Cloud data center management is a key problem due to the numerous and heterogeneous strategies that can be applied, ranging from the VM placement to the federation with other clouds. Performance evaluation of Cloud Computing infrastructures is required to predict and quantify the cost-benefit of a strategy portfolio and the corresponding Quality of Service (QoS) experienced by users. Such analyses are not feasible by simulation or on-the-field experimentation, due to the great number of parameters that have to be investigated.
In this paper, we present an analytical
model, based on Stochastic Reward Nets (SRNs), that is both scalable to model
systems composed of thousands of resources and flexible to represent different
policies and cloud-specific strategies. Several performance metrics are defined
and evaluated to analyze the behavior of a Cloud data center: utilization,
availability, waiting time, and responsiveness. A resiliency analysis is also
provided to take into account load bursts. Finally, a general approach is
presented that, starting from the concept of system capacity, can help system
managers to opportunely set the data center parameters under different working
conditions.
EXISTING SYSTEM:
In order to integrate business requirements and application level needs, in terms of Quality of Service (QoS), cloud service provisioning is regulated by Service Level Agreements (SLAs): contracts between clients and providers that express the price for a service, the QoS levels required during the service provisioning, and the penalties associated with the SLA violations. In such a context, performance evaluation plays a key role allowing system managers to evaluate the effects of different resource management strategies on the data center functioning and to predict the corresponding costs/benefits.
Cloud systems differ from traditional distributed systems. First of all, they are characterized by a very large number of resources that can span different administrative domains. Moreover, the high level of resource abstraction allows implementing particular resource management techniques such as VM multiplexing or VM live migrations that, even if transparent to final users, have to be considered in the design of performance models in order to accurately understand the system behavior.
Finally, different clouds, belonging to
the same or to different organizations, can dynamically join each other to
achieve a common goal, usually represented by the optimization of resources
utilization. This mechanism, referred to as cloud federation, allows providing
and releasing resources on demand thus providing elastic capabilities to the
whole infrastructure.
DISADVANTAGES:
PROPOSED SYSTEM:
In this paper, we present a stochastic model, based on Stochastic Reward Nets (SRNs), that exhibits the above mentioned features allowing capturing the key concepts of an IaaS cloud system. The proposed model is scalable enough to represent systems composed of thousands of resources and it makes possible to represent both physical and virtual resources exploiting cloud specific concepts such as the infrastructure elasticity.
We present work is that a generic and
comprehensive view of a cloud system is presented. Low level details, such as
VM multiplexing, are easily integrated with cloud based actions such as
federation, allowing investigating different mixed strategies. An exhaustive
set of performance metrics are defined regarding both the system provider
(e.g., utilization) and the final users (e.g., responsiveness).
ADVANTAGES:
To provide a fair comparison among different resource management strategies, also taking into account the system elasticity, a performance evaluation approach is described. Such an approach, based on the concept of system capacity, presents a holistic view of a cloud system and it allows system managers to study the better solution with respect to an established goal and to opportunely set the system parameters.
Our analytical techniques represent a good candidate, thanks to the limited solution cost of their associated models. However, to accurately represent a cloud system, an analytical model has to be:
. Scalable: To deal with very large systems composed of hundreds or thousands of resources.
. Flexible: Allowing us to easily implement different strategies and policies and to represent different working conditions.
HARDWARE REQUIREMENT:
SYSTEM DESIGN:
ARCHITECTURE DIAGRAM / UML DIAGRAMS / DAT FLOW DIAGRAM:
External sources or destinations, which may be people or organizations or other entities
Here the data referenced by a process is stored and retrieved.
People, procedures or devices that produce data. The physical component is not identified.
Data moves in a specific direction from an origin to
a destination. The data flow is a “packet” of data.
There are several common modeling rules when creating DFDs:
SYSTEM
ARCHITECTURE:
IMPLEMENTATION:
SRNs allow us to define reward functions that can be associated to a particular state of the model to evaluate the performance level reached by the system during the sojourn in that state.
In the following, we are interested in
performance metrics able to characterize the system behavior from both the provider
and the user point of views. Such metrics will help system designer to size and
manage the cloud data center and they will also be determinant in the SLA
definitions.
