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).

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Abstract-This paper presents a new MapReduce cloud service model, Cura, for provisioning cost-effective MapReduce services in a cloud. In contrast to existing MapReduce cloud services such as a generic compute cloud or a dedicated MapReduce cloud, Cura has a number of unique benefits. Firstly, Cura is designed to provide a cost-effective solution to efficiently handle MapReduce production workloads that have a significant amount of interactive jobs. Secondly, unlike existing services that require customers to decide the resources to be used for the jobs, Cura leverages MapReduce profiling to automatically create the best cluster configuration for the jobs. While the existing models allow only a per-job resource optimization for the jobs, Cura implements a globally efficient resource allocation scheme that significantly reduces the resource usage cost in the cloud. Thirdly, Cura leverages unique optimization opportunities when dealing with workloads that can withstand some slack. By effectively multiplexing the available cloud resources among the jobs based on the job requirements, Cura achieves significantly lower resource usage costs for the jobs. Cura's core resource management schemes include cost-aware resource provisioning, VM-aware scheduling and online virtual machine reconfiguration. Our experimental results using Facebook-like workload traces show that our techniques lead to more than 80% reduction in the cloud compute infrastructure cost with upto 65% reduction in job response times.

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