Mark Allman

Bio: Mark Allman is an academic researcher from Institute of Company Secretaries of India. The author has contributed to research in topics: The Internet & TCP tuning. The author has an hindex of 42, co-authored 140 publications receiving 10444 citations. Previous affiliations of Mark Allman include BBN Technologies & University of California, Berkeley.

TCP Congestion Control

Abstract: This document defines TCP's four intertwined congestion control algorithms: slow start, congestion avoidance, fast retransmit, and fast recovery. In addition, the document specifies how TCP should begin transmission after a relatively long idle period, as well as discussing various acknowledgment generation methods.

Computing TCP's Retransmission Timer

Abstract: This document defines the standard algorithm that Transmission Control Protocol (TCP) senders are required to use to compute and manage their retransmission timer. It expands on the discussion in section 4.2.3.1 of RFC 1122 and upgrades the requirement of supporting the algorithm from a SHOULD to a MUST.

On dominant characteristics of residential broadband internet traffic

Abstract: While residential broadband Internet access is popular in many parts of the world, only a few studies have examined the characteristics of such traffic. In this paper we describe observations from monitoring the network activity for more than 20,000 residential DSL customers in an urban area. To ensure privacy, all data is immediately anonymized. We augment the anonymized packet traces with information about DSL-level sessions, IP (re-)assignments, and DSL link bandwidth.Our analysis reveals a number of surprises in terms of the mental models we developed from the measurement literature. For example, we find that HTTP - not peer-to-peer - traffic dominates by a significant margin; that more often than not the home user's immediate ISP connectivity contributes more to the round-trip times the user experiences than the WAN portion of the path; and that the DSL lines are frequently not the bottleneck in bulk-transfer performance.

Increasing TCP's Initial Window

Abstract: This document specifies an optional standard for TCP to increase the permitted initial window from one or two segment(s) to roughly 4K bytes, replacing RFC 2414. It discusses the advantages and disadvantages of the higher initial window, and includes discussion of experiments and simulations showing that the higher initial window does not lead to congestion collapse. Finally, this document provides guidance on implementation issues.

Enhancing TCP Over Satellite Channels using Standard Mechanisms

Abstract: The Transmission Control Protocol (TCP) provides reliable delivery of data across any network path, including network paths containing satellite channels. While TCP works over satellite channels there are several IETF standardized mechanisms that enable TCP to more effectively utilize the available capacity of the network path. This document outlines some of these TCP mitigations. At this time, all mitigations discussed in this document are IETF standards track mechanisms (or are compliant with IETF standards).

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Stream Control Transmission Protocol

Abstract: This document describes the Stream Control Transmission Protocol (SCTP). SCTP is designed to transport PSTN signaling messages over IP networks, but is capable of broader applications.

TCP Congestion Control

Abstract: This document defines TCP's four intertwined congestion control algorithms: slow start, congestion avoidance, fast retransmit, and fast recovery. In addition, the document specifies how TCP should begin transmission after a relatively long idle period, as well as discussing various acknowledgment generation methods.

NOX: towards an operating system for networks

Abstract: As anyone who has operated a large network can attest, enterprise networks are difficult to manage. That they have remained so despite significant commercial and academic efforts suggests the need for a different network management paradigm. Here we turn to operating systems as an instructive example in taming management complexity. In the early days of computing, programs were written in machine languages that had no common abstractions for the underlying physical resources. This made programs hard to write, port, reason about, and debug. Modern operating systems facilitate program development by providing controlled access to high-level abstractions for resources (e.g., memory, storage, communication) and information (e.g., files, directories). These abstractions enable programs to carry out complicated tasks safely and efficiently on a wide variety of computing hardware. In contrast, networks are managed through low-level configuration of individual components. Moreover, these configurations often depend on the underlying network; for example, blocking a user’s access with an ACL entry requires knowing the user’s current IP address. More complicated tasks require more extensive network knowledge; forcing guest users’ port 80 traffic to traverse an HTTP proxy requires knowing the current network topology and the location of each guest. In this way, an enterprise network resembles a computer without an operating system, with network-dependent component configuration playing the role of hardware-dependent machine-language programming. What we clearly need is an “operating system” for networks, one that provides a uniform and centralized programmatic interface to the entire network. Analogous to the read and write access to various resources provided by computer operating systems, a network operating system provides the ability to observe and control a network. A network operating system does not manage the network itself; it merely provides a programmatic interface. Applications implemented on top of the network operating system perform the actual management tasks. The programmatic interface should be general enough to support a broad spectrum of network management applications. Such a network operating system represents two major conceptual departures from the status quo. First, the network operating system presents programs with a centralized programming model; programs are written as if the entire network were present on a single machine (i.e., one would use Dijkstra to compute shortest paths, not Bellman-Ford). This requires (as in [3, 8, 14] and elsewhere) centralizing network state. Second, programs are written in terms of high-level abstractions (e.g., user and host names), not low-level configuration parameters (e.g., IP and MAC addresses). This allows management directives to be enforced independent of the underlying network topology, but it requires that the network operating system carefully maintain the bindings (i.e., mappings) between these abstractions and the low-level configurations. Thus, a network operating system allows management applications to be written as centralized programs over highlevel names as opposed to the distributed algorithms over low-level addresses we are forced to use today. While clearly a desirable goal, achieving this transformation from distributed algorithms to centralized programming presents significant technical challenges, and the question we pose here is: Can one build a network operating system at significant scale?