Responsiveness It is the steady-state probability R that the system is able to accept a request within a given time deadline _. The computation of such a parameter requires the knowledge of the waiting time cumulative distribution function (CDF). To this end, it is possible to apply the tagged customer technique by modifying the SRN model to isolate the behavior of a single user request u and to observe its movements through the system. In the tagged customer model shown in Fig. 3, the system queue is modeled through two places. Place Pcustomer contains a single token that represents the arrival of request u. The P tokens initially present in place Pqueue represent the number of requests still waiting in the queue when u arrives, while the M1 and M2 tokens initially present in places Pres and Prun represent the corresponding system status.
MODULES:
USER MODULE:
IAAS CLOUD SYSTEM:
ANALYTICAL MODEL:
CLOUD FEDERATION:
MODELING VM MULTIPLEXING:
RESILIENCY
ANALYSIS:
MODULES DESCRIPTION:
USER MODULE:
ADMIN:
In this module is used to help the server to view details and upload files with the security. Admin upload the data’s to database. Also view the subscriber details and user details. Admin find the redistribute details. Also who send the data and receive the data’s.
USER:
In this module, Users are having authentication and security to access the detail which is presented in the ontology system. Before accessing or searching the details user should have the account in that otherwise they should register first user can register their details like name, password, gender, age, and then. We develop this module, where the cloud storage can be made secure.
IAAS CLOUD SYSTEM:
Cloud computing is a promising technology able to strongly modify the way computing and storage resources will be accessed in the near future in the provision of on-demand access to virtual resources available on the Internet, cloud systems offer services at three different levels: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). In particular, IaaS clouds provide users with computational resources in the form of virtual machine (VM) instances deployed in the provider data center, while PaaS and SaaS clouds offer services in terms of specific solution stacks and application software suites, respectively.
Our business requirements and application-level needs, in terms of quality of service (QoS), cloud service provisioning is regulated by service-level agreements (SLAs): contracts between clients and providers that express the price for a service, the QoS levels required during the service provisioning, and the penalties associated with the SLA violations. In such a context, performance evaluation plays a key role allowing system managers to evaluate the effects of different resource management strategies on the data center.
ANALYTICAL MODEL:
IaaS cloud system composed of N physical resources job requests (in terms of VM instantiation requests) are enqueued in the system queue. Such a queue has a finite size Q; once its limit is reached, further requests are rejected. The system queue is managed according to a FIFO scheduling policy. When a resource is available, a job is accepted and the corresponding VM is instantiated.
We assume that the instantiation time is negligible and that the service time (i.e., the time needed to execute a job) is exponentially distributed with mean 1=_. According to the VM multiplexing technique in the cloud system can provide a number M of logical resources greater than N. In this case, multiple VMs can be allocated in the same physical machine (PM), for example, a core in a multicore architecture.
Multiple VMs sharing the same PM can incur in a reduction of the performance mainly due to I/O interference between VMs. We define the degradation factor d (_ 0) as the percentage increase in the expected service time experienced by a VM when multiplexed with another VM. The performance degradation of multiplexed VMs depends on the multiplexing technique.
CLOUD FEDERATION:
Cloud federation allows the system to use, in particular situations, the resources offered by other public cloud systems through a sharing and paying model. In this way, elastic capabilities can be exploited to respond to particular load conditions. Job requests can be redirected to other clouds by transferring the corresponding VM disk images through the network. With respect to the federation technique, we make the following assumptions:
Finally, with respect to the arrival process, we will investigate three different scenarios. In the first one (constant arrival process), we assume the arrival process be a homogeneous Poisson process with rate _. However, largescale distributed systems with thousands of users, such as cloud systems, could exhibit self-similarity/long-range dependence with respect to the arrival process. For these reasons, to take into account the dependences of the job arrival rate on both the days of a week and the hours of a day, in the second scenario (Periodic arrival process), we also choose to model the job arrival process as a Markov Modulated Poisson Process (MMPP).
MODELING VM MULTIPLEXING:
The proposed model is scalable enough to represent systems composed of thousands of resources and it makes possible to represent both physical and virtual resources exploiting cloud-specific concepts such as the infrastructure elasticity. With respect to the existing literature, the innovative aspect of the present work is that a generic and comprehensive view of a cloud system is presented. Low-level details, such as VM multiplexing, are easily integrated with cloud-based actions such as federation, allowing us to investigate different mixed strategies. An exhaustive set of performance metrics is defined regarding both the system provider (e.g., utilization) and the final users (e.g., responsiveness).
Moreover, different working conditions are investigated and a resiliency analysis is provided to take into account the effects of load bursts. Finally, to provide a fair comparison among different resource management strategies, also taking into account the system elasticity, a performance evaluation approach is described. Such an approach, based on the concept of system capacity, presents a holistic view of a cloud system and it allows system managers to study the better solution with respect to an established goal and to opportunely set the system parameters.
