A peer-to-peer delivery system for internet short video sharing

In this thesis, we considered the effect of the content delivery network architecture on the popular short video sharing websites such as YouTube. The high number of users demanding videos impacts YouTube scalability which requires a new content delivery structure. Considering the high performance of P2P overlay networks, we propose an efficient peer-to-peer based system for short video sharing in the Internet in which all participant peers are responsible to distribute video replicas they have stored. This system comprises of a BitTorrent like network and a central media streaming server. To proficiently utilize P2P in our system, we propose some important approaches including an efficient and reliable indexing scheme, an efficient downloading strategy, a reliable content distribution mechanism, and a fairness policy. The simulations results demonstrate that the proposed system significantly increases client peers download speed while reduces the server workload and the startup delay for an improved playback quality.

A peer-to-peer delivery system for internet short video sharing

In this thesis, we considered the effect of the content delivery network architecture on the popular short video sharing websites such as YouTube. The high number of users demanding videos impacts YouTube scalability which requires a new content delivery structure. Considering the high performance of P2P overlay networks, we propose an efficient peer-to-peer based system for short video sharing in the Internet in which all participant peers are responsible to distribute video replicas they have stored. This system comprises of a BitTorrent like network and a central media streaming server. To proficiently utilize P2P in our system, we propose some important approaches including an efficient and reliable indexing scheme, an efficient downloading strategy, a reliable content distribution mechanism, and a fairness policy. The simulations results demonstrate that the proposed system significantly increases client peers download speed while reduces the server workload and the startup delay for an improved playback quality.

An Efficient Indexing Scheme for Network Traffic Collection and Retrieval System

Historical network traffic retrieval, both at the packet and flow level, has been applied in many fields of network security, such as network traffic analysis and network forensics. To retrieve specific packets from a vast number of packet traces, it is an effective solution to build indexes for the query attributes. However, it brings challenges of storage consumption and construction time overhead for packet indexing. To address these challenges, we propose an efficient indexing scheme called IndexWM based on the wavelet matrix data structure for packet indexing. Moreover, we design a packet storage format based on the PcapNG format for our network traffic collection and retrieval system, which can speed up the extraction of index data from packet traces. Offline experiments on randomly generated network traffic and actual network traffic are performed to evaluate the performance of the proposed indexing scheme. We choose an open-source and widely used bitmap indexing scheme, FastBit, for comparison. Apart from the native bitmap compression method Word-Aligned Hybrid (WAH), we implement an efficient bitmap compression method Scope-Extended COMPAX (SECOMPAX) in FastBit for performance evaluation. The comparison results show that our scheme outperforms the selected bitmap indexing schemes in terms of time consumption, storage consumption and retrieval efficiency.

ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data

Recently, various environmental data, such as microdust pollution, temperature, humidity, etc., have been continuously collected by widely deployed Internet of Things (IoT) sensors. Although these data can provide great insight into developing sustainable application services, it is challenging to rapidly retrieve such data, due to their multidimensional properties and huge growth in volume over time. Existing indexing methods for efficiently locating those data expose several problems, such as high administrative cost, spatial overhead, and slow retrieval performance. To mitigate these problems, we propose a novel indexing scheme termed ST-Trie, for efficient retrieval over spatiotemporal IoT environment data. Given IoT sensor data with latitude, longitude, and time, the proposed scheme first converts the three-dimensional attributes to one-dimensional index keys. The scheme then builds a trie-based index, consisting of internal nodes inserted by the converted keys and leaf nodes containing the keys and pointers to actual IoT data. We leverage this index to process various types of queries. In our experiments with three real-world datasets, we show that the proposed ST-Trie index outperforms existing approaches by a substantial margin regarding response time. Furthermore, we show that the query processing performance via ST-Trie also scales very well with an increasing time interval. Finally, we demonstrate that when compressed, the ST-Trie index can significantly reduce its space overhead by approximately a factor of seven.