CLIENT/SERVER TECHNOLOGY:FEATURES OF C / S SYSTEMS

By -
1. FEATURES OF C / S SYSTEMS
Advantages

The advantages of C / S systems derive from their distributed processing structure, which C / S systems adopt, where processing is handled by two or more cooperating geographically distinct processors.

1. Performance tuning: In a distributed processing environment, the service requests from a client are not concentrated on a single server but can be distributed among several servers. Therefore, it becomes possible to distribute required workload among processing resources and improve the performance, such as response time to the client. Furthermore, adopting an efficient al- gorithm of request distribution such as an adaptive algorithm taking account of current loads of all servers makes it possible to improve the throughput (the amount of work processed per unit time) of the system.

2. Availability improvement: Clients are sensitive to performance of the system, but they do not perceive the physical structure of the distribution for requests. In other words, the group of networked servers seems to the client like one server. In a distributed processing environment, even if one server breaks, other servers can substitute for the broken server and continue to process its tasks. This can guarantee the high availability of services to the client compared to a host-centric processing environment.

3. Scalability and cost-efficiency: Scalability is the capacity of the system to perform more total work in the same elapsed time when its processing power is increased. With a distributed system, the processing capacity of the system can be scaled incrementally by adding new computers or network elements as the need arises, and excessive investment in a system can be avoided at the introduction stage of the C / S system. On the other hand, with a host-centric system, if the load level is going to saturate the system, the current system will be replaced by a more powerful computer. If further growth is planned, redundant computer capacity will need to be built into the current system to cope with future growth in requirements.

Disadvantages

1. Strong dependence on the network infrastructure: Because a distributed environment is largely based on a networking infrastructure, the performance and availability of the distributed system are strongly influenced by the state of the network. For example, if a network failure, such as of the router or transmission line, occurs, performance and availability for services may seri- ously deteriorate. The system designer and manager should design the fault-tolerant system and plan the disaster recovery taking account of the networking infrastructure.

2. Security: From the viewpoint of security, the possibility exists that confidential information stored on the database may leak out through the network. The system manager should take security measures such as authentication, access control, and cryptographic control according to the security level of information.

3. Complexity of system configuration: C / S systems are composed of several components from multiple vendors. Distributed applications often have more complex functionality than cen-

tralized applications, and they are built from diverse components. Multitier architectures, which will be explained in the next section, provide a nearly limitless set of choices for where to locate processing functions and data. In addition to choices about locations, there are also more hardware and software choices, and more data sharing occurs. These cause the difficulty in developing a C / S system and the complexity of managing it.

Comments

Post a Comment

Popular posts from this blog

DUALITY THEORY:THE ESSENCE OF DUALITY THEORY

By -
Image
THE ESSENCE OF DUALITY THEORY Given our standard form for the primal problem at the left (perhaps after conversion from another form), its dual problem has the form shown to the right. Thus, with the primal problem in maximizatio n form, the dual problem is in minimization form instead. Furthermore, the dual problem uses exactly the same pa r amete r s as the primal problem, but in different locations, as summarized below. 1. The coefficients in the objective function of the primal problem are the right-hand sides of the functional constraints in the dual problem. 2. The right-hand sides of the functional constraints in the primal problem are the coef- ficients in the objective function of the dual problem. 3. The coefficients of a variable in the functional constraints of the primal problem are the coefficients in a functional constraint of the dual problem. To highlight the comparison, now look at these same two problems in matrix notation (as introduced at the beginn...
Read more

NETWORK OPTIMIZATION MODELS:THE MINIMUM SPANNING TREE PROBLEM

By -
Image
THE MINIMUM SPANNING TREE PROBLEM The minimum spanning tree problem bears some similarities to the main version of the shortest-path problem presented in the preceding section. In both cases, an undi r ecte d and connected network is being considered, where the given information includes some mea- sure of the positive length (distance, cost, time, etc.) associated with each link. Both problems also involve choosing a set of links that have the shortest total length among all sets of links that satisfy a certain property. For the shortest-path problem, this property is that the chosen links must provide a path between the origin and the destination. For the minimum spanning tree problem, the required property is that the chosen links must provide a path between ea c h pair of nodes. The minimum spanning tree problem can be summarized as follows: 1. You are given the nodes of a network but not the links. Instead, you are given the po- tential links and the positive length for each ...
Read more

INTEGER PROGRAMMING:THE BRANCH-AND-CUT APPROACH TO SOLVING BIP PROBLEMS

By -
TH E BRANCH-AND-CUT APPROACH TO SOLVING BIP PROBLEMS Integer programming has been an especially exciting area of OR since the mid-1980s be- cause of the dramatic progress being made in its solution methodology. Background To place this progress into perspective, consider the historical background. One big break- through had come in the 1960s and early 1970s with the development and refinement of the branch-and-bound approach. But then the state of the art seemed to hit a plateau. Rel- atively small problems (well under 100 variables) could be solved very efficiently, but even a modest increase in problem size might cause an explosion in computation time be- yond feasible limits. Little progress was being made in overcoming this exponential growth in computation time as the problem size was increased. Many important problems arising in practice could not be solved. Then came the next breakthrough in the mid-1980s, with the introduction of the b r an c h- and-cu t approach to sol...
Read more