Jonathan K. Shapiro

Incentives to promote availability in peer-to-peer anonymity systems

Peer-to-peer (P2P) anonymous communication systems are vulnerable to free-riders, peers that use the system while providing little or no service to others and whose presence limits the strength of anonymity as well as the efficiency of the system. Free-riding can be addressed by building explicit incentive mechanisms into system protocols to promote two distinct aspects of cooperation among peers-compliance with the protocol specification and the availability of peers to serve others. In this paper we study the use of payments to implement an incentive mechanism that attaches a real monetary cost to low availability. Through a game theoretic analysis, we evaluate the effectiveness of such an incentive, finding that peer availability can be significantly increased through the introduction of payments under many conditions. We also demonstrate how a payment-based incentive that preserves anonymity can be implemented and integrated with a popular class of P2P anonymity systems.

Optimization-based congestion control for multicast communications

This article outlines an approach for multicast congestion control based on an economic model that has been successfully applied to unicast congestion control. In this model, congestion signals are interpreted as prices and congestion-controlled sessions as utility maximizing agents. A naive extension of the unicast model fails to achieve a reasonable balance between providing the incentives necessary to promote the use of multicast and ensuring that multicast sessions do not interact too aggressively with unicast sessions. We extend the model by introducing a rational definition of multicast utility. The revised model provides a basis for multicast congestion control protocols that provide incentives to use multicast but are necessarily unfair to unicast traffic. We show, however, that the degree of unfairness can be controlled by appropriately setting a design parameter with a limiting case of strict fairness.

Estimation of congestion price using probabilistic packet marking

One key component of recent pricing-based congestion control schemes is an algorithm for probabilistically setting the Explicit Congestion Notification bit at routers so that a receiver can estimate the sum of link congestion prices along a path. We consider two such algorithms - a well-known algorithm called Random Exponential Marking (REM) and a novel algorithm called Random Additive Marking (RAM). We show that if link prices are unbounded, a class of REM-like algorithms are the only ones possible. Unfortunately, REM computes a biased estimate of total price and requires setting a parameter for which no uniformly good choice exists in a network setting. However, we show that if prices can be bounded and therefore normalized, then there is an alternate class of feasible algorithms, of which RAM is representative and furthermore, only the REM-like and RAM-like classes are possible. For properly normalized link prices, RAM returns an optimal price estimate (in terms of mean squared error), outperforming REM even if the REM parameter is chosen optimally. RAM does not require setting a parameter like REM, but does require a router to know its position along the path taken by a packet. We present an implementation of RAM for the Internet that exploits the existing semantics of the time-to-live field in IP to provide the necessary path position information.

Service Clouds: A Distributed Infrastructure for Constructing Autonomic Communication Services

This paper describes Service Clouds, a distributed infrastructure designed to facilitate rapid prototyping and deployment of services that enhance communication performance, robustness, and security. The infrastructure combines adaptive middleware functionality with an overlay network substrate in order to support dynamic instantiation and reconfiguration of services. The Service Clouds architecture includes a collection of low-level facilities that can be either invoked directly by applications or used to compose more complex services. After describing the Service Clouds architecture, we present results of two experimental case studies conducted on the PlanetLab Internet testbed, the first to improve throughput of bulk data transfer, and the second to enhance the robustness of multimedia streaming

Service Clouds: Distributed Infrastructure for Adaptive Communication Services

This paper describes service clouds, a distributed infrastructure designed to facilitate rapid prototyping and deployment of adaptive communication services. The infrastructure combines adaptive middleware functionality with an overlay network substrate in order to support dynamic instantiation and reconfiguration of services. The service clouds architecture includes a collection of low-level facilities that can be invoked directly by applications or used to compose more complex services. After describing the service clouds architecture, we present results of experimental case studies conducted on the PlanetLab Internet testbed alone and a mobile computing testbed.

