In this section, we only identified some of the design issues for a layered framework and sketched some guidelines for add and drop schemes that works with a simple periodic loss pattern. In a real network, loss pattern varies with time. Thus, the server should monitor the pattern and predict the near future based on the recent past. Once the time for the upcoming congestion is estimated, the server invokes the primary mechanism that we described in the previous section to drop those layers that can not recover. If an unexpected back off occurs, the server should invoke a secondary mechanism for add and drop and try to adjust its behavior promptly.
Toward this end, the following issues are still open and needs to be studies: 1. What is the time scale for averaging and adaptation?
2. What is the appropriate moving average algorithm that captures long-term trend of network’s behavior without oscillating?
3. How does the server predict network’s behavior from the moving averaged history? 4. When does a server invokes a secondary mechanism for dropping?
5. What is the secondary mechanism for dropping?
7
Contribution and Future Work
The goal of our proposed architecture is to make realtime playback applications good network citizens. We attempt to provide a practical solution for a large group of playback applications to enable large scale deployment of these applications in the Internet in the absence of reservation or differentiated services.
This document presents our initial work to achieve this goal. In this section we present our contribu- tions and describe our proposed future work
7.1
Contributions
As part of our initial attempt to design and evaluate an architecture for realtime playback applications, we have:
Designed and developed the rate adaptation protocol(RAP) to be well-behaved and achieve inter- protocol fairness in general and exhibit TCP-friendly behavior in particular. We have only em- ulated those mechanisms from TCP’s congestion control that are known as strength of TCP and avoid from those issues that might cause performance problems.
Devised and evaluated a fine grain rate adaptation mechanism to emulate TCP’s ack-clocking property and improve stability of the protocol. The fine grain adaptation mechanism only fine tune the rate that is coarsely adjusted by the RAP protocol.
Presented a methodology for simulations to limit the inter-dependency among different variables. This methodology allows us to recognize an effect that is caused by TCP’s performance problem from those phenomena that are due to coexisting with RAP flows.
Evaluated various aspects of the RAP protocol to examine its interaction with TCP through exten- sive simulations. Although achieving TCP-friendliness over a wide range of network parameters is extremely challenging, RAP reasonably fulfill this goal. We have conducted detailed simulation and explore a large portion of parameter space to ensure that an observed behavior is not an artifact of simulation parameters. Our results show that the fine grain adaptation extends inter-protocol fairness to a wider range. Divergence of TCP’s congestion control from the AIMD algorithm is often the main cause for the imposed unfairness against TCP in special cases. This problem is pronounced more clearly with Reno and Tahoe while it has a more limited impact on Sack. Reno and Tahoe while it has a more limited impact on Sack. Toward this end, we observed that the big- ger TCP’s congestion window is, the closer it follows the AIMD algorithm. We have studied the
impact of burstiness on fairness. Our results reveals that protocol’s burstiness affects the obtained bandwidth but the effect varies among different simulations. We have examined the interaction between RAP and RED gateways in the presence of TCP traffic. We found out that RED gateways with a proper configuration, can result in an ideal inter-protocol sharing.
Introduced Quality adaptation as a new dimension for error control in the context of realtime applications. We also sketched a layered frame work for quality adaptation and identified some of the design issues as a foundation for our future work.
7.2
Remaining Thesis
Our future work focuses on three remaining components of our proposed architecture; 1. Quality adap- tation, 2. Error control, 3. Buffering Mechanism
7.2.1 Quality Adaptation
We plan to study different aspects of quality adaptation through simulations using ns. We will particu- larly study the following issues:
Adding scheme: We plan to further investigate on a probing approach that not only refills buffers
of the lower layers but also explores the availability of bandwidth before adding a new layer. We also plan to quantify the buffer requirement for the probing approach.
Dropping scheme: We plan to further study the primary mechanism for dropping and its inter-
action with inter-layer bandwidth allocation. Furthermore, we will investigate on the necessity of a secondary mechanism for dropping to be invoked after an unexpected congestion event and potential adjustments on inter-layer bandwidth allocations in these scenarios.
In addition, we will examine different moving average algorithms for measurement of network behavior to capture long-term trend in aggregate traffic and predict the time for an upcoming congestion.
Another task is to specify the appropriate time scale for measurements and adjustments. We also plan to study the behavior of the network traffic to extract some statistics and pattern. This information will lead us to identify the appropriate time scale for the adjustments as well as typical unexpected congestion events as the worst case scenario to target for recovery. Furthermore, we will use this information to specify when the secondary mechanism must be invoked and how it
must react. In this context, we will address the stability and smoothness of add and drop scheme with different averaging technics.
As a starting point, we use a random function as a predictor for next congestion. The result with the random predictor will serve as a lower bound for efficiency. We also will calculate an optimal add and drop plan for a given loss pattern by post processing. The optimal scenario specifies an upper bound for efficiency. We can evaluate our add and drop schemes from its distance to the optimal solution.
Inter-layer Bandwidth Allocation: Our investigations on drop scheme would affect the inter-layer
bandwidth allocation scheme. Particularly, a hybrid approach of parallel and sequential recovery is investigated. For example, the base layer may utilize the entire bandwidth to recover as in sequential recovery and then the rest of the layers recover in a parallel fashion. This approach provides a higher level of protection for the base layer. We will also investigate buffer requirement for different recovery mechanisms.
7.2.2 Error Control
Our future work on error control will mainly focus on retransmission. We will quantify buffer require- ment for retransmission as well as bandwidth allocation and timing issues for retransmission in our sim- ulations. We will study other repair approach for error recovery as a potential complementary schemes for retransmission.
7.2.3 Buffering Scheme
The buffering scheme is mainly influenced by the add scheme. We plan to evaluate buffer requirement for protection of lower layers and its implications on add and drop schemes. We will also investigate adaptive buffering schemes where the amount of protection buffers will adaptively adjusted with network behavior.
7.3
Expected Contribution
Evaluation of the overall behavior of the proposed end-to-end architecture for realtime applications over the best effort network.
Design and evaluation of Quality adaptation. More specifically, design and evaluation of add and drop schemes that leads to a smooth display of realtime stream.
– Interaction between Error control and Quality adaptation mechanisms within the regulated
rate by the RAP module.
– Quantifying buffer requirements for add, drop schemes as well as protections
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