Sensor networks are composed of small sensing devices that have the capability to take various measurements of their environment such as temperature, sound, light etc. These devices are equipped with a processor and wireless communication antenna and are powered with a battery. Upon deployment in a field, they form an ad hoc network and communicate with each other and with data processing centers. The routing protocol in such networks has an important effect on congestion, especially with increasing sizes of the deployments. Congestion becomes worse when a particular area is generating most of the data. This may occur in some deployments when sensors in one area of interest are requested to gather and transmit data at a higher rate than others.
We believe that all data generated in a sensor network may not be equally important; some may have a low priority while others have a higher priority and hence differentiated service must be provided to these data. In such a scenario, routing dynamics can lead to congestion on specific paths. Since congestion is a self-compounding problem, these paths are usually close to each other which lead to an entire zone in the network facing congestion. We refer to this zone as the congestion zone or conzone.
Congestion can adversely affect the network in two ways.
First, it can lead to indiscriminate dropping of data, i.e. some packets of high priority might be dropped while others of less priority are delivered. This happens because sensor nodes are very simple devices and do not have the capability to differentiate packets (i.e. they do not have multiple queues for different priority levels). Second, congestion can cause an increase in energy consumption as links become saturated. This can lead to depletion of the limited energy available in the sensor nodes in the congested area.
In this paper, we examine data delivery issues in the presence of congestion in wireless sensor networks. We propose the use of data prioritization and a simple priority aware routing protocol, Congestion Aware Routing (CAR). CAR does not use multiple priority queues, a QoS aware MAC layer or specialized scheduling algorithms. The first step in this protocol is to dynamically discover the conzone. The second step is to enforce differentiated routing; high priority packets are routed in the conzone. Low priority packets generated outside the conzone stay outside while those generated within the conzone are routed out. In effect, conzone nodes are dedicated to serving high priority data which will enable them to provide better service and lengthen their lifetime.
Our extensive simulations show that CAR leads to a significant increase in the successful packet delivery ratio of high priority data to the sink, and a clear decrease in the average delay to CAR also provides low jitter which makes it able to support real-time multimedia applications. It also reduces the energy consumed in the nodes that lie on the conzone which leads to an increase in connectivity lifetime. We now consider the network formation process. Once the sink node discovers its surrounding neighbors, it broadcasts a “Build Mesh” message asking all nodes in the network to organize as a mesh. In that message the sink provides its ID and zero as its depth. Once a neighboring node hears this message it will check if it has already joined the routing network (i.e. if it knows its depth); if not then it sets its depth to one plus the depth in the message received and sets the source of the message as a parent.
Each node then rebroadcasts the Build Mesh message, with its own ID and depth to its neighbors. If a node is already a member of the network, then it will check the depth in the message, and if that depth is less than its own, then the source of the message is added as a parent. In that case, the message is not rebroadcast. In this fashion, the Build Mesh message is sent down the network until all nodes become part of this routing structure. Similar to TAG, the Build Mesh message can be periodically broadcast to maintain the topology and adapt to changes caused by the failure, addition or mobility of nodes.
1.3 SCOPE OF THE PROJECT:
Design goals of the congestion aware routing (CAR) protocol for sensor networks are to provide high priority data with better service quality compared to other routing schemes. These include higher delivery ratios, lower delays and lower jitter to support real-time data. We also aim at decreasing energy consumption which will lengthen the lifetime of the network. To achieve these goals, CAR divides the network into two regions; the congestion zone (conzone) and the remaining part of the network. While high priority data is routed through the conzone, low priority data is routed using the other nodes. Low priority data that originates outside the conzone is routed exclusively on off-conzone nodes using regular routing protocols such as low priority data that originate inside the conzone are efficiently routed out of the conzone.
ELASTIC OPTICAL NETWORKING: A NEW DAWN FOR THE OPTICAL LAYER?
PUBLICATION: O. Gerstel, M. Jinno, A. Lord, and S. J. B. Yoo, IEEE Commun. Mag., vol. 50, no. 2, pp. s12–s20, Feb. 2012.
