A survey on OFDM-based elastic core optical networking

Orthogonal frequency-division multiplexing (OFDM) is a modulation technology that has been widely adopted in many new and emerging broadband wireless and wireline communication systems. Due to its capability to transmit a high-speed data stream using multiple spectral-overlapped lower-speed subcarriers, OFDM technology offers superior advantages of high spectrum efficiency, robustness against inter-carrier and inter-symbol interference, adaptability to server channel conditions, etc. In recent years, there have been intensive studies on optical OFDM (O-OFDM) transmission technologies, and it is considered a promising technology for future ultra-high-speed optical transmission. Based on O-OFDM technology, a novel elastic optical network architecture with immense flexibility and scalability in spectrum allocation and data rate accommodation could be built to support diverse services and the rapid growth of Internet traffic in the future. In this paper, we present a comprehensive survey on OFDM-based elastic optical network technologies, including basic principles of OFDM, O-OFDM technologies, the architectures of OFDM-based elastic core optical networks, and related key enabling technologies. The main advantages and issues of OFDM-based elastic core optical networks that are under research are also discussed

Distributed complex event recognition

Complex Event Recognition (CER) has emerged as a prominent technology for detecting situations of interest, in the form of query patterns, over large streams of data in real-time. Thus, having query evaluation mechanisms that minimize latency is a shared desiderata. Nonetheless, the evaluation of CER queries is well known to be computationally expensive. Indeed, such evaluation requires maintaining a set of partial matches which grows super-linearly in the number of processed events. While most prominent solutions for CER run in a centralized setting, this has proved inefficient for Big Data requirements, where it is necessary to scale the system to cope with an increasing arrival rate of events while maintaining a stable throughput. To overcome these issues, we propose a novel distributed CER system that focuses on the efficient evaluation of a large class of complex event queries, including n-ary predicates, time windows, and partition-by event correlation operator. This system uses a state-of-the-art automaton-based distributed algorithm that circumvents the super-linear partial match problem. Moreover, in the presence of heavy workloads, the system can scale-out by increasing the number of processing units with little overhead. We additionally provide a proof of correctness of the algorithm. We experimentally compare our system against the state-of-the-art sequential CER engine that inspired our work and show that our system outperform its predecessor in the presence of queries with complex predicates. Furthermore, we show that, in the presence of Big Data requirements, our system performance is overall better