Quantum key distribution (QKD) networking over wavelength division multiplexing (WDM) network infrastructure provides a new paradigm for secure communications across the networks. With limited wavelength resources in existing WDM networks, how to efficiently schedule QKD to provision sufficient secret keys over WDM networks becomes a new challenge. This study proposes a new time-scheduled scheme in combination with quantum key pool (QKP) technique to overcome this challenge. A new architecture of QKD-over-WDM networks is presented, in which QKPs are constructed to decouple secret-key resources from the underlying infrastructure for timely secret-key provisioning. In the time-scheduled scheme, routing, wavelength and time-slot allocation (RWTA) problem is proposed for efficient QKP construction (QKPC) over WDM networks, in which three sub-problems (i.e., secret-key consumption, time-slot allocation, and time-slot continuity) are discussed respectively. An integer linear programming (ILP) model and a novel joint path-and-link -based RWTA (JPL-RWTA) heuristic algorithm are designed to solve the RWTA problem, in which the three sub-problems are addressed together. Simulation results indicate that the ILP model and JPL-RWTA algorithm are effective approaches to obtain the positive balance between resource utilization of QKPC (RUQKPC) and successful probability of QKPC (SPQKPC). Moreover, the SPQKPC can be increased at the cost of reducing the flexibility of secret-key consumption, the uniformity of time-slot allocation, and the proportion of time-slot continuous QKPC (TC-QKPC) to time-slot discrete QKPC (TD-QKPC) requests.

Following successful adoption of cloud computing, many service providers (SPs) are now using high-performance Virtual Machines (VMs) located in large datacenters owned by public cloud infrastructure providers to deploy their virtual network functions (VNFs). Since using these VMs has a cost depending on utilization time, a complex problem of VNF placement and scheduling (VPS) must be addressed to achieve satisfactory network performance (e.g., latency) while minimizing the cost paid to lease VMs. In this study, a cost-efficient VPS scheme (CEVPS) is proposed to address the VPS problem in public cloud networks considering dynamic requests of ordered sequences of VNFs. Our CE-VPS scheme goes beyond existing solutions as it models some important practical aspects such as an additional latency incurred by booting a VM and installing a VNF instance. Also, CE-VPS considers that VNFs can be multi-threaded or single-threaded, and that their throughput as a function of allocated computing resources must be modeled differently. CEVPS is formulated as a mixed inter linear program (MILP) and also as an efficient heuristic algorithm. CE-VPS achieves lower cost and latency than conventional Best-Availability and Cost- Efficient Proactive VNF Placement schemes, and a better tradeoff between resource consumption and latency performance than a conventional Low-Latency scheme.

Data transmission with optical fiber is vulnerable to eavesdropping. Moreover, conventional key distribution technology suffers from increasing computational power and upgraded attack algorithms. To address these issues, quantum key distribution (QKD), a quantum technology that secures secret information (such as a cryptographic key) exchange between two parties, can be used to guarantee secure data transmission. Integrating QKD into existing wavelength division multiplexing optical networks has been verified through a series of experiments, which contribute to ensuring network security and saving fiber resources. This paper addresses the resource assignment problem in QKD-enabled optical networks. First, a QKD-enabled optical network architecture is introduced. A small fraction of wavelength channels are segmented into multiple time slots with optical time division multiplexing technology to construct quantum key channels (QKChs) and measuring-basis channels, and then the remaining wavelengths can construct traditional data channels. Second, a static routing, wavelength, and time-slot assignment (RWTA) strategy is proposed and verified by the integer linear programming formulation and a heuristic algorithm. In the RWTA, QKChs are assigned for service requests according to the security levels specified by relevant key-updating periods. Thus, the secret keys for data encryption can update periodically to enhance security. Simulation results indicate that there is a trade-off between security (i.e., security levels and security-level types) and resource utilization.