Optical networks are undergoing significant changes, fueled by the exponential growth of traffic due to multimedia services and by the increased uncertainty in predicting the sources of this traffic due to the ever changing models of content providers over the Internet. The change has already begun: simple on-off modulation of signals, which was adequate for bit rates up to 10 Gb/s, has given way to much more sophisticated modulation schemes for 100 Gb/s and beyond. The next bottleneck is the 10-year-old division of the optical spectrum into a fixed “wavelength grid,” which will no longer work for 400 Gb/s and above, heralding the need for a more flexible grid. Once both transceivers and switches become flexible, a whole new elastic optical networking paradigm is born. In this article we describe the drivers, building blocks, architecture, and enabling technologies for this new paradigm, as well as early standardization efforts.
MODELING THE ROUTING AND SPECTRUM ALLOCATION PROBLEM FOR FLEXGRID OPTICAL NETWORKS
PUBLICATION: L. Velasco, M. Klinkowski, M. Ruiz, and J. Comellas, Photon. Netw. Commun., vol. 24, no. 3, pp. 177–186, 2012.
Flexgrid optical networks are attracting huge interest due to their higher spectrum efficiency and flexibility in comparison with traditional wavelength switched optical networks based on the wavelength division multiplexing technology. To properly analyze, design, plan, and operate flexible and elastic networks, efficient methods are required for the routing and spectrum allocation (RSA) problem. Specifically, the allocated spectral resources must be, in absence of spectrum converters, the same along the links in the route (the continuity constraint) and contiguous in the spectrum (the contiguity constraint). In light of the fact that the contiguity constraint adds huge complexity to the RSA problem, we introduce the concept of channels for the representation of contiguous spectral resources. In this paper, we show that the use of a pre-computed set of channels allows considerably reducing the problem complexity. In our study, we address an off-line RSA problem in which enough spectrum needs to be allocated for each demand of a given traffic matrix. To this end, we present novel integer lineal programming (ILP) formulations of RSA that are based on the assignment of channels. The evaluation results reveal that the proposed approach allows solving the RSA problem much more efficiently than previously proposed ILP-based methods and it can be applied even for realistic problem instances, contrary to previous ILP formulations.
DISTANCE-ADAPTIVE SPECTRUM RESOURCE ALLOCATION IN SPECTRUM-SLICED ELASTIC OPTICAL PATH NETWORK
PUBLICATION: M. Jinno et al., “,” IEEE Commun. Mag., vol. 48, no. 8, pp. 138–145, Aug. 2010.
The rigid nature of current wavelength-routed optical networks brings limitations on network utilization efficiency. One limitation originates from mismatch of granularities between the client layer and the wavelength layer. The recently proposed spectrum-sliced elastic optical path network (SLICE) is expected to mitigate this problem by adaptively allocating spectral resources according to client traffic demands. This article discusses another limitation of the current optical networks associated with worst case design in terms of transmission performance. In order to address this problem, we present a concept of a novel adaptation scheme in SLICE called distance-adaptive spectrum resource allocation. In the presented scheme the minimum necessary spectral resource is adaptively allocated according to the end-to-end physical condition of an optical path. Modulation format and optical filter width are used as parameters to determine the necessary spectral resources to be allocated for an optical path. Evaluation of network utilization efficiency shows that distance-adaptive SLICE can save more than 45 percent of required spectrum resources for a 12-node ring network. Finally, we introduce the concept of a frequency slot to extend the current frequency grid standard, and discuss possible spectral resource designation schemes.
QOT PREDICTION FOR CORE NETWORKS WITH UNCOMPENSATED COHERENT TRANSMISSION
PUBLICATION: M. Angelou, P. N. Ji, I. Tomkos, and T. Wang, in Proc. OECC/PS Jul. 2013, pp. 1–2, paper TuQ3-4.
