How can traffic shaping help in congestion management?

- Traffic shaping is an important part of congestion avoidance mechanism which in turn comes under congestion management. 

- If the traffic can be controlled, obviously we would be able to maintain control over the network congestion. 

Congestion avoidance scheme can be divided in to the following two parts:

1. Feedback mechanism and
2. The control mechanism

- The feedback mechanism is also known as the network policies and the control mechanism is known as the user policies.

- Of course there are other components also but these two are the most important. 

- While analyzing one component it is simply assumed that the other components are operating at optimum levels. 

- At the end, it has to be verified whether the combined system is working as expected or not under various types of conditions.

Network policy has got the following three algorithms:

1. Congestion Detection: 

- Before information can be sent as the feedback to the network, its load level or the state level must be determined. 

- Generally, there can be n number of possible states of the network. 

- At a given time the network might be in one of these states. 

- Using the congestion detection algorithm, these states can be mapped in to the load levels that are possible. 

- There are two possible load levels namely under-load and over-load. 

- Under-load means below the knee point and overload occurs above knee point. 

- If this function’s k–ary version is taken, it would produce k load levels. 

- There are three criteria based up on which the congestion detection function would work. They are link utilization, queue lengths and processor utilization. 

2. Feedback Filter: 

- After the load level has been determined, it has to be verified that whether or not the state lasts for duration of sufficiently longer time before it is signaled to the users. 

- It is in this condition that the feedback of the state is actually useful. 

- The duration is long enough to be acted up on. 

- On the other hand a state that might change rapidly might create confusion. 

- The state passes by the time the users get to know of it. 

- Such states misleading feedback. 

- A low pass filter function serves the purpose of filtering the desirable states. 

3. Feedback Selector: 

- After the state has been determined, this information has to be passed to the users so that they may contribute in cutting down the traffic. 

- The purpose of the feedback selector function is to identify the users to whom the information has to be sent.

User policy has got the following three algorithms: 

1.Signal Filter: 

- The users to which the feedback signals are sent by the network interpret them after accumulating a number of signals. 

- The nature of the network is probabilistic and therefore signals might not be the same. 

- According to some signals the network might be under-loaded and according to some other it might be overloaded. 

- These signals have to be combined to decide the final action. 

- Based up on the percentage, an appropriate weighting function might be applied. 

2. Decision Function: 

- Once the load level of the network is known to the user, it has to be decided whether or not to increase the load.

- There are two parts of this function: the direction is determined by the first one and the amount is decided by the second one. 

- First part is decision function and the second one is increase/ decrease algorithms. 

3. Increase/Decrease Algorithm: 

- Control forms the major part of the control scheme.

- The control measure to be taken is based up on the feedback obtained. 

- It helps in achieving both fairness and efficiency. 

What are different policies to prevent congestion at different layers?

- Many times it happens that the demand for the resource is more than what network can offer i.e., its capacity. 

- Too much queuing occurs in the networks leading to a great loss of packets. 

- When the network is in the state of congestive collapse, its throughput drops down to zero whereas the path delay increases by a great margin. 

- The network can recover from this state by following a congestion control scheme.

- A congestion avoidance scheme enables the network to operate in an environment where the throughput is high and the delay is low. 

- In other words, these schemes prevent a computer network from falling prey to the vicious clutches of the network congestion problem. 

- Recovery mechanism is implemented through congestion and the prevention mechanism is implemented through congestion avoidance. 

- The network and the user policies are modeled for the purpose of congestion avoidance. 

- These act like a feedback control system. 

The following are defined as the key components of a general congestion avoidance scheme:

Ø  Congestion detection

Ø  Congestion feedback

Ø  Feedback selector

Ø  Signal filter

Ø  Decision function

Ø  Increase and decrease algorithms

- The problem of congestion control gets more complex when the network is using a connection-less protocol. 

- Avoiding congestion rather than simply controlling it is the main focus. 

- A congestion avoidance scheme is designed after comparing it with a number of other alternative schemes. 

