To adapt to the intensive, bursty, and latency-constrained traffic from large language model (LLM) training and inference, scale-up networks are now facing tremendous challenges. Existing electrical packet switching (EPS) fabrics provide packet-level flexibility at the cost of high power consumption and long latency. Introducing optical circuit switching (OCS) in scale-up networks offers direct optical connections that can effectively reduce power consumption and latency, but OCS lacks the packet-level flexibility required by LLM inference (especially for mixture-of-experts (MoE) inference). To address these dilemmas, this work presents xSwitch, an optical-electrical-integrated (O-E-integrated) interconnect for scale-up networks, and demonstrates its effectiveness experimentally. Unlike the traditional hybrid-optical-electrical interconnects that usually place EPS and OCS in parallel, xSwitch integrates one port-count-reduced (defined by the O/E port ratio) EPS layer (ESL) on top of an OCS layer (OSL). Then, OSL can establish optical connections for xPU pairs with stable and intensive traffic demands, while bypassing the ESL for energy and latency reduction, and the dynamic and unpredictable traffic between xPUs can be provisioned by letting OSL forward it to the ESL. We prototype xSwitch with off-the-shelf components, validate its effectiveness with real-world MoE inference tasks, and also confirm its scalability with large-scale simulations. Our results indicate that for MoE inference workloads, xSwitch with a 2:1 O/E port ratio limits the average gaps to the full-EPS baseline to 1.96% in TTFT and 0.59% in TPOT.

Driven by emerging distributed computing and cloud-edge collaborative applications, metro and regional networks have experienced continuous surges in hub-and-spoke (H&S) traffic, posing great challenges to existing point-to-point network infrastructures. While coherent point-to-multipoint optical transceivers (P2MP-TRXs) present a more cost-effective solution for accommodating H&S traffic, existing P2MP networking paradigms fail to address the dynamic and asymmetric nature of traffic prevalent in such networks, and consequently, could lead to subpar resource utilization. In this paper, we fill this gap by investigating dynamic asymmetric subcarrier allocation and reconfiguration in drop-and-continue (D&C) optical networks. In particular, our approach aims at minimizing the operational cost of service provisioning by invoking reactive and coordinated connection consolidation as a remedy for the inability to accommodate traffic demands with in-service or newly activated P2MP-TRXs. We first devise an integer linear programming (ILP) model to solve the multi-objective optimization problem exactly. As the problem is proved to beNP-hard, we further develop a column generation (CG)-based approximation algorithm that can offer guaranteed optimality bounds within reasonable time, as well as a polynomial-time heuristic framework employing priority-queue-based progressive search. Extensive simulations verify the effectiveness of our proposal, demonstrating up to 43.3% reduction in bandwidth blocking ratio and 2.9% improvement in spectrum utilization compared with the state of the art.

In remote direct memory access (RDMA)-based remote procedure call (RPC) systems, using reliable connection (RC) transport mode with one-sided verbs might face challenges from excessive queue pairs (QPs) and per-client request regions when there is high client concurrency. This can cause cache thrashing on the RDMA network interface card (RNIC) and CPU and restrict server-side scalability. To address this issue, this paper proposesRdmax, a highly-scalable RC-based RPC system incorporating two innovations. First, it leverages QP multiplexity to serve multiple independent clients simultaneously with one RC QP, breaking the one-to-one connection constraint of RC. This also allows to post responses for different clients in one batch. Second, it uses in-network request dispatching to consolidate per-client request regions into a shared one, while still ensuring conflict-free client writes.Rdmaxrealizes these innovations with a programmable network, which manages the states of RC QPs and the request region and modifies relevant fields in RDMA packets accordingly. In a dummy RPC benchmark,Rdmaxachieves up to$10.4\times $higher throughput. For online transaction processing,Rdmaxoutperforms two state-of-the-arts based on client grouping and unreliable datagram schemes, improving throughput by up to 44.3% and 45.7%, while reducing 99% tail latency by up to 66.8% and 40.0%, respectively.

