Disaggregated data centers (DCs) can offer high flexibility for resource allocation; hence, their resource utilization can be significantly improved. However, communications between different types of resources, in particular between CPU and memory, in fully disaggregated DCs face severe problems in terms of stringent requirements for ultra-low latency and ultra-high transmission bandwidth. Optical fiber communication is promising to provide high capacity and low latency, but it is still challenging for the state-of-the-art optical technologies to meet the requirements of fully disaggregated DCs. In this article, different levels of resource disaggregation are investigated. For fully disaggregated DCs, two architectural options are presented, where optical interconnects are necessary for the CPU-memory communications. We review the state-of-the-art optical transmission and switching technologies, and analyze pros and cons of their applicability in the disaggregated DCs. The results reveal that resource disaggregation does improve the resource utilization in DCs. However, the bandwidth provided by the state-of-the-art technologies is not always sufficient for fully disaggregated DCs. It calls for further advances in optical communications to fully utilize the advantages of fully disaggregated DCs.

Network slicing is a key concept in 5G networking. It enables an infrastructure provider (InP) to support heterogeneous services over a common platform by creating a customized slice for each one of them. Once in operation, the slices can be dynamically scaled up/down to match the variation of service requirements. Although an InP generates revenue by accepting a slice request, however it might need to pay a penalty (proportional to the level of service degradation) if a slice cannot be scaled up when required. Hence, it becomes crucial to decide which slice requests should be accepted in order to maximize the net profit of an InP. This paper presents a slice admission strategy based on big data analytics (BDA) predictions. The intuition is to accept a slice request only when it is estimated that no service degradation will take place for both the incoming slice request and the slices already in operation. In this way, the penalty paid by an InP is contained, with beneficial effects on the overall net profit. Apart from simulations, the performance of the proposed admission policy has also been evaluated using emulation. Simulation results show that, in the presence of a high penalty due to service degradation, using BDA predictions brings up to 50.7% increase in profit, as compared to a slice admission policy without BDA. Emulation results for a small network scenario show a profit increase of up to 383% with only a small impact on the slice provisioning time (i.e., due to the processing of BDA predictions).

Fog computing is expected to be integrated with communication infrastructure, giving rise to the concept of fog-enhanced radio access networks (FeRANs) to support various mission-critical applications. Such architecture brings computation capabilities closer to end users, thereby reducing the communication latency to access services. In the context of FeRAN, service migration is needed to tackle limited resources in a single fog node and to provide continuous service for mobile end users. To support service migration, high capacity and low latency are required in mobile backhaul networks. Passive optical networks can be a promising solution for such mobile back-haul, in which bandwidth is shared by both migration traffic and that which is not associated with service migration. In this paper, we propose a bandwidth slicing mechanism, in which the bandwidth can be provisioned to the migration traffic and non-migration traffic dynamically and effectively to meet their different delay requirements. Simulation results verify that the proposed delay-aware bandwidth slicing scheme can handle the migration traffic properly, i.e., sending it within a required time threshold, while limiting the impact of the migration traffic on the latency and jitter of the non-migration traffic, particularly that with high priority.

Resource utilization of modern data centers is significantly limited by the mismatch between the diversity of the resources required by running applications and the fixed amount of hardwired resources (e.g., number of central processing unit CPU cores, size of memory) in the server blades. In this regard, the concept of function disaggregation is introduced, where the integrated server blades containing all types of resources are replaced by the resource blades including only one specific function. Therefore, disaggregated data centers can offer high flexibility for resource allocation and hence their resource utilization can be largely improved. In addition, introducing function disaggregation simplifies the system upgrade, allowing for a quick adoption of new generation components in data centers. However, the communication between different resources faces severe problems in terms of latency and transmission bandwidth required. In particular,the CPU-memory interconnects in fully disaggregated data centers require ultra-low latency and ultra-high transmission bandwidth in order to prevent performance degradation for running applications. Optical fiber communication is a promising technique to offer high capacity and low latency, but it is still very challenging for the state-of-the-art optical transmission technologies to meet the requirements of the fully disaggregated data centers. In this paper, different levels of function disaggregation are investigated. For the fully disaggregated data centers, two architectural options are presented, where optical interconnects are necessary for CPU-memory communications. We review the state-of-the-art optical transmission technologies and carry out performance assessment when employing them to support function disaggregation in data centers. The results reveal that function disaggregation does improve the efficiency of resource usage in the data centers, although the bandwidth provided by the state-of-the-art optical transmission technologies is not always sufficient for the fully disaggregated data centers. It calls for research in optical transmission to fully utilize the advantages of function disaggregation in data centers.

Connection provisioning in Wavelength Division Multiplexing (WDM) networks needs to account for a number of crucial parameters. On the one hand, operators need to ensure the connection availability requirements defined in Service Level Agreements (SLAs). This is addressed by selecting an appropriate amount of backup resources and recovery strategies for the connections over which services are provisioned. Services requiring less strict availability requirements can be routed over unprotected lightpaths. Services with more strict availability requirements are provisioned over protected lightpaths in order to cope with possible failures in the network. Another important aspect to consider during the provisioning process is energy efficiency. Green strategies leverage on setting network devices in Sleep Mode (SM) or Active Mode (AM) depending on whether or not they are needed to accommodate traffic. However, frequent power state changes introduce thermal fatigue which in turn has a negative effect on the device lifetime. Finally, in multi-period traffic scenarios, it is also important to minimize the number of reconfigurations of lightpaths already established in the network in order to avoid possible traffic disruptions at higher layers. The work presented in this paper tackles the connection provisioning paradigm in an optical backbone network with a multi-period traffic scenario. More specifically the paper looks into the interplay among (i) energy efficiency, (ii) thermal fatigue, and (iii) lightpath reconfiguration aspects. To this end, the Energy and Fatigue Aware Heuristic with Unnecessary Reconfiguration Avoidance (EFAH-URA) is introduced, showing that it is possible to balance the three aspects mentioned above in an efficient way. When compared to the pure energy-aware strategies, EFAH-URA significantly improves the average connection availability for both unprotected and protected connections. On the other hand, it is done at the expense of reduced energy saving.

