In this paper, we investigate the evolution of an optical network architecture and discuss the future direction of research on optical network design and control. We review existing research on optical network design and control and present some open challenges. One of the important open challenges lies in multilayer resource optimization including IT and optical network resources. We propose an adaptive joint optimization method of IT resources and optical spectrum under time-varying traffic demand in optical networks while avoiding an increase in operation cost. We formulate the problem as mixed integer linear programming and then quantitatively evaluate the trade-off relationship between the optimality of reconfiguration and operation cost. We demonstrate that we can achieve sufficient network performance through the adaptive joint optimization while suppressing an increase in operation cost.

Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Defined Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable flexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC traffic flow measurement data provided by INT, the framework firstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classifier to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.

Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.

Service function chain (SFC) is a series of ordered virtual network functions (VNFs) for processing traffic flows in the virtualized networking environment of future networks. In this paper, we present a mathematical model and dynamic programing based scheme for solving the problem of SFC placement on substrate networks equipped with network function virtualization (NFV) capability. In this paper, we first formulate the overall cost of SFC placement as the combination of setup cost and operation cost. We then formulate the SFC placement problem as an integer linear programing (ILP) model with the objective of minimizing the overall cost of setup and operation, and propose a delay aware dynamic programming based SFC placement scheme for large networks. We conduct numeric simulations to evaluate the proposed scheme. We analyze the cost and performance of network under different optimization objectives, with and without keeping the order of VNFs in SFC. We measure the success rate, resources utilization, and end to end delay of SFC on different topologies. The results show that the proposed scheme outperforms other related schemes in various scenarios.

We propose an effective technique for estimation of targets by ground penetrating radar (GPR) using model-based compressive sensing (CS). We demonstrate the technique's performance by applying it to detection of buried landmines. The conventional CS algorithm enables the reconstruction of sparse subsurface images using much reduced measurement by exploiting its sparsity. However, for landmine detection purposes, CS faces some challenges because the landmine is not exactly a point target and also faces high level clutter from the propagation in the medium. By exploiting the physical characteristics of the landmine using model-based CS, the probability of landmine detection can be increased. Using a small pixel size, the landmine reflection in the image is represented by several pixels grouped in a three dimensional plane. This block structure can be used in the model based CS processing for imaging the buried landmine. The evaluation using laboratory data and datasets obtained from an actual mine field in Cambodia shows that the model-based CS gives better reconstruction of landmine images than conventional CS.

Basic structural characteristics, which are useful in modular synthesis based on strongly connected state machines, of SMA/LBFC nets are discussed in this paper. A more convincing and direct proof of the equivalence of two structural characterization of the class of Petri nets is given. This proof will give clearer view of the structural characteristics of LBFC/SMA nets. On the other hand, however, the structural characteristics are not practically amenable in application to modular synthesis of SMA nets from a given set of SCSM's since all possible SCSM's should be examined for the verification of the given conditions. The later half of this paper is devoted into strengthening the results, i. e. , in composition of an SMA net from a given set of SCSM's the condition is also satisfied in any SCSM generated by composition.