Quality Of Service Routing Computer Science Essay

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The Qos (Quality of service) routing is finding the cheapest path. QoS routing is a critical element for QoS provision. It is to find a constrained shortest path. In particular, finding the cheapest delay-constrained path is critical.

A packet is sent on the shortest path to its destination. If multiple shortest path are there then shortest choose the arbitrarily.

The characteristic of routing algorithm consisting the weights are assigned statically. The route change occurred only when change in the topology. Qos used in multimedia conferencing.

The network provides a bandwidth to a flow. A flow is a sequence of packets from source to destination.

In order to exploit the available capacity on alternate route the Qos routing proposed.

The qos traffic itself will saturate the links. If the qos traffic is given priority in the link bandwidth allocation, their requirements will be met. The technique reduces the bandwidth available to the best effort traffic. The best effort traffic in the case of high load from qos traffic [1],[2].

The Qos guarantee for the conventional static shortest path routing for flows.

Use dynamically obtained load information to compute the shortest path routes for best-effort traffic.

The techniques for distributing load information and using it for computing shortest paths are same as in QoS routing. It does not require connection oriented network layer. Since packets of best-effort flows as well as those that require QoS are routed using the conventional shortest path datagram routing.

Our algorithm Does not require connection oriented network layer. Thus, it can be realized in networks such as Internet. _ It reduces route instability. Unlike QoS routing where the path chosen was governed by the load imposed by QoS flows, in our algorithm the path chosen for best effort traffic is not governed by the best effort load. This reduces the coupling in the routing control loop. Hence, the route is expected to be more stable.

Dynamic routing technique [1] that made dynamic routing decisions for best effort traffic based on measured load of best effort traffic itself. It can be realized at a lower cost than QoS routing. In case of flows that


The Qos mainly consisting these problems:

Packet scheduling, admission control, resource reservation, and traffic engineering.

It is to find a constrained shortest path â€" a network path that satisfies a given set of constraints [1], [2]. For interactive real-time traffic, the delay constrained least-cost path has particular importance.

A network that supports qos on traffic engineering with the application software and reserve capacity

In the network nodes. A best effort network does not support quality of service. The complex qos control mechanism is providing high quality communication over a best network over-provisioning the capacity so that it is sufficient for the expected peak traffic load.

Many problems happened to packets when it travel from source to destination.

The following problems occurred from the point of view of the sender and receiver: [3], [4].


The other users sharing the same network resources, the bit-rate that can be provided to a certain data stream .which is too low for real-time multimedia services, all data streams get the same scheduling priority.

Dropped packets:

The router failed to drop the packets, if they arrive when buffers are already full, the packets might be droped.its depending on the state of the network. And which is impossible to define what will happen in advanced.


Its takes a long time for each packet to reach the destination. because it takes a less direct route to avoid congestion. This is different from bandwidth, as the delay can build up over time, even if the bandwidth is almost normal.


Packets from the source will reach the destination with different delays. A packet's delay varies with its position in the queues of the routers along the path between source and destination and this position can vary unpredictably.

Out-of-order delivery:

When a collection of related packets is routed through the Internet, different packets may take different routes, each resulting in a different delay. The result is that the packets arrive in a different order than they were sent.


Sometimes the packets are corrupted, while traveling, the destination detects this packets. That’s packets are dropped, and again ask the sender to repeat itself.


The new idea of this solution is to agree on a discrete jitter value per QoS class which is imposed on network nodes. Including best effort, four QoS classes were defined, two elastic and two inelastic. The solution has several benefits:

End-to-end delay and packet loss rate can be predicted [4].

1. It is easy to implement with simple scheduler and queue length.

2. Nodes can be easily verified for compliance.

3. End users do notice the difference in quality.

4. The usage of traffic classes.

5. Appropriate network dimensioning.


[1] Eric Crawley, Raj Nair, Bala Rajagopalan, and Hal Sandick. A Framework for QoS-based Routing in the Internet. Internet RFC (RFC 2386), August 1998.

[2] R. Guerin, S. Kamat, A. Orda, T. Przygienda, and D. Williams. QoS Routing Mechanisms and OSPF Extensions. Internet Draft (draft-guerin-qos-routing-ospf-03.txt), work in progress, March 1998.

[3] Technical, Commercial and Regulatory Challenges of QoS: An Internet Service Model Perspective by Xipeng Xiao (Morgan Kaufmann, 2008, ISBN 0-12-373693-5)

[4] Deploying IP and MPLS QoS for Multiservice Networks: Theory and Practice by John Evans, Clarence Filsfils (Morgan Kaufmann, 2007, ISBN 0-12-370549-5).

Shortest path constraints:


The shortest path is between specific pairs of nodes, means from one origin to all other nodes or forms all other nodes to single destination .The shortest path passing through a specific set of nodes.

The Qos (Quality of service) routing is finding the cheapest path. In particular, finding the cheapest delay-constrained path is critical. The designing algorithms that solve the "-approximation of the problem with an adjustable accuracy. The main approach is to discretize (i.e., scale and round)The algorithms directly relates to the errors during discretization. Mainly consisting two techniques that reduce the discretization errors. To reduce the execution time using power -law topologies with 1000 nodes. To implement distributed multimedia applications it is difficult of ensuring Qos.

The main purpose of this project is creating shortest path using constraints, by using two techniques, randomized discretization and path-delay discretization, which reduce the discretization errors and allow faster algorithms to design. The discretization error on a path is statistically proportional to the path length.

The end-to-end packet delay [1] and loss behavior in the Internet. By varying the interval between probe packets, it is possible to study the structure of the Internet load over different time scales.

Our project agrees with results obtained by others using simulation and experimental approaches. For example, our estimates of Internet workload are consistent with the hypothesis of a mix of bulk traffic with larger packet size, and interactive traffic with smaller packet size.


The problem is also called the single-pair shortest path problem.

The shortest path mainly consisting these problems:

The single-source shortest path problem, in which we have to find shortest paths from a source to destination.

The single-destination shortest path problem, in which we have to find shortest paths from all vertices in the graph to a single destination .This, can be reduced to the single-source shortest path problem by reversing the edges.

The all-pairs shortest path problem, in which we have to find shortest paths between every pair of vertices.

These generalizations have significantly more efficient algorithms than the simplistic approach of running a single-pair shortest path algorithm on all relevant pairs of vertices.

Shortest problem with resource constraints:

The elementary shortest-path problem (ESPPRC) with resource constraints is a widely used modeling tool in formulating vehicle-routing and crew-scheduling applications. The ESPPRC often occurs as a sub problem of an enclosing problem, where it is used to generate implicitly the set of all feasible routes or schedules, as in the column-generation formulation of the vehicle-routing problem with time windows (VRPTW). As the ESPPRC problem is NP-hard in the strong sense, classical solution approaches are based on the corresponding no elementary shortest-path problem with resource constraints (SPPRC), which can be solved using a pseudo-polynomial labeling algorithm.