Abstract — Overlay networks are widely used to deploy functionality at edge nodes without changing network routers. Each node in an overlay network maintains connections with a number of peers, forming a graph upon which a distributed application or service is implemented. In an “Eclipse ” attack, a set of malicious, colluding overlay nodes arranges for a correct node to peer only with members of the coalition. If successful, the attacker can mediate most or all communication to and from the victim. Furthermore, by supplying biased neighbor information during normal overlay maintenance, a modest number of malicious nodes can eclipse a large number of correct victim nodes. This paper studies the impact of Eclipse attacks on structured overlays and shows the limitations of known defenses. We then present the design, implementation, and evaluation of a new defense, in which nodes anonymously audit each other’s connectivity. The key observation is that a node that mounts an Eclipse attack must have a higher than average node degree. We show that enforcing a node degree limit by auditing is an effective defense against Eclipse attacks. Furthermore, unlike most existing defenses, our defense leaves flexibility in the selection of neighboring nodes, thus permitting important overlay optimizations like proximity neighbor selection (PNS). I.

This paper is concerned with the extra-resource analysis of online scheduling algorithms. In particular, it studies how to make use of multiple processors to counteract the lack of future information in online deadline scheduling. Our results extend the previous work that are primarily based on using a faster processor to obtain a performance guarantee. The challenge arises from the fact that jobs are sequential in nature and cannot be executed on more than one processor at the same time. Thus, a faster processor can speed up a job while multiple unit-speed processors cannot help.

We consider the problem of scheduling jobs with deadlines in an on-line single-processor system. It is known for long that unless the system is underloaded, any on-line algorithm is not optimal in the sense that it may fail to match the optimal offline algorithm on the total work of jobs meeting their deadlines. In this paper, we extend this fact with two new lower bound results. First, we show that even if the on-line scheduler can use a processor s times faster than the offline scheduler, where s 1 is any real number less than the golden ratio (i.e. (1 + 5)=2 1:618), no online algorithm can be optimal. Furthermore, if we restrict our attention to on-line algorithms that decide at the release time of a job whether the job will be processed to meet its deadline, then the speed requirement for optimal on-line algorithms is at least 2. These lower bound results should be contrasted with the recent upper bound result [11] that with a two times faster processor, a simple extension of the earliest deadline first strategy (EDF) is optimal.