On the Performance and Robustness of Managing Reliable Transport Connections

We revisit the problem of connection management for reliable transport. At one extreme, a pure soft-state (SS) approach (as in Delta-t [9]) safely removes the state of a connection at the sender and receiver once the state timers expire without the need for explicit removal messages. And new connections are established without an explicit handshaking phase. On the other hand, a hybrid hard-state/soft-state (HS+SS) approach (as in TCP) uses both explicit handshaking as well as timer-based management of the connection’s state. In this paper, we consider the worst-case scenario of reliable single-message communication, and develop a common analytical model that can be instantiated to capture either the SS approach or the HS+SS approach. We compare the two approaches in terms of goodput, message and state overhead. We also use simulations to compare against other approaches, and evaluate them in terms of correctness (with respect to data loss and duplication) and robustness to bad network conditions (high message loss rate and variable channel delays). Our results show that the SS approach is more robust, and has lower message overhead. On the other hand, SS requires more memory to keep connection states, which reduces goodput. Given memories are getting bigger and cheaper, SS presents the best choice over bandwidth-constrained, error-prone networks.

Using Markets and Spam to Combat Malware

We propose an economic mechanism to reduce the incidence of malware that delivers spam. Earlier research proposed attention markets as a solution for unwanted messages, and showed they could provide more net benefit than alternatives such as filtering and taxes. Because it uses a currency system, Attention Bonds faces a challenge. Zombies, botnets, and various forms of malware might steal valuable currency instead of stealing unused CPU cycles. We resolve this problem by taking advantage of the fact that the spam-bot problem has been reduced to financial fraud. As such, the large body of existing work in that realm can be brought to bear. By drawing an analogy between sending and spending, we show how a market mechanism can detect and prevent spam malware. We prove that by using a currency (i) each instance of spam increases the probability of detecting infections, and (ii) the value of eradicating infections can justify insuring users against fraud. This approach attacks spam at the source, a virtue missing from filters that attack spam at the destination. Additionally, the exchange of currency provides signals of interest that can improve the targeting of ads. ISPs benefit from data management services and consumers benefit from the higher average value of messages they receive. We explore these and other secondary effects of attention markets, and find them to offer, on the whole, attractive economic benefits for all – including consumers, advertisers, and the ISPs.

CSR: Constrained Selfish Routing in Ad-hoc Networks

Routing protocols for ad-hoc networks assume that the nodes forming the network are either under a single authority, or else that they would be altruistically forwarding data for other nodes with no expectation of a return. These assumptions are unrealistic since in ad-hoc networks, nodes are likely to be autonomous and rational (selfish), and thus unwilling to help unless they have an incentive to do so. Providing such incentives is an important aspect that should be considered when designing ad-hoc routing protocols. In this paper, we propose a dynamic, decentralized routing protocol for ad-hoc networks that provides incentives in the form of payments to intermediate nodes used to forward data for others. In our Constrained Selfish Routing (CSR) protocol, game-theoretic approaches are used to calculate payments (incentives) that ensure both the truthfulness of participating nodes and the fairness of the CSR protocol. We show through simulations that CSR is an energy efficient protocol and that it provides lower communication overhead in the best and average cases compared to existing approaches.

On the Cost of Supporting Multihoming and Mobility

As the Internet has evolved and grown, an increasing number of nodes (hosts or autonomous systems) have become multihomed, i.e., a node is connected to more than one network. Mobility can be viewed as a special case of multihoming—as a node moves, it unsubscribes from one network and subscribes to another, which is akin to one interface becoming inactive and another active. The current Internet architecture has been facing significant challenges in effectively dealing with multihoming (and consequently mobility). The Recursive INternet Architecture (RINA) [1] was recently proposed as a clean-slate solution to the current problems of the Internet. In this paper, we perform an average-case cost analysis to compare the multihoming / mobility support of RINA, against that of other approaches such as LISP and MobileIP. We also validate our analysis using trace-driven simulation.

Assessing the Security of a Clean-Slate Internet Architecture

The TCP/IP architecture was originally designed without taking security measures into consideration. Over the years, it has been subjected to many attacks, which has led to many patches to counter them. Our investigations into the fundamental principles of networking have shown that carefully following an abstract model of Interprocess Communication (IPC) addresses many problems [1]. Guided by this IPC principle, we designed a clean-slate Recursive INternet Architecture (RINA) [2]. In this paper, we show how, without the aid of cryptographic techniques, the bare-bones architecture of RINA can resist most of the security attacks faced by TCP/IP. We also show how hard it is for an intruder to compromise RINA. Then, we show how RINA inherently supports security policies in a more manageable, on-demand basis, in contrast to the rigid, piecemeal approach of TCP/IP.

Is a Detector Only Good for Detection?

A common design of an object recognition system has two steps, a detection step followed by a foreground within-class classification step. For example, consider face detection by a boosted cascade of detectors followed by face ID recognition via one-vs-all (OVA) classifiers. Another example is human detection followed by pose recognition. Although the detection step can be quite fast, the foreground within-class classification process can be slow and becomes a bottleneck. In this work, we formulate a filter-and-refine scheme, where the binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the FRGC V2 data set, hand shape detection and parameter estimation on a hand data set and vehicle detection and view angle estimation on a multi-view vehicle data set. On all data sets, our approach has comparable accuracy and is at least five times faster than the brute force approach.

