Epidemic forwarding in mobile social networks

Recent years have witnessed the prosperity of mobile social networks, where various information is shared among mobile users through their opportunistic contacts. To investigate efficiency of information dissemination in wireless networks, epidemic models have been employed to study message forwarding delays, presuming message delivery whenever an opportunistic contact occurs. A practical concern is typically neglected, that one mobile user may only be willing to pass information onto others with social ties, rather than anyone upon contact. Under such a constraint, information dissemination may behave differently, according to the pattern of social ties that exist in the network. In this paper, we model social-aware epidemic forwarding in mobile social networks using mean-field equations, and carefully study the end-to-end unicast message propagation delays under different levels of social ties among users. Both cases of limited and unlimited message validity are considered in our models, i.e., whether relay nodes may delete a message after carrying it for some finite time T or never. Through careful theoretical analysis and empirical studies, we made a number of intriguing observations: First, the topology of social relation graphs significantly influences message forwarding delays, i.e., the more skewed the social relationship distribution is, the larger delay it results in. Second, the average delivery delay remains fairly stable with the growth of system scale, presenting a sharp contrast with the case without social awareness. Third, we observe that with a moderate choice of T, message delivery can achieve a successful ratio of almost 100% with an expected delay very close to the case of unlimited validity, signifying that a good tradeoff can be achieved between end-to-end message delivery efficiency and energy/storage overhead at the relay nodes in a network. All these provide useful guidance for efficient information dissemination protocol design in practical mobile social networks.published_or_final_versionThe 2012 IEEE International Conference on Communications (ICC), Ottawa, Canada, 10-15 June 2012. In IEEE International Conference on Communications, 2012, p. 1-

Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis

Critical size of ego communication networks

With the help of information and communication technologies, studies on the overall social networks have been extensively reported recently. However, investigations on the directed Ego Communication Networks (ECNs) remain insufficient, where an ECN stands for a sub network composed of a centralized individual and his/her direct contacts. In this paper, the directed ECNs are built on the Call Detail Records (CDRs), which cover more than 7 million people of a provincial capital city in China for half a year. Results show that there is a critical size for ECN at about 150, above which the average emotional closeness between ego and alters drops, the balanced relationship between ego and network collapses, and the proportion of strong ties decreases. This paper not only demonstrate the significance of ECN size in affecting its properties, but also shows accordance with the "Dunbar's Number". These results can be viewed as a cross-culture supportive evidence to the well-known Social Brain Hypothesis (SBH).Comment: 6 pages, 4 figures, 1 tabl