We propose a framework and methodology for quantifying the effect of denial of service (DoS) attacks on a distributed system. We present a systematic study of the resistance of gossip-based multicast protocols to DoS attacks. We show that even distributed and randomized gossip-based protocols, which eliminate single points of failure, do not necessarily eliminate vulnerabilities to DoS attacks. We propose Drum – a simple gossip-based multicast protocol that eliminates such vulnerabilities. Drum was implemented in Java and tested on a large cluster. We show, using closed-form mathematical analysis, simulations, and empirical tests, that Drum survives severe DoS attacks. 1

Abstract — As CMOS technology scales down into the deepsubmicron (DSM) domain, devices and interconnects are subject to new types of malfunctions and failures that are harder to predict and avoid with the current system-on-chip (SoC) design methodologies. Relaxing the requirement of 100 % correctness in operation drastically reduces the costs of design but, at the same time, requires SoCs be designed with some degree of system-level faulttolerance. In this paper, we introduce a high-level model of DSM failure patterns and propose a new communication paradigm for SoCs, namely stochastic communication. Specifically, for a generic tile-based architecture, we propose a randomized algorithm which not only separates computation from communication, but also provides the required fault-tolerance to on-chip failures. This new technique is easy and cheap to implement in SoCs that integrate a large number of communicating IP cores. I.

Gossip-based multicast can be an effective tool for providing highly reliable and scalable message dissemination. Previous work has shown it to be useful in a variety of group communication settings when processes all belong to a single process group. In this paper, we consider the problem of gossiping within overlapping process subgroups. If each subgroup independently runs the standard gossip protocol, then the total gossip overhead could be high for a process that is a member of many subgroups. We present anovel gossip protocol that allows individual subgroup members to trade-off update quality for gossip overhead, enabling processes to belong to several subgroups while maintaining a low total gossip overhead. Our results include a mathematical model for message dissemination under this modified gossip protocol, and an algorithm that computes gossip parameters such that all processes within a subgroup achieve their desired update quality. Preliminary results are promising.

We study the e#ectiveness of automatic patching and quantify the speed of patch dissemination required for worm containment. We focus on random scanning as this is representative of current generation worms, though smarter strategies exist. We find that even such "dumb" worms require very fast patching. Our primary focus is on how delays due to worm detection and patch generation and dissemination a#ect worm spread. Motivated by scalability and trust issues, we consider a hierarchical system where network hosts are partitioned into subnets, each containing a patch server (termed superhost). Patches are disseminated to superhosts through an overlay connecting them and, after verification, to end hosts within subnets. When patch dissemination delay on the overlay is negligible, we find that the number of hosts infected is exponential in the ratio of worm infection rate to patch rate. This implies strong constraints on the time to disseminate, verify and install patches in order for it to be e#ective. We also provide bounds that account for alert or patch dissemination delay. Finally, we evaluate the use of filtering in combination with patching and show that it can substantially improve worm containment. The results accommodate a variety of overlays by a novel abstraction of minimum broadcast curve. They demonstrate that e#ective automatic patching is feasible if combined with mechanisms to bound worm scan rate and with careful engineering of the patch dissemination. The results are obtained analytically and verified by simulations.

A new class of gossip protocols is presented to diffuse updates securely. The protocols rely on annotating updates with the path along which they travel. To avoid a combinatorial explosion in the number of such annotated updates, rules are employed to choose which updates to keep. Different sets of rules lead to different protocols. Results of simulated executions of a collection of such protocols are described the protocols would appear to be practical, even in large networks.

Abstract—The problem of computing functions of values at the nodes in a network in a fully distributed manner, where nodes do not have unique identities and make decisions based only on local information, has applications in sensor, peer-to-peer, and adhoc networks. The task of computing separable functions, which can be written as linear combinations of functions of individual variables, is studied in this context. Known iterative algorithms for averaging can be used to compute the normalized values of such functions, but these algorithms do not extend in general to the computation of the actual values of separable functions. The main contribution of this paper is the design of a distributed randomized algorithm for computing separable functions. The running time of the algorithm is shown to depend on the running time of a minimum computation algorithm used as a subroutine. Using a randomized gossip mechanism for minimum computation as the subroutine yields a complete fully distributed algorithm for computing separable functions. For a class of graphs with small spectral gap, such as grid graphs, the time used by the algorithm to compute averages is of a smaller order than the time required by a known iterative averaging scheme. Index Terms—Data aggregation, distributed algorithms, gossip algorithms, randomized algorithms. I.

As CMOS technology scales down into the deep-submicron (DSM) domain, the costs of design and verification for Systems-On-Chip (SoCs) are rapidly increasing due to the inefficiency of traditional CAD tools. Relaxing the requirement of 100% correctness for devices and interconnects drastically reduces the costs of design but, at the same time, requires that SoCs be designed with some systemlevel fault-tolerance. In this paper, we introduce a new communication paradigm for SoCs, namely stochastic communication. The newly proposed scheme not only separates communication from computation, but also provides the required built-in faulttolerance to DSM failures, is scalable and cheap to implement. For a generic tile-based architecture, we show how a ubiquitous multimedia application (an MP3 encoder) can be implemented using stochastic communication in an efficient and robust manner. More precisely, up to 70% data upsets, 80% packet drops because of buffer overflow, and severe levels of synchronization failures can be tolerated while maintaining a low latency.

We study how to efficiently diffuse updates to a large distributed system of data replicas, some of which may exhibit arbitrary (Byzantine) failures. We assume that strictly fewer than t replicas fail, and that each update is initially received by at least t correct replicas. The goal is to diffuse each update to all correct replicas while ensuring that correct replicas accept no updates generated spuriously by faulty replicas. To achieve this, each correct replica further propagates an update only after receiving it from at least t others. In this way, no correct replica will ever propagate or accept an update that only faulty replicas introduce, since it will receive that update from only the t 1 faulty replicas.

Recently, there has been a growing interest in gossip-based protocols that employ randomized communication to ensure robust information dissemination. In this paper, we present a novel gossip-based scheme using which all the nodes in an n-node overlay network can compute the common aggregates of MIN, MAX, SUM, AVERAGE, and RANK of their values using O(n log log n) messages within O(log n log log n) rounds of communication. To the best of our knowledge, ours is the first result that shows how to compute these aggregates with high probability using only O(n log log n) messages. In contrast, the best known gossip-based algorithm for computing these aggregates requires O(n log n) messages and O(log n) rounds. Thus, our algorithm allows system designers to trade off a small increase in round complexity with a significant reduction in message complexity. This can lead to dramatically lower network congestion and longer node lifetimes in wireless and sensor networks, where channel bandwidth and battery life are severely constrained. 1.

In multipeer communication decentralised probabilistic protocols have received a lot of attention because of their robustness against faults in the communication traffic and their potential to provide scalability for large groups. These protocols provide a probabilistic guarantee for a propagated event to reach every group member. Recent work aims to improve the scalability of such protocols by reducing memory requirements. In saving memory resources, the history buffer, which is used to "remember" received events and to prevent multiple deliveries of events to the application, plays a very significant role. We examine how the buffer size should be chosen to challenge the multiple delivery problem. Further, we propose and evaluate several methods of organising the dissemination of events in order to provide high reliability and reduce the number of multiple deliveries at the same time.