VM multiplexing technique in the cloud system can provide a number M of logical resources greater than N. In this case, multiple VMs can be allocated in the same physical machine (PM), for example, a core in a multicore architecture. Multiple VMs sharing the same PM can incur in a reduction of the performance mainly due to I/O interference between VMs. We define the degradation factor d (_ 0) as the percentage increase in the expected service time experienced by a VM when multiplexed with another VM. The performance degradation of multiplexed VMs depends on the multiplexing technique and on the VM placement strategy. We assume that, to reduce the degradation and to obtain a fair distribution of VMs, the system is able to optimally balance the load among the PMs with respect to the resources required by VMs (e.g., trying to multiplex CPU-bound VMs only with I/O-bound VMs), thus reaching a homogeneous degradation factor. Then, indicating with T ¼ 1=_ the expected service time of a VM in isolation, we can derive the expected time needed to execute two multiplexed VMs as T2 ¼ T _ ð1 þ dÞ. In general, we can express the expected execution time of I multiplexed VMs
RESILIENCY ANALYSIS:
Through a transient solution of the cloud performance model of it is possible to investigate the trend over time of some performance metrics. Such an analysis is straightforward to assess the resiliency of the cloud infrastructure, in particular when the load is characterized by bursts. In fact, even if the infrastructure is optimally sized with respect to the expected load, during a load burst, users can experience a degradation of the perceived QoS with corresponding violations of SLAs. For this reason, it is needed to predict the effects of a particular load condition to study the ability of the system to react to an overload situation. To study the system resiliency, we highlight the arrival of a single burst taking into account a bursty arrival process characterized by the following behavior:
The bursty arrival process is modeled by opportunely changing the exponentially distributed firing time of the transition Tarr in the cloud performance model through the adoption of the technique described in of all; we can identify three temporal phases:
In each phase, the model is solved in transitory by setting the firing rate of Tarr with the corresponding mean value: _n for the regular load, _b for the load burst. Moreover, at the beginning of each phase (i.e., before the change on the firing rate is applied), the initial state probabilities of the model.
CHAPTER 5
5.0 SYSTEM STUDY:
5.1 FEASIBILITY STUDY:
The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.
Three key considerations involved in the feasibility analysis are
5.1.1 ECONOMICAL FEASIBILITY:
This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.
This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.
5.1.3 SOCIAL FEASIBILITY:
The aspect of study is to check the level of
acceptance of the system by the user. This includes the process of training the
user to use the system efficiently. The user must not feel threatened by the
system, instead must accept it as a necessity. The level of acceptance by the
users solely depends on the methods that are employed to educate the user about
the system and to make him familiar with it. His level of confidence must be
raised so that he is also able to make some constructive criticism, which is
welcomed, as he is the final user of the system.
5.2 SYSTEM TESTING:
Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months. This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.
5.2.1 UNIT TESTING:
A program
represents the logical elements of a system. For a program to run
satisfactorily, it must compile and test data correctly and tie in properly
with other programs. Achieving an error free program is the responsibility of
the programmer. Program testing checks
for two types
of errors: syntax
and logical. Syntax error is a
program statement that violates one or more rules of the language in which it
is written. An improperly defined field dimension or omitted keywords are
common syntax errors. These errors are shown through error message generated by
the computer. For Logic errors the programmer must examine the output
carefully.
UNIT TESTING:
| Description | Expected result |
| Test for application window properties. | All the properties of the windows are to be properly aligned and displayed. |
| Test for mouse operations. | All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions. |
5.1.3 FUNCTIONAL TESTING:
Functional
testing of an application is used to prove the application delivers correct
results, using enough inputs to give an adequate level of confidence that will
work correctly for all sets of inputs. The functional testing will need to
prove that the application works for each client type and that personalization
function work correctly.When a program is tested, the actual output is
compared with the expected output. When there is a discrepancy the sequence of
instructions must be traced to determine the problem. The process is facilitated by breaking the
program into self-contained portions, each of which can be checked at certain
key points. The idea is to compare program values against desk-calculated
values to isolate the problems.
FUNCTIONAL TESTING:
| Description | Expected result |
| Test for all modules. | All peers should communicate in the group. |
| Test for various peer in a distributed network framework as it display all users available in the group. | The result after execution should give the accurate result. |
5.1. 4 NON-FUNCTIONAL TESTING:
The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:
5.1.5 LOAD TESTING:
An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.
