The paradigm of network function virtualization (NFV) with the support of software defined networking (SDN) emerges as a promising approach for customizing network services in fifth generation (5G) networks. In this paper, a multicast service orchestration framework is presented, where joint traffic routing and virtual network function (NF) placement are studied for accommodating multicast services over an NFV-enabled physical substrate network. First, we investigate a joint routing and NF placement problem for a single multicast request accommodated over a physical substrate network, with both single-path and multipath traffic routing. The joint problem is formulated as a mixed integer linear programming (MILP) problem to minimize the function and link provisioning costs, under the physical network resource constraints, flow conservation constraints, and NF placement rules; Second, we develop an MILP formulation that jointly handles the static embedding of multiple service requests over the physical substrate network, where we determine the optimal combination of multiple services for embedding and their joint routing and placement configurations, such that the aggregate throughput of the physical substrate is maximized, while the function and link provisioning costs are minimized. Since the presented problem formulations are NP-hard, low complexity heuristic algorithms are proposed to find an efficient solution for both single-path and multipath routing scenarios. Simulation results are presented to demonstrate the effectiveness and accuracy of the proposed heuristic algorithms.

Software defined networking (SDN) and network function virtualization (NFV) are key enabling technologies in fifth generation (5G) communication networks for embedding service-level customized network slices in a network infrastructure, based on statistical resource demands to satisfy long-term quality of service (QoS) requirements. However, traffic loads in different slices are subject to changes over time, resulting in challenges for consistent QoS provisioning. In this paper, a dynamic flow migration problem for embedded services is studied, to meet end-to-end (E2E) delay requirements with time-varying traffic. A multi-objective mixed integer optimization problem is formulated, addressing the trade-off between load balancing and reconfiguration overhead. The problem is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. It is proved that there is no optimality gap between the two problems; hence, we can obtain the optimum of the original problem by solving the MIQCP problem with some post-processing. To reduce time complexity, a heuristic algorithm based on redistribution of hop delay bounds is proposed to find an efficient solution. Numerical results are presented to demonstrate the aforementioned trade-off, the benefit from flow migration in terms of E2E delay guarantee, as well as the effectiveness and efficiency of the heuristic solution.

In this paper, a comprehensive software-defined networking (SDN) based transmission protocol (SDTP) is presented for fifth generation (5G) communication networks, where an SDN controller gathers network state information from the physical network to improve data transmission efficiency between end hosts, with in-path packet retransmission. In the SDTP, we first develop a new two-way handshake mechanism for connection establishment between a pair of end host. With the aid of SDN control module, signaling exchanges for establishing E2E connections are migrated to the control plane to improve resource utilization in the data plane. A new SDTP packet header format is designed to support efficient data transmission with in-path packet caching and packet retransmission. Based on the new data packet format, a novel in-path receiver-based packet loss detection and caching-based packet retransmission scheme is proposed to achieve in-path fast recovery of lost packets. Extensive simulation results are presented to validate the effectiveness of the proposed protocol in terms of low connection establishment delay and low end-to-end packet transmission delay.

Service-oriented virtual network deployment is based on statistical resource demands of different services, while data traffic from each service fluctuates over time. In this paper, a delay-aware flow migration problem for embedded services is studied to meet end-to-end (E2E) delay requirement with time-varying traffic. A non-convex multi-objective mixed integer optimization problem is formulated, addressing the trade-off between maximum load balancing and minimum reconfiguration overhead due to flow migrations, under processing and transmission resource constraints and QoS requirement constraints. Since the original problem is non-solvable in optimization solvers due to unsupported types of quadratic constraints, it is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. The optimality gap between the two problems is proved to be zero, so we can obtain the optimum of the original problem through solving the MIQCP problem with some post-processing. Numerical results are presented to demonstrate the aforementioned trade-off, as well as the benefit from flow migration in terms of E2E delay performance guarantee.

