TCP smart framing: a segmentation algorithm to reduce TCP latency

Abstract

TCP Smart Framing, or TCP-SF for short, enables the Fast Retransmit/Recovery algorithms even when the congestion window is small. Without modifying the TCP congestion control based on the additive-increase/multiplicative-decrease paradigm, TCP-SF adopts a novel segmentation algorithm: while Classic TCP always tries to send full-sized segments, a TCP-SF source adopts a more flexible segmentation algorithm to try and always have a number of in-flight segments larger than 3 so as to enable Fast Recovery. We motivate this choice by real traffic measurements, which indicate that today's traffic is populated by short-lived flows, whose only means to recover from a packet loss is by triggering a Retransmission Timeout. The key idea of TCP-SF can be implemented on top of any TCP flavor, from Tahoe to SACK, and requires modifications to the server TCP stack only, and can be easily coupled with recent TCP enhancements.The performance of the proposed TCP modification were studied by means of simulations, live measurements and an analytical model. In addition, the analytical model we have devised has a general scope, making it a valid tool for TCP performance evaluation in the small window region. Improvements are remarkable under several buffer management schemes, and maximized by byte-oriented schemes.

Passive Identification and Analysis of TCP Anomalies

“…The amount of RTO accounts for the main part of anomalous events, being almost 70%. This is not surprising, since RTO events correspond to the most likely reaction of TCP to segment losses: indeed, Fast Retransmit is not commonly triggered, since only long TCP flows, which represent a small portion of the total traffic, can actually use this feature [9]. Packet reordering is also present in a significant amount.…”

Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow

“…The first variation of B(ϕ(x)) (with respect to ϕ(x)) can be easily shown to be given by (7) Differentiating (3) and (4) with respect to ϕ(x), one obtains (8) and (9) where δ(·) is the delta function, and A − and A + are the areas of Ω − and Ω + given by ∫ Ω χ − (x) dx and ∫ Ω χ + (x) dx, respectively. By substituting (8) and (9) into (7) and combining the corresponding terms, one can arrive at (10) where (11) Assuming the same kernel K(z) is used for computing the last two terms in (11), i.e., K(z) = K − (z) = K + (z), the latter can be further simplified to the following form: (12) where (13) Finally, introducing an artificial time parameter t, the gradient flow of that minimizes (6) is given by (14) where the subscript denotes the corresponding partial derivative, and V(x) is defined as given by either (11) or (12).…”

“…The latter is usually distinguished on a semantic basis and assumed to be either an object or a background. Thus, for example, the object may be associated with a diseased organ in medical imaging [1], [2], an intruder in surveillance video [3], [4], a moving part of a machine in robotics [5], [6], a maneuvering vehicle in traffic control [7], [8], or a target in navigation and military applications [9], [10].…”

Upgrading Mice to Elephants: Effects and End-Point Solutions

“…Mellia et al [28] propose a segmentation algorithm, TCP Smart Framing (TCP-SF) to reduce TCP's latency. The basic notion is to split data into smaller segments when TCP's congestion window is less than four maximum segment sizes (MSS).…”