Data consistency is a critical topic in distributed systems. In existing consistency models, causal consistency has attracted a significant amount of attention because it can satisfy high-performance requirements even in the presence of network partitions. At present, most of the causal consistency models face a tradeoff between throughput and update visibility. Simultaneously, they cannot take full advantage of partial geo-replication. To resolve the problems, this paper proposes a causal consistency model that supports partial replication using the adjacency list, called Adjoin. In Adjoin, each data center (DC) stores only a subset of the full data, by reading adjacency relationships, and the relevant nodes quickly reach synchronization. We also introduce the Adjacency Stable Vector and Adjacency Dependency Set to capture causality, which reduces the system storage overhead. We evaluate Adjoin with different workloads on a cloud platform using multiple sites. The results show that Adjoin has good performance in terms of throughput and update visibility compared with previous causal consistency models.

At present, most causal consistency models based on cloud storage can no longer meet the needs of delay-sensitive applications. Moreover, the overhead of data synchronization between replicas is too high. This paper proposes a causal consistency model of edge-cloud collaborative based on grouping protocol. The model based on the edge-cloud collaboration architecture, partitions cloud data centers and groups edge nodes by distributed hash tables, and stores a subset of the complete data set in nodes located at the edge of the network. Thereby realize partial geo-replication in edge-cloud collaboration environment. At the same time, we design a group synchronization algorithm called Imp_Paxos, so that the update only needs to be synchronized to the main group, which reduces the visibility delay of the update and decreases the data synchronization overhead.Besides, a sort timestamp is proposed in this paper, which generates different timestamps according to the type of update to track causality, keeping the amount of metadata managed in a relatively stable and low state.Threrfore, the proposed model reduces the overhead of metadata for system management, and improves throughput quantity of system. Experiments show that, our model performs well in terms of throughput, operation latency, and update visibility latency compared with existing causal consistency models.

With the rapid development of P2P network, free riding has become a serious problem. Controlling free riding is a hot research both in academic and industrial communities. In this paper, a Distributed and Monitoring-based Mechanism (DMM) is proposed to discourage free riding in P2P network. Based on the behavior and function of nodes in the network, the paper makes the network system abstractly as a distributed and monitoring structure focus on avoiding malignant cheating and dishonest behaviors. Through analyzing kinds of factors that effect the nodes' contribution to the whole network, a utility function is defined to determine the useful degree of the nodes and make corresponding treatments to the nodes' service request. Moreover, we introduce reputation to guarantee the QoS and import license to realize the rewards and punishment with nodes in the network. Finally, the effectiveness and feasibility of the model is illustrated by the simulation experiment designed with PeerSim.

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