Theses

Biniam Guulay :  CheesePi: Measuring Home Network Performance Using Dedicated Hardware Devices

Internet users may not get the service quality promised by their providers, and also may not know what service they can receive. When users experience poor Internet connection performance, it is not easy to identify the source of the problem. We develop CheesePi, a distributed measurement system that measures the Internet connection experience of home users based on some network performance attributes (e.g. latency, packet loss rate, and WiFi signal quality). The CheesePi runs on a Raspberry Pi (a credit card sized computer) connected to the user’s home network as a measurement agent. It is important to measure the network performance from the user’s side since it is difficult to measure each individual’s link from the operator (provider) side. Each measurement agent conducts measurement periodically without disturbing the user’s Internet quality. Measurements are conducted during big media events from SICS (Swedish Institute of Computer Science) labs and student accommodations. The measurement results show customers with an Ethernet connection experienced significantly better latency and packet loss compared to WiFi users. In most of the measurements, users at SICS lab perceived better latency and packet loss compared to the users at the student accommodation. We also quantify how customers experienced lower performance when streaming from websites which do not use CDN technology compared to the websites which do use CDN, particularly during big media events.

http://cheesepi.sics.se/StudentReports/accurate_traffic_gen.pdf

 

Rebecca Portelli :  CheesePi: Delay Characterization through TCP-based Analysis from End-to-End Monitoring

With increasing access to interconnected IP networks, people demand a faster response time from Internet services. Traffic from web browsing, the second most popular service, is particularly time-sensitive. This demands reliability and a guarantee of delivery with a good quality of service from ISPs. Additionally, the majority of the population do not have the technical background to monitor the delay themselves from their home networks, and their ISPs do not have a vantage point to monitor and diagnose network problems from the users’ perspective.

Hence, the aim of this research was to characterise the “in-protocol” network delay encountered during web browsing from within a LAN. This research presents TCP traffic monitoring performed on a client device as well as TCP traffic monitoring over both the client and the server devices separately observing an automated web client/server communication. This was followed by offline analysis of the captured traces where each TCP flow was dissected into: handshake, data transfer, and teardown phases. The aim behind such extraction was to enable characterisation of network round-trip delay as well as network physical delay, end host processing delay, web transfer delay, and packets lost as perceived by the end hosts during data transfer.

The outcome of measuring from both end devices showed that monitoring from both ends of a client/server communication results to a more accurate measurement of the genuine delay encountered when packets traverse the network than when measuring from the client-end only. Primarily, this was concluded through the ability to distinguish between the pure network delay and the kernel processing delay experienced during the TCP handshake and teardown. Secondly, it was confirmed that the two RTTs identified in a TCP handshake are not symmetrical and that a TCP teardown RTT takes longer than the TCP handshake RTT within the same TCP flow since a server must take measures to avoid SYN flooding attacks. Thirdly, by monitoring from both end devices, it was possible to identify routing path asymmetries by calculating the physical one-way delay a packet using the forward path in comparison to the physical delay of a packet using the reverse path. Lastly, by monitoring from both end devices, it is possible to distinguish between a packet that was actually lost and a packet that arrived with a higher delay than its subsequent packet during data transfer. Furthermore, utilizing TCP flows to measure the RTT delay excluding end host processing gave a better characterisation of the RTT delay as opposed to using ICMP traffic.excluding end host processing gave a better characterisation of the RTT delay as opposed to using ICMP traffic.

http://cheesepi.sics.se/StudentReports/delay_characterisation.pdf

 

Sagar Sharma: CheesePi: Measuring Home Network Performance Using Dedicated Hardware Devices

Internet users may not get the service quality promised by their providers, and also may not know what service they can receive. When users experience poor Internet connection performance, it is not easy to identify the source of the problem. We develop CheesePi, a distributed measurement system that measures the Internet connection experience of home users based on some network performance attributes (e.g. latency, packet loss rate, and WiFi signal quality). The CheesePi runs on a Raspberry Pi (a credit card sized computer) connected to the user’s home network as a measurement agent. It is important to measure the network performance from the user’s side since it is difficult to measure each individual’s link from the operator (provider) side. Each measurement agent conducts measurement periodically without disturbing the user’s Internet quality. Measurements are conducted during big media events from SICS (Swedish Institute of Computer Science) labs and student accommodations. The measurement results show customers with an Ethernet connection experienced significantly better latency and packet loss compared to WiFi users. In most of the measurements users at the SICS lab perceived better latency and packet loss compared to the users at the student accommodation. We also quantify how customers experienced lower performance when streaming from websites which do not use CDN technology compared to the websites which do use CDN, particularly during big media events.

http://cheesepi.sics.se/StudentReports/accurate_traffic_gen.pdf

 

Gustaf Lindstedt:  Efficient Scheduling of Peer to Peer Measurements

http://cheesepi.sics.se/StudentReports/gustaf.pdf

With the growing dependency on Internet connectivity in our daily lives,
monitoring connection quality to ensure a good quality of service has become
increasingly important. The CheesePi project aims to build a platform for
monitoring connection quality from the home user’s perspective. And with peer
to peer technologies becoming more prevalent the need for quality of service
monitoring between peers become more important. This thesis analyses the
problem of scheduling connection quality measurements between peers in a
network. A method is presented for scheduling measurements which make use
of statistical models of the individual links in the network based on previous
measurement data. The method applies the ADWIN1 adaptive windowing
algorithm over the models and decides a priority based on the relative window
sizes for each link. This method is evaluated against a round-robin scheduler
through simulation and is shown to provide a better scheduling than round
robin in most cases in terms of achieving the most “information gain” per
measurement iteration. The results show that for sudden changes in a network
link the scheduler prioritises measurements for that link and therefore converge
its view of the network to the new stable state more quickly than when using
round-robin scheduling. The scheduling method was developed to be practically
applicable to the CheesePi project and might effectively be deployed in real
systems running the CheesePi platform. The thesis also contains an evaluation
of two online algorithms for mean and variance as to how they react to change
in the data source from which the samples are taken.