the emerging trend in the usage of legitimate search engines as proxies for address harvesting. Other observations point to the decline of harvesting activity on our sites and the existence of only a small set of hosts being responsible for a major fraction of the received spam.
In order to optimize the QoE of e-mail, our findings reveal several guidelines for webmasters to mitigate spam, e.g., i) to continue using obfuscation methods for displaying e-mail addresses on the web, e.g., by using Javascript code,ii)to restrict embedding e-mail addresses in web sites sent to legitimate browsers, and in particular not to search engine bots,iii) to rely on blacklists, e.g., provided by Project Honey Pot, to limit the likelihood of address harvesting.
9.6 Future Work
We see two main directions for future work arising from this excursion.
The first direction follows on the presented work and focuses on getting a better understanding of e-mail address harvesting. Example questions concern the number of active harvesters and their relationship to spammers and botmasters. Are there only a few major active harvesters as suggested by our work? Are spammers and harvesters the same parties? Are botnet resources rented for harvesting as they are rented for spamming? Preliminary work in this direction complemented the presented harvester study with a classification of the spamming botnets and the advertised spam campaigns. Initial results show that different botnets are used for different campaigns, which suggests that the harvested addresses are sold on the market.
The second direction aims at a first exploration of e-mail QoE. This specifically concerns studying factors influencing QoE. Following the QoE definition from Sec-tion 3.1, we see the following influence factors.
• E-mail currently provides an unreliable communication service that doesn’t always inform the sender about unsuccessful e-mail delivery. We therefore ar-gue thereliabilityof e-mail to impact it’s utility. Undelivered e-mail without further notification is arguably perceived as annoying.
• Further, the timeliness of delivery denotes a second factor concerning e-mail delivery. Based on prior experience in using e-e-mail, users will arguably have expectation regarding delivery performance. Failures in meeting these expectations will impact QoE.
• The presence of unsolicited e-mail—i.e., spam as studied in this excursion—
contributes annoyance when using e-mail. Unsolicited e-mail becomes partic-ularly annoying when users receive immediate notifications of new e-mail, e.g.,
as often used on mobile phones and PCs. Spam further impactsuser efficiency by requiring automatic or manual filtering.
Future work should deepen this understanding and further explore other communi-cation services (e.g., chat services, in particular mobile chat services that are gaining popularity). A detailed investigation of QoE impacts not only touches on currently unexplored ground, but can also provide useful insights fori) service monitoring and operation andii) the design of future communication services.
10
Conclusion and Outlook
10.1 Summary
The Internet has become an essential part of the lives of millions of people and an invaluable asset to businesses. As an emerging trend, data storage and processing is shifting to the Cloud (e.g., Google Apps, or Cloud gaming), making users more and more dependent on the network to perform their daily activities. Despite the crucial importance of Internet services, they remain susceptible to bad service quality. A particular factor influencing service quality is buffering at various layers.
This thesis assessed the impact of buffering on Quality of Experience (QoE). QoE is an active research area aiming to quantify the users’ perception of applications and Internet services. This is challenging since the users’ perception is subjective. We tackled this challenge using a multi-disciplinary approach that combined QoE and networking research to take a cross-layer perspective on network and application buffering.
Network buffering occurs in hosts, switches, and routers throughout the Internet. It impacts network performance by contributing delays, jitter, and packet losses. We illustrated that packet losses, mainly caused by congestion and overflowing buffers, have detrimental effects on video QoE. Motivated by this observation, we discussed Scalable Video Coding (SVC) as means for QoE management to optimize video QoE in phases of congestion. SVC allows for seamless adaption to varying network conditions. In this setting, we studied bandwidth reductions achieved by either i) video resolution reductions, ii) image quality reductions, or iii) frame rate reduc-tions. We evaluated the impact of these changes on common full-reference QoE
metrics. This evaluation of bandwidth adaptation mechanisms showed that reso-lution changes yield better overall QoE scores and higher bandwidth savings than frame rate reductions. Further, we evaluated QoE impacts of model based packet loss generators used in QoE studies. We showed that model choice impacts QoE and proposed a new fitting technique that is optimized for replicating aspects relevant to video QoE.
The size of network buffers influences network performance by controlling the level of introduced delay, jitter, and packet loss. Surprisingly, even after decades of research and operational experience, ‘proper’ buffer dimensioning remains an unresolved and controversially debated topic. Most recently, this debate has focused on the negative effects of large buffers. This lead to proposed Internet engineering changes, despite the absence of sufficient empirical evidence. As an understanding of buffering ef-fects is crucial before altering important engineering aspects, we broadly studied the impact of buffer sizing on QoE in an extensive study involving relevant user applica-tions (e.g., voice, video, and web browsing), real hardware, and realistic workload.
This study showed that the dominant factor for the QoE is the level of competing network workload. That is, workloads in which the competing flows keep the queue at the bottleneck link filled (e.g., via many short-lived and therefore not congestion controlled flows) have much larger impact on QoE than buffer size. The study addi-tionally showed that exacerbated (bloated) buffers have a significant effect on QoE metrics. Reasonable buffer sizes that follow standard sizing guidelines, however, have a significant effect on QoS, but impact QoE metrics only marginally. This lead us to conclude that limiting congestion, e.g., via QoS mechanisms or over-provisioning, may actually yield more immediate improvements in QoE than efforts to reduce buffering.
Application buffering is used to compensate for performance variations, e.g., orig-inating from network buffering. One example is the proprietary retransmission scheme deployed in a major IPTV system. This thesis revealed insights in the func-tioning of this proprietary scheme. We showed that the resend mechanism deployed by Set-Top-Boxes in a major IPTV network is based on a simple buffer-based resend scheme that offers drastic QoE improvements for low loss rates. When QoE metrics are used by ISPs for QoE monitoring inside the network, they do not account for error recovery mechanisms at the edge and thus are prone to QoE mispredictions.
By revealing insights into the resend mechanism used by a major IPTV network, we pave the way for improved QoE metrics accounting for error recovery mechanism.
To optimize Web QoE, we performed a hit rate analysis of caching schemes by fo-cusing on YouTube video popularities. The thesis contributed a new caching scheme that offers higher cache hit rates than traditional Least Recently Used caches.
Finally, we broadened the view of QoE by discussing spam as major QoE determinant in e-mail. A large-scale study conducted over 3.5 years revealed insights into address harvesting as the origin of spam and proposes mechanisms for spam mitigation that can help to improve e-mail QoE. The study showed that harvester can often be