Recently, the growing of deep space explorations has attracted notable interests on interplanetary network (IPN), which is the key infrastructure for communications across vast distances in the solar system. However, the unique characteristics of IPN pose numerous unexplored challenges for interplanetary data transfers (IP-DTs), i.e., the challenges that existing schemes developed for Earth-based networks are ill-equipped to handle. To address these challenges, we first propose a novel distributed algorithm that leverages the Lyapunov optimization to jointly optimize the routing, scheduling and rate control of IP-DTs at each node. Specifically, our proposal adaptively optimizes the data-rate and bundle scheduling at each output port of a node, significantly improving the end-to-end (E2E) latency and delivery ratio of IP-DTs under a long-term energy constraint. Then, we further explore the heterogeneity of IPN to introduce limited state information exchange among nodes, and devise mechanisms for generating and disseminating state messages to facilitate timely adjustments of routing and scheduling schemes in response to unexpected link disruptions and traffic surges. Simulations verify the advantages of our proposal over the state-of-the-arts.

As deep space exploration missions grow in complexity, efficient data transfer in interplanetary networks (IPNs) becomes paramount. However, the vast distances, limited bandwidth, and dynamic nature of IPNs pose significant challenges for the routing and data scheduling of interplanetary data transfers (IP-DTs). To address these challenges, this work proposes a novel distributed, graph neural network (GNN) based multiagent deep reinforcement learning (DRL) approach that can jointly optimize the routing and scheduling of IP-DTs. Our proposal is based on the proximal policy optimization (PPO) framework along with the graph attention networks (GATs). We make the DRL agents for IPN nodes in each subnetwork around a celestial body learn and operate independently, for making intelligent routing and scheduling decisions to properly tradeoff between average end-to-end (E2E) latency and delivery ratio of IP-DTs while ensuring good scalability. Extensive simulations confirm that our proposal handles the routing and scheduling of IP-DTs much better than existing benchmarks. Further, by modifying the interplanetary overlay network (ION) software platform developed by NASA, we build a semi-physical IPN emulator based on Raspberry Pi boards, implement our proposal in it, and conduct experiments with real data transfers between IPN nodes. Experimental results verify that our proposal can work for practical IPNs without causing excessive overheads and prove its advantages.

Nowadays, the performance of data-center networks (DCNs) has become crucial for advancing large-scale computing applications. Hence, to improve the throughput, energy-efficiency and latency of DCNs, people are trying to replace the electrical packet switching (EPS) based spine switches with optical circuit switching (OCS) based ones. In an OCS-based DCN, topology engineering (TPE) is the key operation to dynamically reconfigure its inter-pod topology for accommodating traffic with optimized resource utilization. TPE consists of two highly-correlated steps, i.e., optimizing the target physical inter-pod topology of the DCN based on a traffic matrix, and planning the procedure of OCS reconfiguration such that hitless transition can be achieved. In this paper, we study how to optimize the two steps jointly to efficiently accelerate the hitless reconfiguration of an OCS-based DCN. We formulate a mixed linear programming model (MILP) to solve the joint optimization exactly. Then, to solve the problem time-efficiently, we propose an approach that optimizes TPE design greedily according to various metrics to minimize the number of stages required in hitless reconfiguration for TPE. Extensive simulations verify the effectiveness of our proposals and demonstrate their benefits over existing benchmark.

The dynamic traffic changes in an optical circuit switching based data-center network (ODCN) can make the utilization of its optical connections fragmented, degrading the efficiency of service provisioning in the ODCN. Consequently, connection defragmentation becomes imperative. However, consolidating traffic onto fewer connections may unilaterally add the risk of bandwidth contention and undermine ODCNs’ robustness against unexpected traffic bursts. To address this issue, we propose risk-aware connection defragmentation (RA-cDF), which explores the topology flexibility of ODCN to consolidate traffic such that the active optical connections through optical circuit switching (OCS) switches can be minimized together with the risk of future bandwidth contention on remaining connections. We formulate a mixed integer linear programming (MILP) model to address the RA-cDF problem exactly, followed by a heuristic to solve it time-efficiently. Extensive simulations confirm the effectiveness of our proposals.

