Effective caching in the domain name system (DNS) is critical to its performance and scalability. Existing DNS only supports weak cache consistency by using the time-to-live (TTL) mechanism, which functions reasonably well in normal situations. However, maintaining strong cache consistency in DNS as an indispensable exceptional handling mechanism has become more and more demanding for three important objectives: 1) to quickly respond and handle exceptions such as sudden and dramatic Internet failures caused by natural and human disasters, 2) to adapt increasingly frequent changes of Internet Protocol (IP) addresses due to the introduction of dynamic DNS techniques for various stationed and mobile devices on the Internet, and 3) to provide fine-grain controls for content delivery services to timely balance server load distributions. With agile adaptation to various exceptional Internet dynamics, strong DNS cache consistency improves the availability and reliability of Internet services. In this paper, we first conduct extensive Internet measurements to quantitatively characterize DNS dynamics. Then, we propose a proactive DNS cache update protocol (DNScup), running as middleware in DNS name servers, to provide strong cache consistency for DNS. The core of DNScup is an optimal lease scheme, called dynamic lease, to keep track of the local DNS name servers. We compare dynamic lease with other existing lease schemes through theoretical analysis and trace-driven simulations. Based on the DNS dynamic update protocol, we build a DNScup prototype with minor modifications to the current DNS implementation. Our system prototype demonstrates the effectiveness of DNScup and its easy and incremental deployment on the Internet.

Object detection quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Partial coverage, where a sensing field is partially sensed by active sensors at any time, is an appropriate approach to balancing the two conflicting design requirements of monitoring applications. Under partial coverage, we develop an analytical framework for object detection in sensor networks, and mathematically analyze average-case object detection quality in random and synchronized sensing scheduling protocols. Our analytical framework facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Furthermore, we propose three wave sensing scheduling protocols to achieve bounded worst-case object detection quality. We justify the correctness of our analyses through rigorous proof, and validate the effectiveness of the proposed protocols through extensive simulation experiments

Effective caching in Domain Name System (DNS) is critical to its performance and scalability. Existing DNS only supports weak cache consistency by using the Time-To-Live (TTL) mechanism, which functions reasonably well in normal situations. However, maintaining strong cache consistency in DNS as an indispensable exceptional handling mechanismhas become more and more demanding for three important objectives: (1) to quickly respond and handle exceptional incidents, such as sudden and dramatic Internet failures caused by natural and human disasters, (2) to adapt increasingly frequent changes of IP addresses due to the introduction of dynamic DNS techniques for various stationed and mobile devices on the Internet, and (3) to provide finegrain controls for content delivery services to timely balance server load distributions. With agile adaptation to various exceptional Internet dynamics, strong DNS cache consistency improves the availability and reliability of Internet services. In this paper, we propose a proactive DNS cache update protocol, called DNScup, running as middleware in DNS nameservers, to provide strong cache consistency for DNS. The core of DNScup is a dynamic lease technique to keep track of the local DNS nameservers, whose clients need cache coherence to avoid losing service availability. Based on the DNS Dynamic Update protocol, we have built a DNScup prototype with minor modifications to the current DNS implementation. Our trace-driven simulation and system prototype demonstrate the effectiveness of DNScup and its easy and incremental deployment on the Internet.

While current peer-to-peer (P2P) systems facilitate static file sharing, newly developed applications demand that P2P systems be able to manage dynamically changing files. Maintaining consistency between frequently updated files and their replicas is a fundamental reliability requirement for a P2P system. In this paper, we present SCOPE, a structured P2P system supporting consistency among a large number of replicas. By building a replica-partition-tree (RPT) for each key, SCOPE keeps track of the locations of replicas and then propagates update notifications. Our theoretical analyses and experimental results demonstrate that SCOPE can effectively maintain replica consistency while preventing hot spot and node-failure problems. Its efficiency in maintenance and failure-recovery is particularly attractive to the deployment of large-scale P2P systems.

The demand of delivering streaming media content in the Internet has become increasingly high for scientific, educational, and commercial applications. Three representative technologies have been developed for this purpose, each of which has its merits and serious limitations. Infrastructure-based CDNs with dedicated network bandwidths and powerful media replicas can provide high quality streaming services but at a high cost. Server-based proxies are cost-effective but not scalable due to the limited proxy capacity and its centralized control. Client-based P2P networks are scalable but do not guarantee high quality streaming service due to the transient nature of peers. To address these limitations, we present a novel and efficient design of a scalable and reliable media proxy system supported by P2P networks. This system is called PROP abbreviated from our technical theme of "collaborating and coordinating PROxy and its P2P clients". Our objective is to address both scalability and reliability issues of streaming media delivery in a cost-effective way. In the PROP system, the clients' machines in an intranet are self-organized into a structured P2P system to provide a large media storage and to actively participate in the streaming media delivery, where the proxy is also embedded as an important member to ensure quality of streaming service. The coordination and collaboration in the system are efficiently conducted by our P2P management structure and replacement policies. We have comparatively evaluated our system by trace-driven simulations with synthetic workloads and with a real-life workload trace extracted from the media server logs in an enterprise network. The results show that our design significantly improves the quality of media streaming and the system scalability.

Although Web prefetching is regarded as an effective method to improve client access performance, the associated overhead prevents it from being widely deployed. Specifically, a major weakness in existing Web servers is that prefetching activities are scheduled independently of dynamically changing server workloads. Without proper control and coordination between the two kinds of activities, prefetching can negatively affect the Web services and degrade Web access performance. We first develop an open queuing model to characterize detailed transactions in Web servers. Using this model, we analyze server resource utilization and average response time with different request arrival rates when prefetching is involved under different kinds of Web services. Guided by this model, we then design a responsive and adaptive prefetching scheme that dynamically adjusts the prefetching aggressiveness in Web servers. Our scheme not only prevents the Web servers from being overloaded, but it can also minimize the average server response time. We have effectively implemented this scheme on an Apache Web server. Our measurement-based performance evaluation shows our model can accurately predict the utilization of Web server resources and the correspondent average response time.

Prediction by partial match (PPM) is a commonly used technique in Web prefetching, where prefetching decisions are made based on historical URLs in a dynamically maintained Markov prediction tree. Existing approaches either widely store the URL nodes by building the tree with a fixed height in each branch, or only store the branches with frequently accessed URLs. Building the popularity information into the Markov prediction tree, we propose a new prefetching model, called popularity-based PPM. In this model, the tree is dynamically updated with a variable height in each set of branches where a popular URL can lead a set of long branches, and a less popular document leads a set of short ones. Since majority root nodes are popular URLs in our approach, the space allocation for storing nodes are effectively utilized. We have also included two additional optimizations in this model: (1) directly linking a root node to duplicated popular nodes in a surfing path to give popular URLs more considerations for prefetching; and (2) making a space optimization after the tree is built to further remove less popular nodes. Our trace-driven simulation results comparatively show a significant space reduction and an improved prediction accuracy of the proposed prefetching technique.