This work investigates the dynamic service caching, computing, and communication (3C) resource optimization for edge-cloud networks, where users, edge node, and cloud all having 3C capabilities. Based on the developed hierarchical network model, we propose a two-timescale problem aiming to minimize energy consumption under queue stability and latency and reliability constraints. A 3C resource optimization approach is then developed based on the formulated problem and Lyapunov optimization. Simulation results show that our approach can outperform the reference schemes in terms of average service delay and reliability, and can effectively provide tradeoff between energy consumption and service delay.

A topic is usually associated with a specific time, place, and person(s). Generally, topics that involve bipolar or competing viewpoints are attention getting and are thus reported in a large number of documents. Identifying the association between important persons mentioned in numerous topic documents would help readers comprehend topics more easily. In this paper, we propose an unsupervised approach for identifying bipolar person names in a set of topic documents. Specifically, we employ principal component analysis (PCA) to discover bipolar word usage patterns of person names in the documents, and show that the signs of the entries in the principal eigenvector of PCA partition the person names into bipolar groups spontaneously. To reduce the effect of data sparseness, we introduce two techniques, called the weighted correlation coefficient and off-topic block elimination. We also present a timeline system that shows the intensity and activeness development of the identified bipolar person groups. Empirical evaluations demonstrate the efficacy of the proposed approach in identifying bipolar person names in topic documents, while the generated timelines provide comprehensive storylines of topics.