Load Testing
| Description | Expected result |
| It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received. | Should designate another active node as a Server. |
5.1.5 PERFORMANCE TESTING:
Performance tests
are utilized in order to determine the widely defined performance of the
software system such as execution time associated with various parts of the
code, response time and device utilization. The intent of this testing is to
identify weak points of the software system and quantify its shortcomings.
PERFORMANCE TESTING:
| Description | Expected result |
| This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management. | Should handle large input values, and produce accurate result in a expected time. |
5.1.6 RELIABILITY TESTING:
The software
reliability is the ability of a system or component to perform its required
functions under stated conditions for a specified period of time and it is
being ensured in this testing. Reliability can be expressed as the ability of
the software to reveal defects under testing conditions, according to the
specified requirements. It the portability that a software system will operate
without failure under given conditions for a given time interval and it focuses
on the behavior of the software element. It forms a part of the software quality
control team.
RELIABILITY TESTING:
| Description | Expected result |
| This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application. | In case of failure of the server an alternate server should take over the job. |
5.1.7 SECURITY TESTING:
Security
testing evaluates system characteristics that relate to the availability,
integrity and confidentiality of the system data and services. Users/Clients
should be encouraged to make sure their security needs are very clearly known
at requirements time, so that the security issues can be addressed by the
designers and testers.
SECURITY TESTING:
| Description | Expected result |
| Checking that the user identification is authenticated. | In case failure it should not be connected in the framework. |
| Check whether group keys in a tree are shared by all peers. | The peers should know group key in the same group. |
5.1.7 WHITE BOX TESTING:
White box
testing, sometimes called glass-box
testing is a test case
design method that uses
the control structure
of the procedural design to
derive test cases. Using
white box testing
method, the software engineer
can derive test
cases. The White box testing focuses on the inner structure of the
software structure to be tested.
5.1.8 WHITE BOX TESTING:
| Description | Expected result |
| Exercise all logical decisions on their true and false sides. | All the logical decisions must be valid. |
| Execute all loops at their boundaries and within their operational bounds. | All the loops must be finite. |
| Exercise internal data structures to ensure their validity. | All the data structures must be valid. |
5.1.9 BLACK BOX TESTING:
Black box
testing, also called behavioral testing, focuses on the functional requirements
of the software. That is, black testing
enables the software
engineer to derive
sets of input
conditions that will
fully exercise all
functional requirements for a
program. Black box testing is not
alternative to white box techniques.
Rather it is
a complementary approach
that is likely
to uncover a different
class of errors
than white box methods. Black box testing attempts to find
errors which focuses on inputs, outputs, and principle function of a software
module. The starting point of the black box testing is either a specification
or code. The contents of the box are hidden and the stimulated software should
produce the desired results.
5.1.10 BLACK BOX TESTING:
| Description | Expected result |
| To check for incorrect or missing functions. | All the functions must be valid. |
| To check for interface errors. | The entire interface must function normally. |
| To check for errors in a data structures or external data base access. | The database updation and retrieval must be done. |
| To check for initialization and termination errors. | All the functions and data structures must be initialized properly and terminated normally. |
All
the above system testing strategies are carried out in as the development,
documentation and institutionalization of the proposed goals and related
policies is essential.
CHAPTER 7
7.0 SOFTWARE SPECIFICATION:
7.1 FEATURES OF .NET:
Microsoft .NET is a set of Microsoft software technologies for rapidly building and integrating XML Web services, Microsoft Windows-based applications, and Web solutions. The .NET Framework is a language-neutral platform for writing programs that can easily and securely interoperate. There’s no language barrier with .NET: there are numerous languages available to the developer including Managed C++, C#, Visual Basic and Java Script.
The .NET framework provides the foundation for components to interact seamlessly, whether locally or remotely on different platforms. It standardizes common data types and communications protocols so that components created in different languages can easily interoperate.
“.NET” is
also the collective name given to various software components built upon the
.NET platform. These will be both products (Visual Studio.NET and Windows.NET
Server, for instance) and services (like Passport, .NET My Services, and so
on).
7.2 THE .NET FRAMEWORK
The .NET Framework has two main parts:
1. The Common Language Runtime (CLR).
2. A hierarchical set of class libraries.
The CLR is described as the “execution engine” of .NET. It provides the environment within which programs run. The most important features are
Managed Code
The code
that targets .NET, and which contains certain extra Information – “metadata” –
to describe itself. Whilst both managed and unmanaged code can run in the
runtime, only managed code contains the information that allows the CLR to
guarantee, for instance, safe execution and interoperability.