Service function chain (SFC) consists of multiple ordered network functions (e.g., firewall, load balancer) and plays an important role in improving network security and ensuring network performance. Offloading SFCs onto programmable switches can bring significant performance improvement, but it suffers from unbearable reconfiguration delays, making it hard to cope with network workload dynamics in a timely manner. To bridge the gap, this paper presents OptRec, an efficient SFC proactive reconfiguration optimization framework based on deep reinforcement learning (DRL). OptRec predicts future traffic and places SFCs on programmable switches in advance to ensure the timeliness of the SFC reconfiguration, which is a proactive approach. However, it is non-trivial to extract effective features from historical traffic information and global network states, while ensuring efficient and stable model training. To this end, OptRec introduces a multi-level feature extraction model for different types of features. Additionally, it combines reinforcement learning and autoregressive learning to enhance model efficiency and stability. Results of in-depth simulations based on real-world datasets show the average prediction error of OptRec is less than 3 can increase the system throughput by up to 69.6 compared with other alternatives.

Due to the capabilities of providing extremely low packet loss and bounded end-to-end latency, deterministic networking (DetNet) has been considered as a promising technology for emerging time-sensitive applications (e.g., industrial control and smart grids) in IP networks. To provide deterministic services, the operator needs to address the routing and scheduling problem. In this work, we study the problem from a novel prospective, i.e., the problem should be optimized not only for deterministic traffic, but also for normal traffic to coexist with the former. Specifically, we redefine the problem as bandwidth allocation, routing and scheduling (BaRS), and model this problem as a bilevel optimization which consists of an upper-level optimization and a lower-level optimization. The upper-level optimization allocates link bandwidth between deterministic and normal traffic to maximize the available bandwidth for normal traffic on the premise of accepting a certain portion of deterministic bandwidth; the lower-level optimization determines specific routing and scheduling solutions for deterministic traffic to maximize the number of accepted deterministic flows. We first formulate the bilevel optimization as a bilevel mixed integer linear programming (BMILP). Then, we propose an exact algorithm based on cutting planes to solve it exactly, and propose an approximation algorithm based on two-level relaxations and randomized rounding to solve it effectively and time-efficiently. Extensive simulations are conducted and the results verify the effectiveness of our proposals in balancing the tradeoff between the available bandwidth for normal traffic and the number of accepted deterministic flows.

Increasing demands for distributed machine learning (DML) have posed significant pressure on data-center networks (DCNs). This promotes the adoption of reconfigurable all-optical interconnects (AOI) in DCNs leveraging optical circuit switching (OCS) for better performance on throughput, energy efficiency, and data transfer latency. Despite their benefits, these OCS-based DCNs (ODCNs) still bear limited flexibility due to the larger switching granularity and longer reconfiguration latency of OCS. To address this issue, this work introduces in-network computing (INC) in an ODCN to realize P4INC-AOI, which can orchestrate INC and AOI to explore their mutual benefits for accelerating the training of DML jobs with less AOI reconfigurations. In the control plane of P4INC-AOI, we address the scheduling of concurrent DML jobs by formulating a mixed integer linear programming (MILP) model and proposing a time-efficient heuristic, to allocate multi-dimensional resources and configure AOI for minimizing the longest job completion time (JCT) across workloads. For the data plane, we extend existing in-network gradient aggregation schemes to accelerate DML jobs more efficiently. We first implement P4INC-AOI and verify its performance in a small-scale ODCN testbed, and further justify its effectiveness with large-scale simulations. Our experimental results demonstrate that compared with an ODCN without INC, P4INC-AOI not only cuts down AOI reconfigurations effectively but also reduces the average JCT of DML jobs in ResNet50 and VGG16 by$46.66\%$and$56.34\%$, respectively.

Enabled by programmable data plane (PDP), innetwork computing (INC) can offload the computation phase of distributed machine learning (DML) training to accelerate the execution of parameter servers (PS’). Meanwhile, all-optical interconnect (AOI) can effectively improve the network throughput for DML training. This work studies how to schedule a batch of parallel DML jobs in an all-optical data center network (DCN) to fully explore the mutual benefits of INC and AOI. Specifically, for each DML job, we determine where to deploy its workers, whether and where to offload/place its PS, and how to plan the routing of data transfers between the workers and the PS, such that its job completion time (JCT) is minimized. We formulate a mixed integer linear programming (MILP) model to optimize the job scheduling for the cases with and without INC, and propose a heuristic to tackle the case with INC quickly. Extensive simulations confirm the effectiveness of our proposals.

