Distributed Algorithmic Mechanism Design (DAMD) combines theoretical computer science’s traditional focus on computational tractability with its more recent interest in incentive compatibility and distributed computing. The Internet’s decentralized nature, in which distributed computation and autonomous agents prevail, makes DAMD a very natural approach for many Internet problems. This paper first outlines the basics of DAMD and then reviews previous DAMD results on multicast cost sharing and interdomain routing. The remainder of the paper describes several promising research directions and poses some specific open problems.

We compare structured and unstructured overlays and derive a hybrid overlay that can outperform both. Unstructured overlays build a random graph and use flooding or random walks on that graph to discover data stored by overlay nodes. Structured overlays assign keys to data items and build a graph that maps each key to the node that stores the corresponding data. Unstructured overlays are widely used in popular applications because they can perform complex queries more efficiently than structured overlays. It is also commonly believed that structured graphs are more expensive to maintain than unstructured graphs and that the constraints imposed by the structure make it harder to exploit heterogeneity to improve scalability. This is not a fundamental problem. We describe techniques that exploit structure to achieve low maintenance overhead, and we present a modified proximity neighbor selection algorithm that can exploit heterogeneity effectively. We performed detailed comparisons of structured and unstructured graphs using simulations driven by real-world traces. Inspired by these results, we developed a hybrid system that uses the graph from structured overlays with the data placement and search strategies of unstructured overlays. The results show that our hybrid system supports complex queries more efficiently than unstructured overlays in realistic scenarios.

Flashcrowds—sudden surges of user arrivals—do occur in BitTorrent, and they can lead to severe service deprivation. However, very little is known about their occurrence patterns and their characteristics in real-world deployments, and many basic questions about BitTorrent flashcrowds, such as How often do they occur? and How long do they last?, remain unanswered. In this paper, we address these questions by studying three datasets that cover millions of swarms from two of the largest BitTorrent trackers. We first propose a model for BitTorrent flashcrowds and a procedure for identifying, analyzing, and modeling BitTorrent flashcrowds. Then we evaluate quantitatively the impact of flashcrowds on BitTorrent users, and we develop an algorithm that identifies BitTorrent flashcrowds. Finally, we study statistically the properties of BitTorrent flashcrowds identified from our datasets, such as their arrival time, duration, and magnitude, and we investigate the relationship between flashcrowds and swarm growth, and the arrival rate of flashcrowds in BitTorrent trackers. In particular, we find that BitTorrent flashcrowds only occur in very small fractions (0.3-2%) of the swarms but that they can affect over ten million users.