Managed Data
With Managed Code comes Managed Data. CLR provides memory allocation and Deal location facilities, and garbage collection. Some .NET languages use Managed Data by default, such as C#, Visual Basic.NET and JScript.NET, whereas others, namely C++, do not. Targeting CLR can, depending on the language you’re using, impose certain constraints on the features available. As with managed and unmanaged code, one can have both managed and unmanaged data in .NET applications – data that doesn’t get garbage collected but instead is looked after by unmanaged code.
Common Type System
The CLR uses something called the Common Type System (CTS) to strictly enforce type-safety. This ensures that all classes are compatible with each other, by describing types in a common way. CTS define how types work within the runtime, which enables types in one language to interoperate with types in another language, including cross-language exception handling. As well as ensuring that types are only used in appropriate ways, the runtime also ensures that code doesn’t attempt to access memory that hasn’t been allocated to it.
Common Language Specification
The CLR provides built-in support for language interoperability. To ensure that you can develop managed code that can be fully used by developers using any programming language, a set of language features and rules for using them called the Common Language Specification (CLS) has been defined. Components that follow these rules and expose only CLS features are considered CLS-compliant.
7.3 THE CLASS LIBRARY
.NET provides a single-rooted hierarchy of classes, containing over 7000 types. The root of the namespace is called System; this contains basic types like Byte, Double, Boolean, and String, as well as Object. All objects derive from System. Object. As well as objects, there are value types. Value types can be allocated on the stack, which can provide useful flexibility. There are also efficient means of converting value types to object types if and when necessary.
The set of classes is pretty comprehensive, providing collections, file, screen, and network I/O, threading, and so on, as well as XML and database connectivity.
The class library is subdivided into a number of sets (or namespaces), each providing distinct areas of functionality, with dependencies between the namespaces kept to a minimum.
7.4 LANGUAGES SUPPORTED BY .NET
The multi-language capability of the .NET Framework and Visual Studio .NET enables developers to use their existing programming skills to build all types of applications and XML Web services. The .NET framework supports new versions of Microsoft’s old favorites Visual Basic and C++ (as VB.NET and Managed C++), but there are also a number of new additions to the family.
Visual Basic .NET has been updated to include many new and improved language features that make it a powerful object-oriented programming language. These features include inheritance, interfaces, and overloading, among others. Visual Basic also now supports structured exception handling, custom attributes and also supports multi-threading.
Visual Basic .NET is also CLS compliant, which means that any CLS-compliant language can use the classes, objects, and components you create in Visual Basic .NET.
Managed Extensions for C++ and attributed programming are just some of the enhancements made to the C++ language. Managed Extensions simplify the task of migrating existing C++ applications to the new .NET Framework.
C# is Microsoft’s new language. It’s a C-style language that is essentially “C++ for Rapid Application Development”. Unlike other languages, its specification is just the grammar of the language. It has no standard library of its own, and instead has been designed with the intention of using the .NET libraries as its own.
Microsoft Visual J# .NET provides the easiest transition for Java-language developers into the world of XML Web Services and dramatically improves the interoperability of Java-language programs with existing software written in a variety of other programming languages.
Active State has created Visual Perl and Visual Python, which enable .NET-aware applications to be built in either Perl or Python. Both products can be integrated into the Visual Studio .NET environment. Visual Perl includes support for Active State’s Perl Dev Kit.
Other languages for which .NET compilers are available include
| ASP.NET XML WEB SERVICES | Windows Forms |
| Base Class Libraries | |
| Common Language Runtime | |
| Operating System |
Fig1 .Net Framework
C#.NET is also compliant with CLS (Common Language Specification) and supports structured exception handling. CLS is set of rules and constructs that are supported by the CLR (Common Language Runtime). CLR is the runtime environment provided by the .NET Framework; it manages the execution of the code and also makes the development process easier by providing services.
C#.NET is
a CLS-compliant language. Any objects, classes, or components that created in
C#.NET can be used in any other CLS-compliant language. In addition, we can use
objects, classes, and components created in other CLS-compliant languages in
C#.NET .The use of CLS ensures complete interoperability among applications,
regardless of the languages used to create the application.
CONSTRUCTORS AND DESTRUCTORS:
Constructors are used to initialize objects, whereas destructors are used to destroy them. In other words, destructors are used to release the resources allocated to the object. In C#.NET the sub finalize procedure is available. The sub finalize procedure is used to complete the tasks that must be performed when an object is destroyed. The sub finalize procedure is called automatically when an object is destroyed. In addition, the sub finalize procedure can be called only from the class it belongs to or from derived classes.