Trade & Cap: A Customer-Managed, Market-Based System for Trading Bandwidth Allowances at a Shared Link

We propose Trade & Cap (T&C), an economics-inspired mechanism that incentivizes users to voluntarily coordinate their consumption of the bandwidth of a shared resource (e.g., a DSLAM link) so as to converge on what they perceive to be an equitable allocation, while ensuring efficient resource utilization. Under T&C, rather than acting as an arbiter, an Internet Service Provider (ISP) acts as an enforcer of what the community of rational users sharing the resource decides is a fair allocation of that resource. Our T&C mechanism proceeds in two phases. In the first, software agents acting on behalf of users engage in a strategic trading game in which each user agent selfishly chooses bandwidth slots to reserve in support of primary, interactive network usage activities. In the second phase, each user is allowed to acquire additional bandwidth slots in support of presumed open-ended need for fluid bandwidth, catering to secondary applications. The acquisition of this fluid bandwidth is subject to the remaining "buying power" of each user and by prevalent "market prices" – both of which are determined by the results of the trading phase and a desirable aggregate cap on link utilization. We present analytical results that establish the underpinnings of our T&C mechanism, including game-theoretic results pertaining to the trading phase, and pricing of fluid bandwidth allocation pertaining to the capping phase. Using real network traces, we present extensive experimental results that demonstrate the benefits of our scheme, which we also show to be practical by highlighting the salient features of an efficient implementation architecture.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Safe Compositional Network Sketches: The Formal Framework

NetSketch is a tool for the specification of constrained-flow applications and the certification of desirable safety properties imposed thereon. NetSketch is conceived to assist system integrators in two types of activities: modeling and design. As a modeling tool, it enables the abstraction of an existing system while retaining sufficient information about it to carry out future analysis of safety properties. As a design tool, NetSketch enables the exploration of alternative safe designs as well as the identification of minimal requirements for outsourced subsystems. NetSketch embodies a lightweight formal verification philosophy, whereby the power (but not the heavy machinery) of a rigorous formalism is made accessible to users via a friendly interface. NetSketch does so by exposing tradeoffs between exactness of analysis and scalability, and by combining traditional whole-system analysis with a more flexible compositional analysis. The compositional analysis is based on a strongly-typed Domain-Specific Language (DSL) for describing and reasoning about constrained-flow networks at various levels of sketchiness along with invariants that need to be enforced thereupon. In this paper, we define the formal system underlying the operation of NetSketch, in particular the DSL behind NetSketch's user-interface when used in "sketch mode", and prove its soundness relative to appropriately-defined notions of validity. In a companion paper [6], we overview NetSketch, highlight its salient features, and illustrate how it could be used in two applications: the management/shaping of traffic flows in a vehicular network (as a proxy for CPS applications) and in a streaming media network (as a proxy for Internet applications).

Verification with Natural Contexts: Soundness of Safe Compositional Network Sketches

In research areas involving mathematical rigor, there are numerous benefits to adopting a formal representation of models and arguments: reusability, automatic evaluation of examples, and verification of consistency and correctness. However, accessibility has not been a priority in the design of formal verification tools that can provide these benefits. In earlier work [30] we attempt to address this broad problem by proposing several specific design criteria organized around the notion of a natural context: the sphere of awareness a working human user maintains of the relevant constructs, arguments, experiences, and background materials necessary to accomplish the task at hand. In this report we evaluate our proposed design criteria by utilizing within the context of novel research a formal reasoning system that is designed according to these criteria. In particular, we consider how the design and capabilities of the formal reasoning system that we employ influence, aid, or hinder our ability to accomplish a formal reasoning task – the assembly of a machine-verifiable proof pertaining to the NetSketch formalism. NetSketch is a tool for the specification of constrained-flow applications and the certification of desirable safety properties imposed thereon. NetSketch is conceived to assist system integrators in two types of activities: modeling and design. It provides capabilities for compositional analysis based on a strongly-typed domain-specific language (DSL) for describing and reasoning about constrained-flow networks and invariants that need to be enforced thereupon. In a companion paper [13] we overview NetSketch, highlight its salient features, and illustrate how it could be used in actual applications. In this paper, we define using a machine-readable syntax major parts of the formal system underlying the operation of NetSketch, along with its semantics and a corresponding notion of validity. We then provide a proof of soundness for the formalism that can be partially verified using a lightweight formal reasoning system that simulates natural contexts. A traditional presentation of these definitions and arguments can be found in the full report on the NetSketch formalism [12].