In this paper, an analytical end-to-end (E2E) packet delay modeling is established for multiple traffic flows traversing an embedded virtual network function (VNF) chain in fifth generation communication networks. The dominant-resource generalized processing sharing is employed to allocate both computing and transmission resources among flows at each network function virtualization (NFV) node to achieve dominant-resource fair allocation and high resource utilization. A tandem queueing model is developed to characterize packets of multiple flows passing through an NFV node and its outgoing transmission link. For analysis tractability, we decouple packet processing (and transmission) of different flows in the modeling and determine average packet processing and transmission rates of each flow as approximated service rates. An M/D/1 queueing model is developed to calculate packet delay for each flow at the first NFV node. Based on the analysis of packet interarrival time at the subsequent NFV node, we adopt an M/D/1 queueing model as an approximation to evaluate the average packet delay for each flow at each subsequent NFV node. The queueing model is proved to achieve more accurate delay evaluation than that using a G/D/1 queueing model. Packet transmission delay on each embedded virtual link between consecutive NFV nodes is also derived for E2E delay calculation. Extensive simulation results demonstrate the accuracy of our proposed E2E packet delay modeling, upon which delay-aware VNF chain embedding can be achieved.

The software defined networking (SDN) enabled network function virtualization (NFV) architecture emerges as a cost-effective solution for service customization in fifth generation (5G) networks. In this paper, a joint traffic routing and virtual network function (VNF) placement problem is studied for a multicast service request accommodated over a physical substrate network, where the multipath traffic routing is considered between embedded VNFs. The joint problem is formulated as a mixed integer linear programming (MILP) problem to minimize the provisioning cost of both VNFs and links, under the physical network resource constraints, flow conservation constraints, and VNF placement rules. Since the problem is NP-hard, low complexity heuristic algorithms, with the consideration of both the single-path and multipath routing cases, are proposed to determine an efficient solution. Simulation results are presented to demonstrate the effectiveness and accuracy of the proposed heuristic algorithms especially for a large-size network.

This paper addresses the problem of satisfying the extreme requirements related to Ultra-Reliable Low Latency Communications (URLLC) in 5G Radio Access Network (RAN). Complementary to the existing Physical (PHY) layer techniques, this paper focuses primarily on higher layer solutions, particularly, on Packet Duplication (PD) as a practical and low complexity technique for URLLC. The theoretic framework behind PD is investigated and the recent enhancements made in the 5G Dual Connectivity (DC) architecture for supporting PD are discussed. For improving the radio resource utilization and to dynamically control the activation of PD, an optimization problem subject to URLLC constraints is formulated and solved heuristically to give the resource configuration in terms of MCS and PRB allocation over multiple links. Following this, it is shown numerically that performing PD in various deployment scenarios results in better utilization of radio resources compared to using a single highly reliable link while effectively satisfying the URLLC requirements.

Network slicing often involves instantiation of certain network functionality into network nodes, possibly subject to function chaining constraints. In this paper, we introduce the novel concept of elastic service function chain (SFC), which is an ordered list of virtual functions that may be optional or recursive, and we address network slicing with such chaining constraints as a Software Defined Topology (SDT) problem. We formulate the SDT problem as a combinatorial optimization problem that includes a multi-commodity flow problem and a bin packing problem as sub-problems. Since it inherits NP hardness from the bin packing sub problem, we develop a heuristic algorithm to tackle it. The algorithm's effectiveness and performance are evaluated via simulation study.

MTC services are undergoing rapid increase; billions of MTC connections can be anticipated in a few years. When supporting MTC services in telecommunications networks, MTC traffic characteristics and its unique service requirements should be exploited in order to optimize resource utilization and offer satisfactory service quality for all devices. In this paper, we present an Information-Centric Virtual Network Architecture (IC-VNA) that allows to meticulously combine both the customer and operator specific virtual functions along with their requirements to construct an augmented virtual network topology. It is shown that through the IC-VNA it is feasible to rapidly roll-out highly programmable and flexible services while providing high degree of scalability and cost effectiveness for both MTC service providers and network operators.