We propose a
comprehensive QoT prediction tool based on fast analytical modeling for
on-the-fly signal assessments in networks with uncompensated coherent systems
and confirm its superiority in reducing over-engineering compared to
system-reach methods.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
The Problem of Existing Solutions in these scenario nodes in the network sends all high priority data to a single sink, tree-based routing is the most appropriate. In this routing scheme, a spanning tree is built with the high priority sink as its root. The setup of such a tree uses controlled flooding from the sink to all nodes in the network. Low priority data, on the other hand, do not need to follow the same routing scheme. This is true because there may be multiple low priority sinks and a node might send data to any of them. For example, temperature readings might be forwarded to one sink while the motion detection measurements go to another sink, and tree based routing schemes suffer from congestion, especially if the number of messages generated in the leaves is high.
This problem becomes worse when we have a mixture of high priority and low priority traffic traveling through the network. This is because low priority messages will cross the tree that is formed to route high priority data in order to reach their destinations. Therefore even when the rate of high priority data is relatively low, the background noise created by low priority traffic will create a congestion zone that spans the deployment from the critical area to the high priority sink. Nodes in this zone become overwhelmed and indiscriminately drop high and low priority messages. These nodes also consume more energy compared to other nodes in the network and hence die sooner. This will lead to only sub-optimal paths being available to route high priority data, or a total loss of connectivity from critical area to the sink even though other nodes outside a single routing scheme is used to route both types of traffic.
2.1.1 DISADVANTAGES:
In such a scenario, routing dynamics can lead to congestion on specific paths. Since congestion is a self-compounding problem, these paths are usually close to each other which lead to an entire zone in the network facing congestion. Congestion can adversely affect the network in two ways. First, it can lead to indiscriminate dropping of data, i.e. some packets of high priority might be dropped while others of less priority are delivered. This happens because sensor nodes are very simple devices and do not have the capability to differentiate packets (i.e. they do not have multiple queues for different priority levels). Second, congestion can cause an increase in energy consumption as links become saturated. This can lead to depletion of the limited energy available in the sensor nodes in the congested area.
2.2 PROPOSED SYSTEM:
We proposed Congestion Aware Routing (CAR) which is a simple routing protocol that uses data prioritization and treats packets according to their priorities. We defined a conzone as the set of sensors that will be required to route high priority packets from the data sources to the sink.
We presented algorithms to build a high priority routing mesh, dynamically discover and configure conzones, and perform differentiated routing. Our solutions do not require active queue management, maintenance of multiple queues or scheduling algorithms, or the use of specialized MAC protocols.
The proposed algorithm for RMSA in a nonlinear elastic network utilizing Nyquist pulse shaping is as follows:
2.2.1 ADVANTAGES:
2.3.1 HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN
ARCHITECUTRE DIAGRAM / UML DIAGRAM / DATA 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 in 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:
3.1 ARCHITECTURE DIAGRAM:
CHAPTER 4
4.0 IMPLEMENTATION:
4.1 ALGORITHM
4.2 MODULES:
SERVER CLIENT MODULE:
FIBER NONLINEARITIES:
DISCOVERY FROM SINK:
NETWORK PROBABILITY (NBP):
ROUTING ALGORITHMS (CAR):
4.3
MODUL DISCRIPTION:
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.
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 6
6.0 SOFTWARE SPECIFICATION:
6.1 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).
6.2 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.
6.3 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.
6.4 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.
6.5 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.
6.6 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
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
8.0 CONCLUSION:
Congestion aware routing has been
investigated in nonlinear elastic optical networks and shown to be effective for
the reference NSFNET topology. We observe that the network blocking probability
(NBP) follows a generalized extreme value distribution, allowing robust
estimates of the load for a given NBP to be obtained. When NSFNET is sequentially
loaded with 100 GbE demands the proposed algorithm with a flexgrid, allows the
network to support 1744 demands compared to 328 demands using a fixed 50 GHz
grid with shortest path routing for NBP = 1%. The congestion aware routing algorithms
investigated resulted in longer average paths, with 5% of all routes exceeding
the maximum shortest path in order to increase the overall network capacity.