Abstract not found

Diagnosing faults in the Internet is arduous and time-consuming, in part because the network is composed of diverse components spread across many administrative domains. We consider an extreme form of this problem: can end users, with no special privileges, identify and pinpoint faults inside the network that degrade the performance of their applications? To answer this question, we present both an architecture for user-level Internet path diagnosis and a practical tool to diagnose paths in the current Internet. Our architecture requires only a small amount of network support, yet it is nearly as complete as analyzing a packet trace collected at all routers along the path. Our tool, tulip, diagnoses reordering, loss and significant queuing events by leveraging well deployed but little exploited router features that approximate our architecture. Tulip can locate points of reordering and loss to within three hops and queuing to within four hops on most paths that we measured. This granularity is comparable to that of a hypothetical network tomography tool that uses 65 diverse hosts to localize faults on a given path. We conclude by proposing several simple changes to the Internet to further improve its diagnostic capabilities.

This note briey summarizes some results from two papers: [4] and [23]. These papers pose the following question: Is it possible to reduce the expected response time of every request at a web server, simply by changing the order in which we schedule the requests? In [4] we approach this question analytically via an M/G/1 queue. In [23] we approach the same question via implementation involving an Apache web server running on Linux.

WWW workload generators are used to evaluate web server performance, and thus have a large impact on what performance optimizations are applied to servers. However, current benchmarks ignore a crucial component: how these servers perform in the environment in which they are intended to be used, namely the widearea Internet.

Modern enterprise networks are of sufficient complexity that even simple faults can be difficult to diagnose — let alone transient outages or service degradations. Nowhere is this problem more apparent than in the 802.11-based wireless access networks now ubiquitous in the enterprise. In addition to the myriad complexities of the wired network, wireless networks face the additional challenges of shared spectrum, user mobility and authentication management. Not surprisingly, few organizations have the expertise, data or tools to decompose the underlying problems and interactions responsible for transient outages or performance degradations. In this paper, we present a set of modeling techniques for automatically characterizing the source of such problems. In particular, we focus on data transfer delays unique to 802.11 networks — media access dynamics and mobility management latency. Through a combination of measurement, inference and modeling we reconstruct sources of delay — from the physical layer to the transport layer — as well as the interactions among them. We demonstrate our approach using comprehensive traces of wireless activity in the UCSD Computer Science building.

The performance of popular Internet Web services is governed by a complex combination of server behavior, network characteristics and client workload -- all interacting through the actions of the underlying transport control protocol (TCP). Consequently, even small changes to TCP or to the network infrastructure can have significant impact on end-to-end performance, yet at the same time it is challenging for service administrators to predict what that impact will be. In this paper we describe the implementation of a tool called Monkey that is designed to help address such questions. Monkey collects live TCP trace data near a server, distills key aspects of each connection (e.g., network delay, bottleneck bandwidth, server delays, etc.) and then is able to faithfully replay the client workload in a new setting. Using Monkey, one can easily evaluate the effects of different network implementations or protocol optimizations in a controlled fashion, without the limitations of synthetic workloads or the lack of reproducibility of live user traffic. Using realistic network traces from the Google search site, we show that Monkey is able to replay traces with a high degree of accuracy and can be used to predict the impact of changes to the TCP stack.

This paper addresses the problem of how to service web requests quickly in order to minimize the client response time. Some of the recent work uses the idea of the Shortest Remaining Processing Time scheduling (SRPT) in Web servers in order to give preference to requests for short files. However, by considering only the size of the file for determining the priority of requests, the previous works lack in capturing potentially useful scheduling information contained in the interaction between networks and end systems. To address this, this paper proposes and implements an algorithm, SWIFT, that focuses on both server and network characteristics in conjunction. Our approach prioritizes requests based on the size of the file requested and the distance of the client from the server. The implementation is at the kernel level for a finer-grained control over the packets entering the network. We present the results of the experiments conducted in a WAN environment to test the efficacy of SWIFT. The results show that for large-sized files, SWIFT shows an improvement of 2.5% - 10% over the SRPT scheme for the tested server loads.

DNS is a critical component of the operation of Internet applications. However, DNS performance in the wide-area is not well understood. A number of studies present DNS performance measurements [1], [2], [3], [4], but the measurements are out of date, are not collected at client locations (e.g., they are taken at root servers), or are collected at very few client locations.

This paper presents a one-year study of Internet packet traffic from a large campus network, showing that 15-25 % of TCP connections have at least one TCP RST (reset). Similar results have also been observed from measurements of other Internet links. The results in this paper show that reset connections arise from local events such as network outages, attacks, or reconfigurations, as well as from global trends in TCP usage. In particular, we identify application-level Web behaviour as the primary contributor to the global trend in reset TCP connections. The most prevalent anomaly is the absence of the normal FIN handshake for connection termination. Instead, connections are often reset by the client. We believe that particular implementations of HTTP/TCP connection management cause this global trend. 1

This paper presents a resource management framework for providing predictable quality of service in Web servers. The framework allows Web server and proxy operators to ensure a minimal quality of service, expressed as an average request rate or average response time, for a certain class of requests (called a service), irrespective of the load imposed by other requests. A measurement-based admission control framework determines whether a service can be hosted on a given server or proxy, based on the measured statistics of the resource consumptions and the desired QoS levels of all the co-located services. In addition, we present a feedback-based resource scheduling framework that ensures that QoS levels are maintained among admitted, co-located services. Experimental results obtained with a prototype implementation of our framework on trace-based workloads show its effectiveness in providing desired QoS levels with high confidence, while achieving high average utilization of the hardware. 1