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MODIST is the first model checker designed for transparently checking unmodified distributed systems running on unmodified operating systems. It achieves this transparency via a novel architecture: a thin interposition layer exposes all actions in a distributed system and a centralized, OS-independent model checking engine explores these actions(More)
Peer-to-peer networks often use incentive policies to encourage cooperation between nodes. Such systems are generally susceptible to collusion by groups of users in order to gain unfair advantages over others. While techniques have been proposed to combat Web spam collusion, there are few measurements of real collusion in deployed systems. In this paper, we(More)
Much work has been done to address the need for incentive models in real deployed peer-to-peer networks. In this paper, we discuss problems found with the incentive model in a large, deployed peer-to-peer network, Maze. We evaluate several alternatives, and propose an incentive system that generates preferences for well-behaved nodes while correctly(More)
With the rapid growth of the demands for mobile data, wireless network faces several challenges, such as lack of efficient interconnection among heterogeneous wireless networks, and shortage of customized QoS guarantees between services. The fundamental reason for these challenges is that the radio access network (RAN) is closed and ossified. We propose(More)
Maze 1 is a P2P file-sharing system with an active and large user base. It is developed, deployed and operated by an academic research team. As such, it offers ample opportunities to conduct experiments to understand user behavior. Embedded in Maze is a set of incentive policies designed to encourage sharing and contribution. This paper presents an in-depth(More)
<i>Batched stream processing</i> is a new distributed data processing paradigm that models recurring batch computations on incrementally bulk-appended data streams. The model is inspired by our empirical study on a trace from a large-scale production data-processing cluster; it allows a set of effective query optimizations that are not possible in a(More)
As the study of large graphs over hundreds of gigabytes becomes increasingly popular for various data-intensive applications in cloud computing, developing large graph processing systems has become a hot and fruitful research area. Many of those existing systems support a <i>vertex-oriented</i> execution model and allow users to develop custom logics on(More)
Cloud services inevitably fail: machines lose power, networks become disconnected, pesky software bugs cause sporadic crashes, and so on. Unfortunately, failure recovery itself is often faulty; e.g. recovery can accidentally re-cursively replicate small failures to other machines until the entire cloud service fails in a catastrophic outage, amplifying a(More)