Cloud Computing & Soft Computing

Cloud Computing and Soft Computing.

Cloud Computing.

Cloud computing refers to the delivery of computing and storage capacity [citation needed] as a service to a heterogeneous community of end-recipients. The name comes from the use of clouds as an abstraction for the complex infrastructure it contains in system diagrams [citation needed]. Cloud computing entrusts services with a user's data, software and computation over a network. It has considerable overlap with software as a service (SaaS).

End users access cloud based applications through a web browser or a light weight desktop or mobile app while the business software and data are stored on servers at a remote location. Proponents 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 computing relies on sharing of resources to achieve coherence and economies of scale similar to a utility (like the electricity grid) over a network (typically the Internet). At the foundation of cloud computing is the broader concept of converged infrastructure and shared services.

Cracteristic.

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 the user interface facilitates interaction between humans and computers. Cloud computing systems typically use REST-based APIs.
• Cost is claimed to be reduced and in a public cloud delivery model capital expenditure is converted to operational expenditure. This is purported to lower barriers 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 are using (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 servers and storage devices to be shared and utilization be increased. 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 is improved if multiple redundant sites are used, 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 near real-time, 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 could 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. 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. However, the complexity of security is greatly increased when data is distributed over a wider area or greater number of devices and in multi-tenant systems that are being 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.

Service Models

Cloud computing providers offer their services according to three fundamental models: Infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) where IaaS is the most basic and each higher model abstracts from the details of the lower models.

Infrastructure as a service (IaaS)

In this most basic cloud service model, cloud providers offer computers – as physical or more often as virtual machines –, raw (block) storage, firewalls , load balancers, and networks. IaaS providers supply these resources on demand from their large pools installed in data centers. Local area networks including IP addresses are part of the offer. For the wide area connectivity, the Internet can be used or - in carrier clouds - dedicated virtual private networks can be configured.

To deploy their applications, cloud users then install operating system images on the machines as well as their application software. In this model, it is the cloud user who is responsible for patching and maintaining the operating systems and application software. Cloud providers typically bill IaaS services on a utility computing basis, that is, cost will reflect the amount of resources allocated and consumed.

Infrastructure-as-a-Service or IaaS Cloud is a platform through which businesses can avail equipment in the form of hardware, servers, storage space etc at pay-per-use service.

Moreover, IaaS is a branch of cloud computing that has gathered attention among the entrepreneurs largely with the prime motive to make their business environments more organized and in sync with the ongoing operational activities of organizations.

When we talk about IaaS functioning, it is not a machine that we are talking about, which does all the work, it is simply a facility given to the business enterprises that offers users the leverage of extra storage space in servers and data centers.

Platform as a service (PaaS)

In the Paas model, cloud providers deliver a computing platform and/or solution stack typically including operating system, programming language execution environment, database, and web server. Application developers can develop and run their software solutions on a cloud platform without the cost and complexity of buying and managing the underlying hardware and software layers. With some PaaS offers, the underlying compute and storage resources scale automatically to match application demand such that cloud user does not have to allocate resources manually.

Software as a service (SaaS)

In this model, cloud providers install and operate application software in the cloud and cloud users access the software from cloud clients. The cloud users do not manage the cloud infrastructure and platform on which the application is running. This eliminates the need to install and run the application on the cloud user's own computers simplifying maintenance and support. What makes a cloud application different from other applications is its elasticity. This can be achieved by cloning tasks onto multiple virtual machines at run-time to meet the changing work demand. Load balancers distribute the work over the set of virtual machines. This process is inconspicuous to the cloud user who sees only a single access point. To accommodate a large number of cloud users, cloud applications can be multitenant, that is, any machine serves more than one cloud user organization. It is common to refer to special types of cloud based application software with a similar naming convention: desktop as a service, business process as a service, Test Environment as a Service, communication as a service.

The pricing model for SaaS applications is typically a monthly or yearly flat fee per user.

Soft Computing.

"Basically, soft computing is not a homogeneous body of concepts and techniques. Rather, it is a partnership of distinct methods that in one way or another conform to its guiding principle. At this juncture, the dominant aim of soft computing is to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness and low solutions cost. The principal constituents of soft computing are fuzzy logic, neurocomputing, and probabilistic reasoning, with the latter subsuming genetic algorithms, belief networks, chaotic systems, and parts of learning theory. In the partnership of fuzzy logic, neurocomputing, and probabilistic reasoning, fuzzy logic is mainly concerned with imprecision and approximate reasoning; neurocomputing with learning and curve-fitting; and probabilistic reasoning with uncertainty and belief propagation".