Content Delivery Networks (CDNs) are crucial for enabling delivery of services that require high capacity and low latency, primarily through geographically-diverse content replication. Optical networks are the only available future-proof technology that meets the reach and capacity requirements of CDNs. However, the underlying physical network infrastructure is vulnerable to various security threats, and the increasing importance of CDNs in supporting vital services intensifies the concerns related to their robustness. Malicious attackers can target critical network elements, thus severely degrading network connectivity and causing large-scale service disruptions. One way in which network operators and cloud computing providers can increase the robustness against malicious attacks is by changing the topological properties of the network through infrastructure upgrades. This work proposes a framework for CDN infrastructure upgrade that performs sparse link and replica addition with the objective of maximizing the content accessibility under targeted link cut attacks. The framework is based on a newly defined content accessibility metric denoted as mu-ACA which allows the network operator to gauge the CDN robustness over a range of attacks with varying intensity. Two heuristics, namely Content-Accessibility Aware Link Addition Heuristic (CAA-LAH), and Content-Accessibility-Aware Replica Addition Heuristic (CAA-RAH) are developed to perform strategic link and replica placement, respectively, and hamper attackers from disconnecting users from the content even in severe attack scenarios. Extensive experiments on real-world reference network topologies show that the proposed framework effectively increases the CDN robustness by adding a few links or replicas to the network.

Network slicing enables an infrastructure provider (InP) to support heterogeneous 5G services over a common platform (i.e., by creating a customized slice for each service). Once in operation, slices can be dynamically scaled up/down to match the variation of their service requirements. An InP generates revenue by accepting a slice request. If a slice cannot be scaled up when required, an InP has to also pay a penalty (proportional to the level of service degradation). It becomes then crucial for an InP to decide which slice requests should be accepted/rejected in order to increase its net profit. This paper presents a slice admission strategy based on reinforcement learning (RL) in the presence of services with different priorities. The use case considered is a 5G flexible radio access network (RAN), where slices of different mobile service providers are virtualized over the same RAN infrastructure. The proposed policy learns which are the services with the potential to bring high profit (i.e., high revenue with low degradation penalty), and hence should be accepted. The performance of the RL-based admission policy is compared against two deterministic heuristics. Results show that in the considered scenario, the proposed strategy outperforms the benchmark heuristics by at least 23%. Moreover, this paper shows how the policy is able to adapt to different conditions in terms of 1) slice degradation penalty versus slice revenue factors, and 2) proportion of high versus low priority services.

A failure in optical backbone network can cause tremendous consequences as a substantial number of connections often each carrying a large amount of data can be interrupted. Therefore, high reliability performance is essential for the network operators. Many existing works that aim at improving network reliability performance implicitly assume that the lifetime of devices is constant and independent of the traffic load. However, the reliability performance of a device is related to its occupancy. For example, the failure rate of erbium doped fiber amplifier (EDFA) can be expressed as a function of the number of amplified wavelengths. On the other hand, the choice of routing and wavelength assignment (RWA) algorithm impacts the link load and, as a consequence, can influence the number of EDFA failures in the network. In this paper we examine how RWA can impact the failure reparation related network operational costs. Several types of RWA approaches are considered, namely load-balancing, energy-awareness, and reliability-awareness. Among all the considered RWA algorithms, the reliability-aware RWA (RA-RWA) approach leverages on EDFA reliability profile to reduce the number of EDFA failures in the network and the related operational costs. The simulation results show that the RWA algorithm impacts in a significant way the operational costs caused by EDFA failures. The cost associated with reparation of an EDFA decreases by 7.8% (in case of RA-RWA) and increases by up to 40% (in case of a load-balancing approach) compared to the classical Shortest Path (SP) approach. Moreover, the cost caused by connection rerouting due to link unavailability triggered by EDFA failure exhibits a 20% decrease (RA-RWA) and up to 94% increase (energy-aware algorithm). We also analyze some key network performance metrics that may be affected by RWA, including blocking probability, link occupancy distribution, and path length.

This chapter introduces a techno-economic framework that provides a complete market analysis of the various business actors for any type of mobile access network deployments. It presents a case study where the proposed business feasibility framework is applied. The chapter presents a comprehensive techno-economic framework for estimating the total cost of ownership (TCO) of a backhaul network segment as well as for analyzing the business viability of a given wireless network deployment. It focuses on two backhaul technologies: microwave and fiber. The chapter addresses the framework proposed specifically only the backhaul segment, but it is general enough to also be applied to the other 5G transport solutions. It also presents the TCO module used in the proposed framework. The module covers both the Capital Expenditure and the Operational Expenditure aspects of the backhaul segment. The backhaul network is responsible for aggregating the users' traffic from the wireless access to the metro/backbone segment of the network.