The World Wide Web can be considered as a large distributed information system that provides access to shared data objects. As one of the most popular applications currently running on the Internet, the size of World Wide Web is of an exponential growth, which results in network congestion and server overloading. Web caching has been recognized as one of the effective schemes to alleviate the server bottleneck and reduce the network traffic, thereby minimize the user access latencies. In this paper, we first discribe the elements of a Web caching system and its desirable properties. Then, we survey the state-of-art techniques which have been used in Web caching systems. Finally, we discuss the research frontier in Web caching.

Extensive measurement studies have shown that end-to-end Internet path performance degradation is correlated with routing dynamics. However, the root cause of the correlation between routing dynamics and such performance degradation is poorly understood. In particular, how do routing changes result in degraded end-toend path performance in the first place? How do factors such as topological properties, routing policies, and iBGP configurations affect the extent to which such routing events can cause performance degradation? Answers to these questions are critical for improving network performance.In this paper, we conduct extensive measurement that involves both controlled routing updates through two tier-1 ISPs and active probes of a diverse set of end-to-end paths on the Internet. We find that routing changes contribute to end-to-end packet loss significantly. Specifically, we study failover events in which a link failure leads to a routing change and recovery events in which a link repair causes a routing change. In both cases, it is possible to experience data plane performance degradation in terms of increased long loss burst as well as forwarding loops. Furthermore, we find that common routing policies and iBGP configurations of ISPs can directly affect the end-to-end path performance during routing changes. Our work provides new insights into potential measures that network operators can undertake to enhance network performance.

This study investigated the relationship between students’ opportunity to learn (OIL) and their science achievement. The data are of 623 8th-graders enrolled in five public schools in Los Angeles, California. Hierarchical linear modeling was used to analyze OTL variables at two levels of instructional processes: the classroom level and the student level. Students’ science test scores are based on a written test and a hands-on test. OTL effects on these two test scores were studied to see whether the effects differ depending on how science achievement is measured. It was found that OTL variables were significant predictors of both written and hands-on test scores even after students’ general ability level, ethnicity, and gender were controlled. The OTL effects varied by test format (written test and hands-on test). Content exposure was the most significant predictor of students’ written test scores, and quality of instructional delivery was the most significant predictor of the hands-on test scores. In conclusion, OTL variables are not unitary constructs; they are multidimensional, and different dimensions of OTL should be measured simultaneously to properly document the OTL-achievement relation.

Serving as the core component in many packet forwarding, differentiating and filtering schemes, packet classification continues to grow its importance in today's IP networks. Currently, most vendors use Ternary CAMs (TCAMs) for packet classification. TCAMs usually use brute-force parallel hardware to simultaneously check for all rules. One of the fundamental problems of TCAMs is that TCAMs suffer from range specifications because rules with range specifications need to be translated into multiple TCAM entries. Hence, the cost of packet classification will increase substantially as the number of TCAM entries grows. As a result, network operators hesitate to configure packet classifiers using range specifications. In this paper, we optimize packet classifier configurations by identifying semantically equivalent rule sets that lead to reduced number of TCAM entries when represented in hardware. In particular, we develop a number of effective techniques, which include: trimming rules, expanding rules, merging rules, and adding rules. Compared with previously proposed techniques which typically require modifications to the packet processor hardware, our scheme does not require any hardware modification, which is highly preferred by ISPs. Moreover, our scheme is complementary to previous techniques in that those techniques can be applied on the rule sets optimized by our scheme. We evaluate the effectiveness and potential of the proposed techniques using extensive experiments based on both real packet classifiers managed by a large tier-1 ISP and synthetic data generated randomly. We observe significant reduction on the number of TCAM entries that are needed to represent the optimized packet classifier configurations.

Anticancer drug responses can be varied for individual patients. This difference is mainly caused by genetic reasons, like mutations and RNA expression. Thus, these genetic features are often used to construct classification models to predict the drug response. This research focuses on the feature selection issue for the classification models. Because of the vast dimensions of the feature space for predicting drug response, the autoencoder network was first built, and a subset of inputs with the important contribution was selected. Then by using the Boruta algorithm, a further small set of features was determined for the random forest, which was used to predict drug response. Two datasets, GDSC and CCLE, were used to illustrate the efficiency of the proposed method.