This paper focuses on the problem of consistency in distributed data stores. We define strong consistency model which provides a simple semantics for application programmers, but impossible to achieve with availability and partition-tolerance. We also define weaker consistency models including causal and eventual consistency. We review COPS and GentleRain as two causally consistent data stores ...

Weakly consistent data stores have become popular because they enable highly available, scalable distributed applications. However, some data needs strong consistency. For applications that mix accesses to strongly and weakly consistent data, programmers must currently choose between bad performance and possible data corruption. We instead introduce a safe mixed-consistency programming model in...

Caching frequently accessed data items on the client side is an effective technique to improve the system performance in wireless networks. Due to cache size limitations, cache replacement algorithms are used to find a suitable subset of items for eviction from the cache. Many existing cache replacement algorithms employ a value function of different factors such as time since last access, entr...

kernel density estimators are the basic tools for density estimation in non-parametric statistics.  the k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. in this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

Today’s online services must meet strict availability and performance requirements. State machine replication, one of the most fundamental approaches to increasing the availability of services without sacrificing strong consistency, provides configurable availability but limited performance scalability. Scalable State Machine Replication (S-SMR) achieves scalable performance by partitioning the...