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Master Thesis: Optimizing content dissemination in federated online social networks
Title:
Optimizing content dissemination in federated online social
networks
Description:
In recent years, federated online social networks are gaining
popularity. However, along with the increase of user numbers, the
content dissemination problems of federated online social networks
have become more and more obvious. In this thesis, we focus on
optimizing the content dissemination in federated online social
networks by reducing the whole network traffic and the traffic load
for individual small servers.
This work proposed Mufesa
(Multilevel Feedback Queue Scheduling Algorithm), a solution that can
classify different types of contents and then transmit them using
various strategies. Specifically, Mufesa classifies the contents into
four categories according to their timeliness requirements and their
size and processes these contents in four job queues with different
priorities. Each queue has its own scheduling algorithm. Instant
messages and Voice/Video-Calls are those contents that should be sent
immediately and will be put into the queues with the highest priority.
There are two queues with the highest priority. The one using the
Roundrobin (RR) Scheduling is designed for instant message and its
subtypes. The one without time slice is for Voice/Video-Calls. Posts,
such as images, videos, texts and so on, are those contents that have
relatively lower priority than instant messages and will come to the
queue with the middle priority. Size-based transmission approach will
be used in this queue. Other OSN features such as like, follow, report
and so on will come to the queue with the low priority, but can be
piggybacked by contents in other queues. Besides, the queues with the
middle and low priority have different timers.
We have
implemented Mufesa in a federated online social network called
PeerTube to provide a proof of concept. Our evaluation using real
datasets shows that Mufesa can efficiently reduce the amount of
traffic in the whole network by 31.05%, can minimize the traffic load
for small home servers and is also able to reduce the number of
requests by 13.65%.
Keywords: Federated online social networks,
content dissemination, content type classification, timer approach,
piggybacking, PeerTube
Supervisor: Sebastian Göndör [1]
Type: Master Thesis
Duration: 6 months

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