Human mobility has been empirically observed to exhibit Lévy flight
characteristics and behaviour with power-law distributed jump size. The fundamental
mechanisms behind this behaviour has not yet been fully explained. In this
paper, we propose to explain the Lévy walk behaviour observed in human
mobility patterns by decomposing them into different classes according to
the different transportation modes, such as Walk/Run, Bike, Train/Subway or
Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing
approximately 10 and 20 million GPS samples with transportation mode information.
We show that human mobility can be modelled as a mixture of different transportation
modes, and that these single movement patterns can be approximated by a lognormal
distribution rather than a power-law distribution. Then, we demonstrate that
the mixture of the decomposed lognormal flight distributions associated with
each modality is a power-law distribution, providing an explanation to the
emergence of Lévy Walk patterns that characterize human mobility patterns.
As a promising new technology with the unique properties like high efficiency, scalability and fault tolerance, Peer-to-Peer (P2P) technology is used as the underlying network to build new Internetscale applications. However, one of the well known issues in such an application (for example WWW) is that the distribution of data popularities is heavily tailed with a Zipf-like distribution. With consideration of the skewed popularity we adopt a proactive caching approach to handle the challenge, and focus on two key problems: where(i.e. the placement strategy: where to place the replicas) and how(i.e. the degree problem: how many replicas are assigned to one specific content)? For the where problem, we propose a novel approach which can be generally applied to structured P2P networks. Next, we solve two optimization objectives related to the how problem: MAX_PERF and MIN_COST. Our solution is called PoPCache, and we discover two interesting properties: (1) the number of replicas assigned to each content is proportional to its popularity; (2) the derived optimal solutions are related to the entropy of popularity. To our knowledge, none of the previous works has mentioned such results. Finally, we apply the results of PoPCache to propose a P2P base web caching, called as WebPoPCache. By means of web cache trace driven simulation, our extensive evaluation results demonstrate the advantages of PoPCache and Web-PoPCache.
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