With the growing popularity of 5G mobile communications, cloud and fog computing, 4K video streaming, etc., population distribution and migration have increasing influence on traffic distribution in metro elastic optical networks (EONs). Traffic distribution is further diversified according to people’s tendency to use network services in different places at different times. We use the term “tidal traffic” to represent traffic distribution with strong disequilibrium in the time and space dimensions. Note that tidal traffic can potentially result in low bandwidth utilization and weak service capability, as network resources are not allocated properly. To address this problem, in this study, we first analyze traffic characteristics of access networks and metro networks, and mathematically formulate an onion tidal traffic model (OTTM) in EONs. Second, based on the traditional routing and spectrum allocation (RSA) algorithm, which provides end-to-end connection by allocating frequency slot resources in EONs, we propose two algorithms to enhance bandwidth efficiency based on the OTTM model. We call them pre-detour RSA (PD-RSA) and pre-detour $k$-shortest paths RSA (PDK-RSA). Next, we analyze the shortcomings of a benchmark algorithm named min-hop $k$-shortest paths RSA (MHK-RSA) under tidal traffic, and compare PD-RSA and PDK-RSA with MHK-RSA via simulation. Simulation results show that PD-RSA and PDK-RSA can effectively reduce at least 26% and 18% of blocking probability, respectively, compared to MHK-RSA with the same time complexity.

Due to rapid growth of content-based cloud services (e.g., video streaming), energy usage of cloud infrastructures, consisting of distributed data centers interconnected by a high-bandwidth long-distance network, keeps increasing. This energy usage could further increase due to requested content redundancy to ensure content-service resiliency. Typical content redundancy schemes are based on content replication (CR), i.e., each content is replicated in at least one secondary dc location, reachable in case of failure affecting the primary location, which induces at least 100% increase in storage energy consumption. To reduce storage energy overhead of CR, we investigate a new redundancy scheme, called content fragmentation (CF). CF exploits Reed-Solomon erasure code to fragment and encode content into blocks with less storage overhead (thus less storage energy usage) to guarantee content-service resiliency. But CF requires additional energy for content reconstruction and data transport in the core network. To determine which scheme is more energy efficient, we formulate, for both, the content-placement problem using mixed integer liner program (MILP), with the objective to minimize energy consumption. Also, due to MILP's poor scalability, we propose a meta-heuristic content placement and routing assignment algorithm for more efficient solutions. We observe the impact on energy usage of three main metrics, i.e., number of content requests (popularity), resiliency, and latency. Results from a realistic case study suggest that CF always consumes less energy than CR given the same resiliency requirement, and CF is particularly energy-efficient when stored contents are less popular and latency-tolerant.

Mobile fronthaul (MF) provides connectivity between a remote radio head (RRH) and base-band processing unit (BBU) in a cloud radio access network. Dedicated MF connections between RRH and BBU are costly due to high bandwidth requirements. The cost issue becomes more pressing as cell densification increases the number of connections. A time-division multiplexing Ethernet passive optical network (TDM-EPON) is a promising solution to reduce MF cost, as it can multiplex several fronthaul flows in the MF network. However, even though mobile users transmit intermittently, RRH samples radio signals continuously, resulting in large constant line-rate connections between RRH and BBU that require huge aggregated bandwidth and render statistical multiplexing gain marginal. To improve upstream transmission efficiency by utilizing statistical multiplexing in an MF network, we propose to enhance the conventional TDM-EPON architecture. The enhanced architecture employs two techniques: (1) traffic classification (classifying upstream traffic into useful and useless by using machine-learning classifiers); and (2) useless-data sifting (sifting useless traffic to avoid the transmission of unnecessary EPON frames). We explore two feature-selection methods together with classifier biasedness in the first technique to achieve the best classification results, which then serve as input to our proposed sifting-based hybrid bandwidth allocation scheme in the second technique. Simulation results show significant improvements in terms of per-RRH traffic load, number of RRHs supported by same EPON, and signal-to-noise ratio, while keeping the end-to-end delay under 100 μs.