Analysis of the Increase and Decrease Algorithms for Congestion Avoidance in Computer Networks

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## Analysis of the Increase and Decrease Algorithms for Congestion Avoidance in Computer Networks

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**Analysis of the Increase and Decrease Algorithms for**Congestion Avoidance in Computer Networks Dah-Ming Chiu and Raj Jain Presented by Yao Zhao**Motivation (1)**• Internet is heterogeneous • Different bandwidth of links • Different load from users • Congestion control • Help improve performance after congestion has occurred • Congestion avoidance • Keep the network operating off the congestion**Motivation (2)**• Fig. 1. Network performance as a function of the load. Broken curves indicate performance with deterministic service and interarrival times**Relate Works**• Centralized algorithm • Information flows to the resource managers and the decision of how to allocate the resource is made at the resource [Sanders86] • Decentralized algorithms • Decisions are made by users while the resources feed information regarding current resource usage [Jaffe81, Gafni82, Mosely84] • Binary feedback signal and linear control • Synchronized model • What are all the possible solutions that converge to efficient and fair states**Linear Control (1)**• 4 examples of linear control functions • Multiplicative Increase/Multiplicative Decrease • Additive Increase/Additive Decrease • Additive Increase/Multiplicative Decrease • Additive Increase/ Additive Decrease**Linear Control (2)**• Multiplicative Increase/Multiplicative Decrease • Additive Increase/Additive Decrease • Additive Increase/Multiplicative Decrease • Multiplicative Increase/ Additive Decrease**Criteria for Selecting Controls**• Efficiency • Closeness of the total load on the resource to the knee point • Fairness • Users have the equal share of bandwidth • Distributedness • Knowledge of the state of the system • Convergence • The speed with which the system approaches the goal state from any starting state**Responsiveness and Smoothness of Binary Feedback System**• Equlibrium with oscillates around the optimal state**Example of Multiplicative Increase/ Multiplicative Decrease**Function**Example of Additive Increase/ Multiplicative Decrease**Function**Convergence to Efficiency**• Negative feedback • So • Or**c>0**Convergence to Fairness (1) where c=a/b (6)**Convergence to Fairness (2)**• c>0 implies: • Furthermore, combined with (3) we have:**Distributedness**• Having no knowledge other than the feedback y(t) • Each user tries to satisfy the negative feedback condition by itself • Implies (10) to be**Important Results**• Proposition 1: In order to satisfy the requirements of distributed convergence to efficiency and fairness without truncation, the linear increase policy should always have an additive component, and optionally it may have a multiplicative component with the coefficient no less than one. • Proposition 2: For the linear controls with truncation, the increase and decrease policies can each have both additive and multiplicative components, satisfying the constrains in Equations (16)**Optimizing the Control Schemes**• Optimal convergence to Efficiency • Tradeoff of time to convergent to efficiency te, with the oscillation size, se. • Optimal convergence to Fairness**Optimal convergence to Efficiency**• Given initial state X(0), the time to reach Xgoal is:**Optimal convergence to Fairness**• Equation (7) shows faireness function is monotonically increasing function of c=a/b. • So larger values of a and smaller values b give quicker convergence to fairness. • In strict linear control, aD=0 => fairness remains the same at every decrease step • For increase, smaller bI results in quicker convergence to fairness => bI =1 to get the quickest convergence to fairness • Proposition 3: For both feasibility and optimal convergence to fairness, the increase policy should be additive and the decrease policy should be multiplicative.**Practical Considerations**• Non-linear controls • Delay feedback • Utility of increased bits of feedback • Guess the current number of users n • Impact of asynchronous operation**Conclusion**• We examined the user increase/decrease policies under the constrain of binary signal feedback • We formulated a set of conditions that any increase/decrease policy should satisfy to ensure convergence to efficiency and fair state in a distributed manner • We show the decrease must be multiplicative to ensure that at every step the fairness either increases or stays the same • We explain the conditions using a vector representation • We show that additive increase with multiplicative decrease is the optimal policy for convergence to fairness