Adaptive Weighing Designs for Keyword Value Computation

Attributing a dollar value to a keyword is an essential part of running any profitable search engine advertising campaign. When an advertiser has complete control over the interaction with and monetization of each user arriving on a given keyword, the value of that term can be accurately tracked. However, in many instances, the advertiser may monetize arrivals indirectly through one or more third parties. In such cases, it is typical for the third party to provide only coarse-grained reporting: rather than report each monetization event, users are aggregated into larger channels and the third party reports aggregate information such as total daily revenue for each channel. Examples of third parties that use channels include Amazon and Google AdSense. In such scenarios, the number of channels is generally much smaller than the number of keywords whose value per click (VPC) we wish to learn. However, the advertiser has flexibility as to how to assign keywords to channels over time. We introduce the channelization problem: how do we adaptively assign keywords to channels over the course of multiple days to quickly obtain accurate VPC estimates of all keywords? We relate this problem to classical results in weighing design, devise new adaptive algorithms for this problem, and quantify the performance of these algorithms experimentally. Our results demonstrate that adaptive weighing designs that exploit statistics of term frequency, variability in VPCs across keywords, and flexible channel assignments over time provide the best estimators of keyword VPCs.

Reliable Cellular Automata with Self-Organization

In a probabilistic cellular automaton in which all local transitions have positive probability, the problem of keeping a bit of information for more than a constant number of steps is nontrivial, even in an infinite automaton. Still, there is a solution in 2 dimensions, and this solution can be used to construct a simple 3-dimensional discrete-time universal fault-tolerant cellular automaton. This technique does not help much to solve the following problems: remembering a bit of information in 1 dimension; computing in dimensions lower than 3; computing in any dimension with non-synchronized transitions. Our more complex technique organizes the cells in blocks that perform a reliable simulation of a second (generalized) cellular automaton. The cells of the latter automaton are also organized in blocks, simulating even more reliably a third automaton, etc. Since all this (a possibly infinite hierarchy) is organized in "software", it must be under repair all the time from damage caused by errors. A large part of the problem is essentially self-stabilization recovering from a mess of arbitrary-size and content caused by the faults. The present paper constructs an asynchronous one-dimensional fault-tolerant cellular automaton, with the further feature of "self-organization". The latter means that unless a large amount of input information must be given, the initial configuration can be chosen to be periodical with a small period.

Distributed Packet Rewriting and its Application to Scalable Server Architectures

To construct high performance Web servers, system builders are increasingly turning to distributed designs. An important challenge that arises in distributed Web servers is the need to direct incoming connections to individual hosts. Previous methods for connection routing have employed a centralized node which handles all incoming requests. In contrast, we propose a distributed approach, called Distributed Packet Rewriting (DPR), in which all hosts of the distributed system participate in connection routing. We argue that this approach promises better scalability and fault-tolerance than the centralized approach. We describe our implementation of four variants of DPR and compare their performance. We show that DPR provides performance comparable to centralized alternatives, measured in terms of throughput and delay under the SPECweb96 benchmark. Finally, we argue that DPR is particularly attractive both for small scale systems and for systems following the emerging trend toward increasingly intelligent I/O subsystems.

Combining Textual and Visual Cues for Content-based Image Retrieval on the World Wide Web

Some WWW image engines allow the user to form a query in terms of text keywords. To build the image index, keywords are extracted heuristically from HTML documents containing each image, and/or from the image URL and file headers. Unfortunately, text-based image engines have merely retro-fitted standard SQL database query methods, and it is difficult to include images cues within such a framework. On the other hand, visual statistics (e.g., color histograms) are often insufficient for helping users find desired images in a vast WWW index. By truly unifying textual and visual statistics, one would expect to get better results than either used separately. In this paper, we propose an approach that allows the combination of visual statistics with textual statistics in the vector space representation commonly used in query by image content systems. Text statistics are captured in vector form using latent semantic indexing (LSI). The LSI index for an HTML document is then associated with each of the images contained therein. Visual statistics (e.g., color, orientedness) are also computed for each image. The LSI and visual statistic vectors are then combined into a single index vector that can be used for content-based search of the resulting image database. By using an integrated approach, we are able to take advantage of possible statistical couplings between the topic of the document (latent semantic content) and the contents of images (visual statistics). This allows improved performance in conducting content-based search. This approach has been implemented in a WWW image search engine prototype.

Preserving Bandwidth Using A Lazy Packet Discard Policy in ATM Networks

A number of recent studies have pointed out that TCP's performance over ATM networks tends to suffer, especially under congestion and switch buffer limitations. Switch-level enhancements and link-level flow control have been proposed to improve TCP's performance in ATM networks. Selective Cell Discard (SCD) and Early Packet Discard (EPD) ensure that partial packets are discarded from the network "as early as possible", thus reducing wasted bandwidth. While such techniques improve the achievable throughput, their effectiveness tends to degrade in multi-hop networks. In this paper, we introduce Lazy Packet Discard (LPD), an AAL-level enhancement that improves effective throughput, reduces response time, and minimizes wasted bandwidth for TCP/IP over ATM. In contrast to the SCD and EPD policies, LPD delays as much as possible the removal from the network of cells belonging to a partially communicated packet. We outline the implementation of LPD and show the performance advantage of TCP/LPD, compared to plain TCP and TCP/EPD through analysis and simulations.