Machine to machine (M2M) traffic is characterized as low-rate, small-packet traffic with correlated transmissions. In this paper, we propose a two-phase traffic control mechanism (2PTC) for carrying M2M traffic in the future fifth generation (5G) networks. In this mechanism, the packets from each machine are directed to a virtual serving gateway associated with the machine, which receives and aggregates traffic from multiple machines and forwards the aggregate traffic to the sink. The first communication phase takes place through a simple single-path routing technique, while the second phase is empowered by multipath traffic engineering (TE) optimization. At the virtual serving gateways, traffic aggregation (TA) may include network-layer flow trunking and application-layer content compression. At the core of 2PTC is joint gateway selection and machine-to-gateway association that favors TA potentials while minimizing association cost and virtual serving gateway count. The described problem is formulated as a mixed integer programming optimization problem. As the structure of the formulation is inherently NP hard, the problem is solved using relaxation and rounding techniques, whose solution quality is evaluated through numerical analysis. We also implement the solution in a network simulator and evaluate the performance of 2PTC, through extensive simulations. Simulation results indicate that routing M2M traffic to properly selected virtual serving gateways for TA can alleviate the large-quantity small-packet problem, while enhancing the performance of the background traffic. This paves the way for further performance enhancement or enabling new features when deploying virtual network functions at virtual serving gateways.

This paper addresses the problem of simultaneously achieving high spectral efficiency (SE) and energy efficiency (EE) in the heterogeneous radio access technology (RAT) environment via biased intra- and inter-RAT offloading. An analytical framework is developed for investigating the SE and EE performance of a two-RAT heterogeneous network (HetNet), based on which it is shown that the feasibility of increasing the SE and EE via offloading is strongly dependent on the load level and the biased offloading factors. For jointly maximizing the SE and EE, a multi-objective optimization problem subject to quality of service (QoS) constraints is formulated and solved to give the Pareto optimal operational regime in terms of the network parameters. Following this, the constrained Pareto regime is used to quantify the tradeoff between SE and EE as an opportunity cost measure. By opportunistically adapting the small cell BS density and biased offloading factors to the load conditions, we numerically show the range of load values that achieves a good balance in the SE-EE tradeoff while satisfying the specified users' QoS requirements.

This paper addresses the problem of characterizing the quality of experience (QoE) of users accessing different types of applications in 802.11n wireless local area network (WLAN) deployment environments. Using the generalized logistic function with fractional powers, an analytically tractable QoE model that can be flexibly applied to different application types (real time and nonreal time) is formulated as a function of several quality of service (QoS) parameters. An 802.11n WLAN testbed is then used to validate the accuracy of the QoE model via experimental QoE measurements. The paper also investigates the impact of different network operational parameters including the number of users supported, percentage of real time traffic streamed and level of co-channel interference on both the QoS and QoE performance. Next, an optimization problem is solved to maximize the overall QoE achievable subject to the QoS constraints of the applications in use. Overall, the developed QoE model can be used as an effective tool for enhancing the Access Point (AP) capacity and the QoE performance by optimally adapting the network operational parameters.

This paper addresses the problem of balancing the tradeoff between spectrum efficiency (SE) and energy efficiency (EE) in heterogeneous cellular networks (HetNets). A framework is developed for analyzing the SE and EE of a 2-tier HetNet consisting of macro and femtocell base stations (BS) with shared spectrum operation. From the analysis it is shown that both the SE and EE can be significantly enhanced with the overlaid deployment of the femto tier. However, the performance gain achievable is found to be strongly dependent on the load level and the BS power consumption attributes. A multi-objective optimization problem that maximizes the SE and EE subject to Quality of Service (QoS) constraints is formulated and solved to give the Pareto optimal operational regime. In contrast to the conventional approach, this paper quantifies the SE-EE tradeoff as a Lebesgue measure, defined by the Pareto optimal regime. Numerical results show that while the performance improvement achieved by balancing the SE-EE tradeoff is marginal under high load conditions, it is feasible to significantly increase the SE and EE during low load conditions and satisfy the users' QoS requirements by optimally adapting the network configuration.

