds up the process and reduces the possibility of human errors.

Users face difficult business problems every day. Cloud computing adopts concepts from Service-oriented Architecture (SOA) that can help the user break these problems intoservices that can be integrated to provide a solution. Cloud computing provides all of its resources as services, and makes use of the well-established standards and best practices gained in the domain of SOA to allow global and easy access to cloud services in a standardized way.

Cloud computing also leverages concepts from utility computing in order to provide metrics for the services used. Such metrics are at the core of the public cloud pay-per-use models. In addition, measured services are an essential part of the feedback loop in autonomic computing, allowing services to scale on-demand and to perform automatic failure recovery.

Cloud computing is a kind of grid computing; it has evolved by addressing the QoS (quality of service) and reliability problems. Cloud computing provides the tools and technologies to build data/compute intensive parallel applications with much more affordable prices compared to traditional parallel computing techniques.

Cloud computing shares characteristics with:

·         Client–server model — Client–server computing refers broadly to any distributed application that distinguishes between service providers (servers) and service requestors (clients).

·          Grid computing — "A form of distributed and parallel computing, whereby a 'super and virtual computer' is composed of a cluster of networked, loosely coupled computers acting in concert to perform very large tasks."

·         Mainframe computer — Powerful computers used mainly by large organizations for critical applications, typically bulk data processing such as: census; industry and consumer statistics; police and secret intelligence services; enterprise resource planning; and financial transaction processing.

·         Utility computing — The "packaging of computing resources, such as computation and storage, as a metered service similar to a traditional public utility, such as electricity."

·         Peer-to-peer — A distributed architecture without the need for central coordination. Participants are both suppliers and consumers of resources (in contrast to the traditional client–server model).

·         Cloud gaming — Also known as on-demand gaming, is a way of delivering games to computers. Gaming data is stored in the provider's server, so that gaming is independent of client computers used to play the game. One such current example, would be a service by OnLive which allows users a certain space to save game data, and load games within the OnLive server.