Since inception, DNS has used a TTL-based replication scheme that allows the source (i.e., an authoritative domain server) to control the frequency of record eviction from client caches. Existing studies of DNS predominantly focus on reducing query latency and source bandwidth, both of which are optimized by increasing the cache hit rate. However, this causes less-frequent contacts with the source and results in higher staleness of retrieved records. Given high data-churn rates at certain providers (e.g., dynamic DNS, CDNs) and importance of consistency to their clients, we propose that cache models include the probability of freshness as an integral performance measure. We derive this metric under general update/download processes and present a novel framework for measuring its value using remote observation (i.e., without access to the source or the cache). Besides freshness, our methods can estimate the inter-update distribution of DNS records, cache hit rate, distribution of TTL, and query arrival rate from other clients. Furthermore, these algorithms do not require any changes to the existing infrastructure/protocols.

Network applications commonly maintain local copies of remote data sources in order to provide caching, indexing, and data-mining services to their clients. Modeling performance of these systems and predicting future updates usually requires knowledge of the inter-update distribution at the source, which can only be estimated through blind sampling-periodic downloads and comparison against previous copies. In this paper, we first introduce a stochastic modeling framework for this problem, where updates and sampling follow independent point processes. We then show that all previous approaches are biased unless the observation rate tends to infinity or the update process is Poisson. To overcome these issues, we propose four new algorithms that achieve various levels of consistency, which depend on the amount of temporal information revealed by the source and capabilities of the download process.

We analyze synchronization issues between two point processes, one modeling data churn at an information source and the other periodic downloads to its replica (e.g., search engine, web cache, distributed database). Due to pull-based synchronization, the replica experiences recurrent staleness, which translates into some form of penalty stemming from its reduced ability to perform consistent computation and/or provide up-to-date responses to customer requests. We model this system under non-Poisson update/refresh processes and obtain sample-path averages of various metrics of staleness cost, generalizing previous results and exposing novel problems in this field.

We analyze synchronization issues arising between two stochastic point processes, one of which models data churn at an information source and the other periodic downloads from its replica (e.g., search engine, web cache, distributed database). Due to lazy (pull-based) synchronization, the replica experiences recurrent staleness, which translates into some form of penalty stemming from its reduced ability to perform consistent computation and/or provide up-to-date responses to customer requests. We model this system under non-Poisson update/refresh processes and obtain sample-path averages of various metrics of staleness cost, generalizing previous results and exposing novel problems in this field.

Network applications commonly maintain local copies of remote data sources in order to provide caching, indexing, and data-mining services to their clients. Modeling performance of these systems and predicting future updates usually requires knowledge of the inter-update distribution at the source, which can only be estimated through blind sampling - periodic downloads and comparison against previous copies. In this paper, we first introduce a stochastic modeling framework for this problem, where the update and sampling processes are both renewal. We then show that all previous approaches are biased unless the observation rate tends to infinity or the update process is Poisson. To overcome these issues, we propose four new algorithms that achieve various levels of consistency, which depend on the amount of temporal information revealed by the source and capabilities of the download process.

We consider pull-based data synchronization issues between a source and its replicas in P2P networks. Under continuous information change and lazy synchronization, these systems are highly susceptible to serving outdated content, which negatively affects their performance and user satisfaction. To understand these scenarios, we first introduce a novel model of interaction between two stochastic point processes - updates at the source and downloads at the replica - and derive the probability that a random query against the replica retrieves fresh content. Unlike prior work, we assume non-Poisson dynamics and determine statistical properties of the replication process that make it perform better for a given download rate. The second half of the paper applies these results to several more difficult algorithms - cascaded replication, cooperative caching, and redundant querying from the clients. Surprisingly, we discover that optimal cooperation involves just a single peer and that redundant querying can hurt the ability of the system to handle load (i.e., may lead to lower scalability).

Traffic monitoring and estimation of flow parameters in high-speed routers have recently become challenging as the Internet grew in both scale and complexity. In this paper, we focus on a family of flow-size estimation algorithms we call Residual-Geometric Sampling (RGS), which generates a random point within each flow according to a geometric random variable and records all remaining packets in a flow counter. Our analytical investigation shows that previous estimation algorithms based on this method exhibit bias in recovering flow statistics from the sampled measurements. To address this problem, we derive a novel set of unbiased estimators for RGS, validate them using real Internet traces, and show that they provide an accurate and scalable solution to Internet traffic monitoring.

Traffic monitoring and estimation of flow parameters in high speed routers have recently become challenging as the Internet grew in both scale and complexity. In this paper, we focus on a family of flow-size estimation algorithms we call Residual-Geometric Sampling (RGS), which generates a random point within each flow according to a geometric random variable and records all remaining packets in a flow counter. Our analytical investigation shows that previous estimation algorithms based on this method exhibit certain bias in recovering flow statistics from the sampled measurements. To address this problem, we derive a novel set of unbiased estimators for RGS, validate them using real Internet traces, and show that they provide an accurate and scalable solution to Internet traffic monitoring.