End-to-End Provisioning of Latency and Availability Constrained 5G Services

We address a key challenge of 5G networks by proposing a strategy for the resource-efficient and end-to-end allocation of compute and connectivity resources in a dynamic 5G service provisioning scenario, such that the service latency and availability requirements are guaranteed. Our heuristic algorithm shows that resource efficiency is significantly improved by processing services in the large core data centers (DCs) with a rich amount of compute resources and exploiting the benefits of traffic grooming over the metro and core fiber links. Moreover, our resource-efficient provisioning algorithm avoids possible violation of the service availability requirements caused by reaching the central DC locations by adding backup connectivity resources. Our simulation results demonstrate a resource efficiency improvement reflected by lowering the service blocking probability by up to four orders of magnitude compared to the conventional service provisioning methods utilizing distributed small DCs

A service-oriented hybrid access network and clouds architecture

Many telecom operators are deploying their own cloud infrastructure with the two-fold objective of providing cloud services to their customers and enabling network function virtualization. In this article we present an architecture we call SHINE, which focuses on orchestrating cloud with heterogeneous access and core networks. In this architecture intra and inter DC connectivity is dynamically controlled, maximizing the overall performance in terms of throughput and latency while minimizing total costs. The main building blocks are: a future-proof network architecture that can scale to offer potentially unlimited bandwidth based on an active remote node (ARN) to interface end-users and the core network; an innovative distributed DC architecture consisting of micro-DCs placed in selected core locations to accelerate content delivery, reducing core network traffic, and ensuring very low latency; and dynamic orchestration of the distributed DC and access and core network segments. SHINE will provide unprecedented quality of experience, greatly reducing costs by coordinating network and cloud and facilitating service chaining by virtualizing network functions.Peer ReviewedPostprint (author’s final draft

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Disaster Resilient Optical Core Networks

During the past few years, the number of catastrophic disasters has increased and its impact sometimes incapacitates the infrastructures within a region. The communication network infrastructure is one of the affected systems during these events. Thus, building a resilient network backbone is essential due to the big role of networks during disaster recovery operations. In this thesis, the research efforts in building a disaster-resilient network are reviewed and open issues related to building disaster-resilient networks are discussed. Large size disasters not necessarily impact the communication networks, but instead it can stimulate events that cause network performance degradation. In this regard, two open challenges that arise after disasters are considered one is the short-term capacity exhaustion and the second is the power outage. First, the post-disaster traffic floods phenomena is considered. The impact of the traffic floods on the optical core network performance is studied. Five mitigation approaches are proposed to serve these floods and minimise the incurred blocking. The proposed approaches explore different technologies such as excess or overprovisioned capacity exploitation, traffic filtering, protection paths rerouting, rerouting all traffic and finally using the degrees of freedom offered by differentiated services. The mitigation approaches succeeded in reducing the disaster induced traffic blocking. Second, advance reservation provisioning in an energy-efficient approach is developed. Four scenarios are considered to minimise power consumption. The scenarios exploit the flexibility provided by the sliding-window advance reservation requests. This flexibility is studied through scheduling and rescheduling scenarios. The proposed scenarios succeeded in minimising the consumed power. Third, the sliding-window flexibility is exploited for the objective of minimising network blocking during post-disaster traffic floods. The scheduling and rescheduling scenarios are extended to overcome the capacity exhaustion and improve the network blocking. The proposed schemes minimised the incurred blocking during traffic floods by exploiting sliding window. Fourth, building blackout resilient networks is proposed. The network performance during power outages is evaluated. A remedy approach is suggested for maximising network lifetime during blackouts. The approach attempts to reduce the required backup power supply while minimising network outages due to limited energy production. The results show that the mitigation approach succeeds in keeping the network alive during a blackout while minimising the required backup power