In this paper, a tractable analytical framework for investigating the energy efficiency (EE) of a 2-tier heterogeneous cellular network consisting of macro and picocell base stations (BS) is developed. The BSs, which are randomly distributed, serve a number of users in the downlink under shared spectrum operation. By incorporating a tunable downlink power control mechanism, the successful transmission probability and the average throughput achievable in both tiers are analytically derived. Since varying load conditions has an important implication on the network performance, the EE is investigated as a function of the load level, considering the ability to transfer the load between the 2 tiers. An optimization problem that maximizes the throughput constrained 2-tier EE is formulated and solved to determine the optimal network operational parameters. In addition to providing valuable design insights, the findings reveal that, for different load conditions, it is feasible to significantly enhance the EE performance by optimally adapting the network configuration.

In this paper, the energy efficiency (EE) of a 2-tier cellular network subject to downlink transmission outage probability constraints is analyzed under a shared spectrum scenario. The heterogeneous network considered consists of randomly distributed microcell BSs overlaid with picocell BSs. Based on the analysis of the structure of the EE function, the sufficient condition that enables the EE of the 2-tier network to exceed that of a single tier network is determined. The operational bounds for the network in terms of the pico tier BS density is derived by analytically solving the optimization problem that maximizes the outage constrained 2-tier EE. The Lagrange multipliers attributed to the outage constraints, which quantify the marginal loss in the EE performance, are found to be strongly dependent on the power consumption parameters of both the micro and pico tier BSs. The implication of the findings is that, by carefully tuning the BS power consumption parameters, it is possible to realize significant improvement in the EE performance for different operational scenarios while satisfying the outage probability objectives.

The focus of this paper is to analyze and assess the performance of various optimization techniques in the effort to determine the maximum lifetime of the nodes and the throughput in a Wireless Sensor Network (WSN) environment while being able to meet the network's application specific design requirements. We formulated the initial objective of the optimization problem as a nonlinear function and solved an instance of the problem in its original form using LOQO [7]. The objective function is then relaxed to assume simplified formulations based on certain assumptions in order to be solved using Geometric Programming (GP) and Linear Programming (LP) techniques with specific solvers, namely CVX and GGPLAB in Matlab. The main findings of this study reveal that there exists a tradeoff in the solutions obtained between performance accuracy and computational complexity. The resulting energy and delay minimization approach is shown to be able to support the various WSN application requirements.

This paper analyzes the interrelationship between the spectral efficiency (SE) and energy efficiency (EE) of cellular networks where the base stations (BSs) are arbitrarily distributed. The SE and EE performance metrics are assessed subject to a downlink transmission outage constraint when operating in interference limited operational environments. The network EE is expressed in closed-form as a function of SE, based on which the performance bounds of the network are determined. It is found that there exists an operating regime for which both SE and EE can be increased up to the optimal density of BSs concurrently utilizing the shared spectrum while satisfying the outage constraint. The SE-EE tradeoff regime is found to be applicable only when certain conditions related to the outage objective and the BS density are satisfied. Furthermore, for a given outage constraint it is shown numerically that, by carefully tuning the signal to interference ratio (SIR) threshold, it is possible to balance and improve both SE and EE performance.

The objective of improving energy efficiency in Wireless Sensor Networks (WSNs) is a primary design requirement given the limited energy available in the battery powered sensor nodes. In this paper, a battery aware distributed clustering and routing protocol is proposed for the maximization of the network lifetime. The proposed protocol incorporates the state of the battery's remaining charge and health parameters in computing the charge utility metric at each cluster formation round. The performance of the proposed protocol is assessed via analysis and simulations and the gains are quantified using common energy related metrics. The numerical results demonstrate that the proposed approach is able to effectively increase the network lifetime significantly when compared to other distributed flat and hierarchical based routing protocols. The implication of the findings is that, by carefully harnessing the information pertaining to the nodes' battery condition at the network layer, it is feasible to realize major improvements in energy efficiency of the WSN.