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

 

Urban Petterson: Measuring VoIP Quality of Experience using the Raspberry Pis

For some users using the Internet is not the smooth experience they desire. Too often they are met by websites loading slowly, phone calls that stutter and video that keeps on buffering. Figuring out the cause of these problems can prove difficult not only to the average user but also to operators and regulators. As a solution, CheesePi was developed. It is a distributed network measurement platform running on a Raspberry Pi. It measures features of a user’s Internet connection by running network tools in a periodic fashion. The measurements are performed without interfering with the users’ Internet quality.A module for measuring VoIP quality between two Pis was developed. It was built using an existing VoIP tool and the ITU-T E-model, a computational model capable of turning VoIP quality features into a human readable estimate of Quality of Experience. The E-model produces values from 0 to 93.The module was tested using four sites. One site showed sudden decreases in quality when calling, from 92 to 82. No other site showed this kind of behaviour. This implies that the data had to use an inferior route due to congestion.

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

 

 

Gustaf Lindstedt:  Efficient Scheduling of Peer to Peer Measurements

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

(Draft above 🙂 )