It is therefore clear that rather than a precise definition for soft computing, it is instead defined by extension, by means of different concepts and techniques which attempt to overcome the difficulties which arise in real problems which occur in a world which is imprecise, uncertain and difficult to categorize.

There have been various subsequent attempts to further hone this definition, with differing results, and among the possible alternative definitions, perhaps the most suitable is the one presented in [4]: "Every computing process that purposely includes imprecision into the calculation on one or more levels and allows this imprecision either to change (decrease) the granularity of the problem, or to "soften" the goal of optimalisation at some stage, is defined as to belonging to the field of soft computing".

The viewpoint that we will consider here (and which we will adopt in future) is another way of defining soft computing, whereby it is considered to be the antithesis of what we might call hard computing. Soft computing could therefore be seen as a series of techniques and methods so that real practical situations could be dealt with in the same way as humans deal with them, i.e. on the basis of intelligence, common sense, consideration of analogies, approaches, etc. In this sense, soft computing is a family of problem-resolution methods headed by approximate reasoning and functional and optimization approximation methods, including search methods. Soft computing is therefore the theoretical basis for the area of intelligent systems and it is evident that the difference between the area of artificial intelligence and that of intelligent systems is that the first is based on hard computing and the second on soft computing.

From this other viewpoint on a second level, soft computing can be then expanded into other components which contribute to a definition by extension, such as the one first given. From the beginning [5], the components considered to be the most important in this second level are probabilistic reasoning, fuzzy logic and fuzzy sets, neural networks, and genetic algorithms (GA), which because of their interdisciplinary, applications and results immediately stood out over other methodologies such as the previously mentioned chaos theory, evidence theory, etc. The popularity of GA, together with their proven efficiency in a wide variety of areas and applications, their attempt to imitate natural creatures (e.g. plants, animals, humans) which are clearly soft (i.e. flexible, adaptable, creative, intelligent, etc.), and especially the extensions and different versions, transform this fourth second-level ingredient into the well-known evolutionary algorithms (EA) which consequently comprise the fourth fundamental component of soft computing.

From this last conception of soft computing, playing fuzzy sets and fuzzy logic a necessarily basic role, we can describe other areas emerging around it simply by considering some of the possible combinations which can arise:

1.      From the first level and beginning with approximate reasoning methods, when we only concentrate on probabilistic models, we encounter the Dempster-Shafer theory and Bayesian networks. However, when we consider probabilistic methods combined with fuzzy logic, and even with some other multi-valued logics, we encounter what we could call hybrid probabilistic models, fundamentally probability theory models for fuzzy events, fuzzy event belief models, and fuzzy influence diagrams.

2.      When we look at the developments directly associated with fuzzy logic, fuzzy systems and in particular fuzzy controllers stand out. Then, arising from the combination of fuzzy logic with neural networks and EA are fuzzy logic-based hybrid systems, the foremost exponents of which are fuzzy neural systems, controllers adjusted by neural networks (neural fuzzy systems which differ from the previously mentioned fuzzy neural systems), and fuzzy logic-based controllers which are created and adjusted with EA.

3.      Moving through the first level to the other large area covered by soft computing (functional approach/optimization methods) the first component which appears is that of neural networks and their different models. Arising from the interaction with fuzzy logic methodologies and EA methodologies are hybrid neural systems, and in particular fuzzy control of network parameters, and the formal generation and weight generation in neural networks.

4.      The fourth typical component of soft computing and perhaps the newest yet possibly most up-to-date is that of EA, and associated with these are four large, important areas: evolutionary strategies, evolutionary programming, GA, and genetic programming. If we were only to focus on these last areas, we could consider that in this case the amalgam of methodologies and techniques associated with soft computing culminate in three important lines: fuzzy genetic systems, bioinspired systems, and applications for the fuzzy control of evolutionary parameters.

On further examination of this last component some additional considerations are needed. Firstly, independently of the broad-minded approach adopted to contemplate what can be embraced by fuzzy genetic systems, bioinspired systems, and fuzzy control applications on evolutionary parameters, other important topics are missing from this description. Secondly, if we are referring in particular to bioinspired systems, it is clear that not only are they the product of fuzzy logic, neural networks or EA (with all the variants that we can consider for these three components) but also that other extremely important methodologies are involved in them.

In the sections which follow we will therefore justify a new definition for soft computing components, which was first referred to in [6], in order to provide a clearer perspective of the different areas that this covers without any loss of essence.

Refrence:

http://modo.ugr.es/en/soft_computing

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