Local detection of selfish routing behavior in ad hoc networks

Reputation mechanisms for detecting and punishing free-riders in ad hoc networks depend on the local detection of selfish behavior. Although naive selfish strategies based on dropping data packets are readily detected, more sophisticated strategies that manipulate ad hoc routing protocols present a greater challenge. In this work we develop a method to distinguish selfish peers from cooperative ones based solely on local observations of AODV routing protocol behavior. Our approach uses the finite state machine model of locally observed AODV actions to build up a statistical description of the behavior of each neighbor. We apply a series of well-known statistical tests to features derived from this description to partition the set neighboring nodes into a cooperative and selfish class. Simulation results for a non-mobile ad hoc network show that our approach can detect two different types of routing manipulation while maintaining a low rate of false positives.

SANDS: Specialized Active Networking for Distributed Simulation

We provide an overview of SANDS (Specialized Active Networking for Distributed Simulation), a DARPA-ITO sponsored research project that is using active networking to develop a new approach to real-time, content-based information dissemination. Our approach is based on the use of active interest filtering, a publish/subscribe mechanism that uses active networks technology to install and control dynamically-established content-based filters in intermediate active routers in an IP multicast distribution tree. Active filters prune unneeded information as early as possible in the distribution tree, ensuring that only data desired (i.e., subscribed to) by a receiver actually reaches that receiver. We describe active interest filtering, the per-node algorithms that implement active filtering, and the signaling protocol that installs interest filter state in the active routers. We describe our prototype implementation effort and present measurements from a working prototype that demonstrate the advantages of active interest filtering in a large-scale ModSAF distributed simulation scenario.

Topology discovery service for router-assisted multicast transport

Many existing proposals for introducing network support for multicast transport require the establishment of signaling paths among adjacent active routers in a session. We present a general-purpose, lightweight protocol to establish a signaling overlay among sparsely deployed active nodes. The resulting overlay is tied to the underlying multicast route and adapts to changes in its topology. In addition, we make this overlay available to other protocols by means of efficient communication primitives to provide reliable signaling between neighboring active routers. Our protocol and associated services can serve as a building block for a variety of active multicast services and greatly simplify their development. We describe its applicability for implementing several previously proposed services.

Optimization-Based Congestion Control for Multicast Communications

Widespread deployment of multicast depends on the existence of congestion control protocols that are provably fair to unicast traffic. In this work, we present an optimization-based congestion control mechanism for single-rate multicast communication with provable fairness properties. The optimization-based approach attempts to find an allocation of rates that maximizes the aggregate utility of the network. We show that the utility of multicast sessions must be carefully defined if a widely accepted property of aggregate utility is to hold. Our definition of session utility amounts to maximizing a weighted sum of simple utility functions, with weights determined by the number of receivers. The fairness properties of the optimal rate allocation depend both on the weights and form of utility function used. We present analysis for idealized topologies showing that while our mechanism is not strictly fair to unicast, its unfairness can be controlled by appropriate choices of parameters.

Service differentiation for delay-sensitive applications: an optimisation-based approach

This paper deals with the performance of delay-sensitive applications running over a network that offers multiple classes of service, where the adaption of application rates in response to network feedback is the primary mechanism available for controlling quality of service. We first evaluate the gain in utilisation allowed by the introduction of several classes of service. To this end we compare the pairs of achievable rates, or schedulable regions, for two types of applications with two distinct delay requirements that make use of a single resource, with either no differentiation, simple priority-based differentiation, or earliest-deadline-first (EDF) scheduling-based differentiation. The main observations are that the gain achieved by differentiation is essentially affected by traffic burstiness, and that the two differentiation schemes yield very similar performance.We then consider what feedback information should be sent to traffic sources from different classes, casting the problem in the framework of optimisation-based congestion control. We establish a connection between the sample-path shadow price rationale for feedback synthesis and the rare perturbation analysis technique for gradient estimation in discrete event systems theory. Based on this connection, we propose several marking schemes, for simple priority-based differentiation with a measure of cost based on loss or delay, and also for EDF-based differentiation with loss-based cost. The interaction of these marking algorithms with simple congestion control algorithms is studied via simulations.