31,734 research outputs found
Measuring Web Speed From Passive Traces
Understanding the quality of Experience (QoE) of web brows- ing is key to optimize services and keep usersâ loyalty. This is crucial for both Content Providers and Internet Service Providers (ISPs). Quality is subjective, and the complexity of todayâs pages challenges its measurement. OnLoad time and SpeedIndex are notable attempts to quantify web performance with objective metrics. However, these metrics can only be computed by instrumenting the browser and, thus, are not available to ISPs. We designed PAIN: PAssive INdicator for ISPs. It is an automatic system to monitor the performance of web pages from passive measurements. It is open source and available for download. It leverages only flow-level and DNS measurements which are still possible in the network despite the deployment of HTTPS. With unsupervised learn- ing, PAIN automatically creates a machine learning model from the timeline of requests issued by browsers to render web pages, and uses it to measure web performance in real- time. We compared PAIN to indicators based on in-browser instrumentation and found strong correlations between the approaches. PAIN correctly highlights worsening network conditions and provides visibility into web performance. We let PAIN run on a real ISP network, and found that it is able to pinpoint performance variations across time and groups of users
Quality in Measurement: Beyond the deployment barrier
Network measurement stands at an intersection in the development of the science. We explore possible futures for the area and propose some guidelines for the development of stronger measurement techniques. The paper concludes with a discussion of the work of the NLANR and WAND network measurement groups including the NLANR Network Analysis Infrastructure, AMP, PMA, analysis of Voice over IP traffic and separation of HTTP delays into queuing delay, network latency and server delay
Monitoring Challenges and Approaches for P2P File-Sharing Systems
Since the release of Napster in 1999, P2P file-sharing has enjoyed a dramatic rise in popularity. A 2000 study by Plonka on the University of Wisconsin campus network found that file-sharing accounted for a comparable volume of traffic to HTTP, while a 2002 study by Saroiu et al. on the University of Washington campus network found that file-sharing accounted for more than treble the volume of Web traffic observed, thus affirming the significance of P2P in the context of Internet traffic. Empirical studies of P2P traffic are essential for supporting the design of next-generation P2P systems, informing the provisioning of network infrastructure and underpinning the policing of P2P systems. The latter is of particular significance as P2P file-sharing systems have been implicated in supporting criminal behaviour including copyright infringement and the distribution of illegal pornograph
Recommended from our members
Simple network management protocol co- existence with hydrocarbon process automation communication real-time network
Hydrocarbon Process Automation Applications (HPAA) utilizes Real-time network connecting process instrumentations, controllers, and real-time logic control applications. Conventional practice is to dedicate a real-time network for process automation applications and prevent other applications from utilizing the same infrastructure. An important application that can help optimize, improve network performance, and provide rapid response time in network diagnostics and mitigation is Simple Network Management Protocol (SNMP). This paper addresses the co-existence of SNMP traffic with real-time applications. The impacts of activating this protocol with the real-time HPAA utilizing high speed Ethernet network design will be examined. Empirical data for an implemented Hydrocarbon process automation system will be used to illustrate the interdependency of application performance, traffic mix, and potential areas of improvements. The outcomes of this effort demonstrate the co-existence of SNMP with HPPA, given special considerations (i.e., bandwidth, number of applications, etc.)
Relaxing state-access constraints in stateful programmable data planes
Supporting the programming of stateful packet forwarding functions in
hardware has recently attracted the interest of the research community. When
designing such switching chips, the challenge is to guarantee the ability to
program functions that can read and modify data plane's state, while keeping
line rate performance and state consistency. Current state-of-the-art designs
are based on a very conservative all-or-nothing model: programmability is
limited only to those functions that are guaranteed to sustain line rate, with
any traffic workload. In effect, this limits the maximum time to execute state
update operations. In this paper, we explore possible options to relax these
constraints by using simulations on real traffic traces. We then propose a
model in which functions can be executed in a larger but bounded time, while
preventing data hazards with memory locking. We present results showing that
such flexibility can be supported with little or no throughput degradation.Comment: 6 page
HLOC: Hints-Based Geolocation Leveraging Multiple Measurement Frameworks
Geographically locating an IP address is of interest for many purposes. There
are two major ways to obtain the location of an IP address: querying commercial
databases or conducting latency measurements. For structural Internet nodes,
such as routers, commercial databases are limited by low accuracy, while
current measurement-based approaches overwhelm users with setup overhead and
scalability issues. In this work we present our system HLOC, aiming to combine
the ease of database use with the accuracy of latency measurements. We evaluate
HLOC on a comprehensive router data set of 1.4M IPv4 and 183k IPv6 routers.
HLOC first extracts location hints from rDNS names, and then conducts
multi-tier latency measurements. Configuration complexity is minimized by using
publicly available large-scale measurement frameworks such as RIPE Atlas. Using
this measurement, we can confirm or disprove the location hints found in domain
names. We publicly release HLOC's ready-to-use source code, enabling
researchers to easily increase geolocation accuracy with minimum overhead.Comment: As published in TMA'17 conference:
http://tma.ifip.org/main-conference
Hypersparse Neural Network Analysis of Large-Scale Internet Traffic
The Internet is transforming our society, necessitating a quantitative
understanding of Internet traffic. Our team collects and curates the largest
publicly available Internet traffic data containing 50 billion packets.
Utilizing a novel hypersparse neural network analysis of "video" streams of
this traffic using 10,000 processors in the MIT SuperCloud reveals a new
phenomena: the importance of otherwise unseen leaf nodes and isolated links in
Internet traffic. Our neural network approach further shows that a
two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide
variety of source/destination statistics on moving sample windows ranging from
100,000 to 100,000,000 packets over collections that span years and continents.
The inferred model parameters distinguish different network streams and the
model leaf parameter strongly correlates with the fraction of the traffic in
different underlying network topologies. The hypersparse neural network
pipeline is highly adaptable and different network statistics and training
models can be incorporated with simple changes to the image filter functions.Comment: 11 pages, 10 figures, 3 tables, 60 citations; to appear in IEEE High
Performance Extreme Computing (HPEC) 201
- âŠ