GARBAGE COLLECTION
Garbage Collection is another new feature in C#.NET. The .NET Framework monitors allocated resources, such as objects and variables. In addition, the .NET Framework automatically releases memory for reuse by destroying objects that are no longer in use.
In C#.NET, the garbage collector checks for the objects that are not currently in use by applications. When the garbage collector comes across an object that is marked for garbage collection, it releases the memory occupied by the object.
OVERLOADING
Overloading is another feature in C#. Overloading enables us
to define multiple procedures with the same name, where each procedure has a
different set of arguments. Besides using overloading for procedures, we can
use it for constructors and properties in a class.
MULTITHREADING:
C#.NET also supports multithreading. An application that supports multithreading can handle multiple tasks simultaneously, we can use multithreading to decrease the time taken by an application to respond to user interaction.
STRUCTURED EXCEPTION HANDLING
C#.NET supports structured handling, which enables us to
detect and remove errors at runtime. In C#.NET, we need to use
Try…Catch…Finally statements to create exception handlers. Using
Try…Catch…Finally statements, we can create robust and effective exception
handlers to improve the performance of our application.
7.5 THE .NET FRAMEWORK
The .NET Framework is a new computing platform that simplifies application development in the highly distributed environment of the Internet.
OBJECTIVES OF .NET FRAMEWORK
1. To provide a consistent object-oriented programming environment whether object codes is stored and executed locally on Internet-distributed, or executed remotely.
2. To provide a code-execution environment to minimizes software deployment and guarantees safe execution of code.
3. Eliminates the performance problems.
There are
different types of application, such as Windows-based applications and
Web-based applications.
7.6 FEATURES OF SQL-SERVER
The OLAP Services feature available in SQL Server version 7.0 is now called SQL Server 2000 Analysis Services. The term OLAP Services has been replaced with the term Analysis Services. Analysis Services also includes a new data mining component. The Repository component available in SQL Server version 7.0 is now called Microsoft SQL Server 2000 Meta Data Services. References to the component now use the term Meta Data Services. The term repository is used only in reference to the repository engine within Meta Data Services
SQL-SERVER database consist of six type of objects,
They are,
1. TABLE
2. QUERY
3. FORM
4. REPORT
5.
MACRO
7.7 TABLE:
A database is a collection of data about a specific topic.
VIEWS OF TABLE:
We can work with a table in two types,
1. Design View
2. Datasheet View
Design View
To build or modify the structure of a table we work in the table design view. We can specify what kind of data will be hold.
Datasheet View
To add, edit or analyses the data itself we work in tables datasheet view mode.
QUERY:
A query is a question that has to be asked the data. Access gathers data that answers the question from one or more table. The data that make up the answer is either dynaset (if you edit it) or a snapshot (it cannot be edited).Each time we run query, we get latest information in the dynaset. Access either displays the dynaset or snapshot for us to view or perform an action on it, such as deleting or updating.
CHAPTER 7
APPENDIX
7.1 SAMPLE SOURCE CODE
7.2
SAMPLE OUTPUT
CHAPTER 8
8.1 CONCLUSION:
In this paper, we have presented a stochastic model to evaluate the performance of an IaaS cloud system. Several performance metrics have been defined, such as availability, utilization, and responsiveness, allowing us to investigate the impact of different strategies on both provider and user point of views. In a market-oriented area, such as the cloud computing, an accurate evaluation of these parameters is required to quantify the offered QoS and opportunely manage SLAs.
We present an analytical model, based on
Stochastic Reward Nets (SRNs), that is both scalable to model systems composed
of thousands of resources and flexible to represent different policies and
cloud-specific strategies. Several performance metrics are defined and
evaluated to analyze the behavior of a Cloud data center: utilization,
availability, waiting time, and responsiveness. A resiliency analysis is also
provided to take into account load bursts. Finally, a general approach is
presented that, starting from the concept of system capacity, can help system
managers to opportunely set the data center parameters under different working
conditions.
8.2 FUTURE ENHANCEMENT:
Future works will include the analysis
of autonomic techniques able to change on-the-fly the system configuration to
react to a change on the working conditions. We will also extend the model to
represent PaaS and SaaS cloud systems and to integrate the mechanisms needed to
capture VM migration and data center consolidation aspects that cover a crucial
role in energy saving policies.