- During the comparison, the algorithm with the right parameter values is selected. 

For doing so few goals have been set with which there is an associated test for verifying whether it is being met by the scheme or not:

Ø  Efficient: If the network is operating at the “knee” point, then it is said to be working efficiently.

Ø  Responsiveness: There is a continuous variation in the configuration and the traffic of the network. Therefore the point for optimal operation also varies continuously.

Ø Minimum oscillation: Only those schemes are preferred that have smaller oscillation amplitude.

Ø Convergence: The scheme should be such that it should bring the network to a point of stable operation for keeping the workload as well as the network configuration stable. The schemes that are able to satisfy this goal are called convergent schemes and the divergent schemes are rejected.

Ø Fairness: This goal aims at providing a fair share of resources to each independent user.

Ø  Robustness: This goal defines the capability of the scheme to work in any random environment. Therefore the schemes that are capable of working only for the deterministic service times are rejected.

Ø  Simplicity: Schemes are accepted in their most simple version.

Ø Low parameter sensitivity: Sensitivity of a scheme is measured with respect to its various parameter values. The scheme which is found to be too much sensitive to a particular parameter, it is rejected.

Ø Information entropy: This goal is about how the feedback information is used. The goal is to get maximum info with the minimum possible feedback.

Ø Dimensionless parameters: A parameter having the dimensions such as the mass, time and the length is taken as a network configuration or speed function. A parameter that has no dimensions has got more applicability.

Ø Configuration independence: The scheme is accepted only if it has been tested for various different configurations.

Congestion avoidance scheme has two main components:

Ø  Network policies: It consists of the following algorithms: feedback filter, feedback selector and congestion detection.

Ø  User policies: It consists of the following algorithms: increase/ decrease algorithm, decision function and signal filter.

These algorithms decide whether the network feedback has to be implemented via packet header field or as source quench messages.

An example of unrealistic expectations in the service industry

This is a true example from around 6 years back when I was working for an IT software solutions provider (the firm did software projects for different customers). This was a decent sized company that had something like 12000 people on the rolls, doing everything from development to testing to requirements analysis, and so on. I was more into the area of a business analyst, translating the requirements document into a form that the developers working on the project would understand.
This was a new project with a new medium sized bank in the Midwest, and the hope was that we would be able to do this project well enough and give them a system that would work so well for them that they would continue with the company and be the start of a long and serious (and profitable) relationship. Sounds good, right ? Well, read on.
Around this time, our company, that was a publicly listed company was getting on just like the other service companies of that time, doing okay, but not generating great figures. Management was getting hit by analysts, and passed on a directive that every project needs to meet the company defined margin. Exceptions only when pleaded before the executive committee, and not otherwise. Implicit was the expectation that anybody who does a project that does not promise enough margin would need to explain the project.
Now, since our project was with a new customer with whom we had high hopes for the future, we could not charge our expected rates; after all, why would the customer then select us ? So, our account manager along with the Vice-President of the unit went ahead and quoted a rate that was atleast 20% lower (getting fewer people assigned to the project than necessary). Guess what ? Pretty soon, the strains and missing people started to show.
Ego also plays a part. For a Vice-President to go before the committee and plead for more money (a reduction in margin) would reflect adversely. The members of the committee, who might be expected to provide an experience of being able to handle these kind of situations and offer some latitude did not do so since they were never offered this project for review. Pretty soon, somebody senior in the team had the bright idea that weekends could be converted into work hours (maybe 1 weekend in 3 could be off), and this idea was implemented with gusto.
You can guess the rest. People from outside the project did not want to join, quality reviews of the project were hesitant because of the many exceptions, and eventually the customer could make out that the quality was not as desired. Project over, account over, and pretty soon the project manager and other senior team members quit and went to other companies.
This was a disaster caused by the management reacting adversely to poor numbers, and unwilling to exercise the due diligence in doing a project (after all, the first criteria for a project should be to make it successful).

How many of you have similar experiences ?