Asynchronous geo-replication for data resources is used to provide high availability and lower latency in modern cloud store systems. Convergent causal consistency is the corner-stone to provide useful semantics for online human interaction services. Compared to full replication, partial replication has potential benefit of lower message counts in social network systems. However, static replication is ineffective for time-varying workloads. We propose a causal+ consistency protocol, CaDRoP, to support dynamic replication, and ensure the convergence property for all comments following a post and the causal ordering between posts with explicit causality. We evaluate CaDRoP protocol with realistic workloads by different PUT rates in terms of the practical price of Amazon AWS. The results show that CaDRoP incurs much lower cost than the statically replicated data store in another causal+ algorithm. We further evaluate CaDRoP by comparing it with a clairvoyant optimal replication solution. The findings indicate that with cache, CaDRoP incurs only around 6% ~ 16% extra cost. Without cache, CaDRoP brings around 2% ~ 4.5% extra cost in steady states.

In wide-area distributed systems, data replication provides fault tolerance and low latency. Causal consistency in such systems is an interesting consistency model. Most existing works assume the data is fully replicated because this greatly simplifies the design of the algorithms to implement causal consistency. Recently, we proposed causal consistency under partial replication because it reduces the number of messages used under a wide range of workloads. One drawback of partial replication is that its meta-data tends to be relatively large when the message size is small. In this paper, we propose an algorithm Approx-Opt-Track which provides approximate causal consistency whereby we can reduce the meta-data at the cost of some violations of causal consistency. The amount of violations can be made arbitrarily small by controlling a tunable parameter, that we call credits. We present the analytic data to show the performance of Approx-Opt-Track. We then give simulation results to show the potential benefit of Approx-Opt-Track, viz., its ability to provide almost the same guarantees as causal consistency, at a smaller cost.

Analyzing social network structures can provide an insight into the character of human interactions and communication mechanisms for solving a variety of social problems. By applying variable social vector clocks and involving weight evolution influence, we construct a coupled-weight and directed link generation algorithm for modeling a social topology in a closed social group. The degree and weight strength distributions of simulation topologies demonstrate the scale-free properties and effectiveness of weight diffusion in the real world.