This paper presents DONet, a Data-driven Overlay Network for live media streaming. The core operations in DONet are very simple: every node periodically exchanges data availability information with a set of partners, and retrieves unavailable data from one or more partners, or supplies available data to partners. We emphasize three salient features of this data-driven design: 1) easy to implement, as it does not have to construct and maintain a complex global structure; 2) efficient, as data forwarding is dynamically determined according to data availability while not restricted by specific directions; and 3) robust and resilient, as the partnerships enable adaptive and quick switching among multi-suppliers. We show through analysis that DONet is scalable with bounded delay. We also address a set of practical challenges for realizing DONet, and propose an efficient member- and partnership management algorithm, together with an intelligent scheduling algorithm that achieves real-time and continuous distribution of streaming contents.

This document proposes HighSpeed TCP, a modification to TCP's congestion control mechanism for use with TCP connections with large congestion windows. The congestion control mechanisms of the current Standard TCP constrains the congestion windows that can be achieved by TCP in realistic environments. For example, for a Standard TCP connection with 1500-byte packets and a 100 ms round-trip time, achieving a steady-state throughput of 10 Gbps would require an average congestion window of 83,333 segments, and a packet drop rate of at most one congestion event every 5,000,000,000 packets (or equivalently, at most one congestion event every 1 2/3 hours). This is widely acknowledged as an unrealistic constraint. To address this limitation of TCP, this document proposes HighSpeed TCP, and solicits experimentation and feedback from the wider community.

This paper introduces Wave and Equation Based Rate Control (WEBRC), the first multiple rate multicast congestion control protocol to be equation based. The equation-based approach enforces fairness to TCP with the benefit that fluctuations in the flow rate are small in comparison to TCP.

Resource allocation and accountability keep reappearing on every list of requirements for the Internet architecture. The reason we never resolve these issues is a broken idea of what the problem is. The applied research and standards communities are using completely unrealistic and impractical fairness criteria. The resulting mechanisms don’t even allocate the right thing and they don’t allocate it between the right entities. We explain as bluntly as we can that thinking about fairness mechanisms like TCP in terms of sharing out flow rates has no intellectual heritage from any concept of fairness in philosophy or social science, or indeed real life. Comparing flow rates should never again be used for claims of fairness in production networks. Instead, we should judge fairness mechanisms on how they share out the ‘cost ’ of each user’s actions on others.

Internet applications currently have a choice between stream and datagram transport abstractions. Datagrams efficiently support small transactions and streams are suited for longrunning conversations, but neither abstraction adequately supports applications like HTTP that exhibit a mixture of transaction sizes, or applications like FTP and SIP that use multiple transport instances. Structured Stream Transport (SST) enhances the traditional stream abstraction with a hierarchical hereditary structure, allowing applications to create lightweight child streams from any existing stream. Unlike TCP streams, these lightweight streams incur neither 3-way handshaking delays on startup nor TIME-WAIT periods on close. Each stream offers independent data transfer and flow control, allowing different transactions to proceed in parallel without head-of-line blocking, but all streams share one congestion control context. SST supports both reliable and best-effort delivery in a way that semantically unifies datagrams with streams and solves the classic “large datagram ” problem, where a datagram’s loss probability increases exponentially with fragment count. Finally, an application can prioritize its streams relative to each other and adjust priorities dynamically through out-of-band signaling. A user-space prototype shows that SST is TCP-friendly to within 2%, and performs comparably to a user-space TCP and to within 10 % of kernel TCP on a WiFi network.

this paper, we evaluate performance of TFRC during handovers between GPRS, WLAN, and UMTS.We measure behavior of TFRC and TCP flows in a testbed implementing vertical handovers using Mobile IP. To verify our testbed measurements and to study the effect of changes in path characteristics, we use an ideal handover model in the ns-2 simulator [40]. Essentially, an ideal handover is represented by a step change in the bottleneck link bandwidth, latency, and buffer size, as if a smooth handover with packet forwarding were implemented [7]. Throughput, aggressiveness, responsiveness, and fairness of TFRC are evaluated. We show that there are significant problems with using TFRC in the presence of vertical handovers. In particular, over a fast link TFRC receives only a fraction of TCP throughput, while over a slow link TFRC can starve concurrent TCP flows after a handover. Two proposals based on overbuffering and an explicit handover notification are demonstrated to be effective solutions to these problems

We study limitations of an equation-based congestion control protocol, called TFRC (TCP Friendly Rate Control). It examines how the three main factors that determine TFRC throughput, namely, the TCP friendly equation, loss event rate estimation and delay estimation, can influence the longterm throughput imbalance between TFRC and TCP. Especially, we show that different sending rates of competing flows cause these flows to experience different loss event rates. There are several fundamental reasons why TFRC and TCP flows have different average sending rates, from the first place. Earlier work shows that the convexity of the TCP friendly equation used in TFRC causes the sending rate difference. We report two additional reasons in this paper: (1) the convexity of 1/x where x is a loss event period and (2) different RTO (retransmission timeout period) estimations of TCP and TFRC. These factors can be the reasons for TCP and TFRC to experience initially different sending rates. But we find that the loss event rate difference due to the differing sending rates greatly amplifies the initial throughput difference; in some extreme cases, TFRC uses around 20 times more, or sometimes 10 times less, bandwidth than TCP.

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Network protocols are typically designed and tested individually. In practice, however, applications use multiple protocols concurrently. This discrepancy can lead to failures from unanticipated interactions between protocols. In this paper, we argue that sensor network communication stacks should have an isolation layer, whose purpose is to make each protocol’s perception of the wireless channel independent of what other protocols are running. We identify two key mechanisms the isolation layer must provide: shared collision avoidance and fair channel allocation. We present an example design of an isolation layer that builds on the existing algorithms of grant-to-send and fair queueing. However, the complexities of wireless make these mechanisms insufficient by themselves. We therefore propose two new mechanisms that address these limitations: channel decay and fair cancellation. Incorporating these new mechanisms reduces the increase in end-to-end delivery cost associated with concurrently operating two protocols by more than 60%. The isolation layer improves median protocol fairness from 0.52 to 0.96 in Jain’s fairness index. Together, these results show that using an isolation layer makes protocols more efficient and robust.

Equation-based congestion control has been a promising alternative to TCP for real-time multimedia streaming over the Internet. However, its behavior remains unknown in the mobile ad hoc wireless network (MANET) domain. In this paper, we study the behavior of TFRC (TCP Friendly Rate Control [1], [2]) over a wide range of MANET scenarios, in terms of throughput fairness and smoothness. Our result shows that while TFRC is able to maintain throughput smoothness in MANET, it obtains less throughput than the competing TCP flows (i.e., being conservative). We analyze several factors contributing to TFRC's conservative behavior in MANET, many of which are inherent to the MANET network. We also show that TFRC's conservative behavior cannot be completely corrected by tuning its loss event interval estimator. Our study shows the limitations of applying TFRC to the MANET domain, and reveals some fundamental difficulties in doing so. At the same