The rapid development of network function virtualization (NFV) has notably increased the implementation of virtual network functions (vNFs), especially the stateful ones, on high-performance programmable data plane (PDP) switches (e.g., those based on P4 and Tofino ASICs). This facilitates offloading stateful service function chains (SFCs) to PDP switches. However, the capability of PDP switches to carry stateful SFCs is still constrained by their limited hardware resources. In this work, we propose to address this issue with adaptive table merging, formulate an integer linear programming (ILP) model to optimize the deployment of stateful SFCs on P4-based PDP switches with table merging, and design a time-efficient heuristic to solve the problem quickly. Simulation results validate that our proposals successfully enhance the deployment efficiency of stateful SFCs, outperforming existing benchmarks significantly.

The support of Mobile Fronthaul (MFH) over Passive Optical Networks (PONs) poses significant challenges due to the stringent latency and bandwidth requirements of Functional Splitting (FS). This paper addresses the problem of Quality of Service (QoS) provisioning in next-generation Ethernet PON (NG-EPON) for the transport of traffic generated by multiple different FS options. We propose a PON bandwidth allocation algorithm that distributes the resources for the Optical Network Units (ONUs) serving Functional Split (FS) options based on their bandwidth and latency requirements in networks with customers renting/owning more than one ONU (multi-ONU customers). Simulation results show that our proposal significantly improves network resource utilization for multi-ONU customers, meeting the latency requirements of the different FS options while reducing the required bandwidth.

As network monitoring is crucial for ensuring the performance of network operations, one key challenge is how to optimize the tradeoff between its overheads and accuracy. This paper proposes a smart reporting mechanism, namely SmtRPTG, which continuously optimizes the scheme of network status collecting and reporting to properly balance the tradeoff. SmtRPTG estimates the distributions of status data of various types based on sampled results, and lets network elements make local decisions on whether and what type of status data should be reported based on the estimations. We formulate a probabilistic model with hidden variables, based on which an algorithm is designed to estimate data distributions for SmtRPTG. Extensive simulations verify the effectiveness of our proposal on balancing the tradeoff between overheads and accuracy of network monitoring.

Service function chain (SFC) consists of multiple ordered network functions (e.g., firewall, load balancer) and plays an important role in improving network security and ensuring network performance. Offloading SFCs onto programmable switches can bring significant performance improvement, but it suffers from unbearable reconfiguration delays, making it hard to cope with network workload dynamics in a timely manner. To bridge the gap, this paper presents OptRec, an efficient SFC reconfiguration optimization framework based on deep reinforcement learning (DRL). OptRec predicts future traffic and places SFCs on programmable switches in advance to ensure the timeliness of the SFC reconfiguration, which is a proactive approach. However, it is non-trivial to extract effective features from historical traffic information and ensure efficient and stable model training. To this end, OptRec introduces a multi-level feature extraction model for different types of features. Additionally, it combines reinforcement learning and autoregressive learning to enhance model efficiency and stability. Results of in-depth simulations based on real-world datasets show the average prediction error of OptRec is less than 3% and OptRec can increase the system throughput by up to 69.6%~72.6% compared with other alternatives.

With the rapid development of network function virtualization (NFV), there is an increasing trend of implementing virtual network functions (vNFs), especially the stateful ones, on high-performance programmable data plane (PDP) switches (e.g., the P4-based PDP switches based on Tofino ASICs), and forming stateful service function chains (SFCs) with them. However, the capability of PDP switches on supporting stateful SFCs is still restricted by the limited hardware resources in them. In this work, we study how to optimize the deployment of stateful SFCs in P4-based PDP switches and design the system of st-SFC, so as to not only utilize the hardware resources on switches efficiently but also minimize the overhead of interactions between control and data planes. We first consider the deployment of stateful SFCs on a single PDP switch. Specifically, we propose to abstract each stateful vNF as a state machine and design a stateful SFC building algorithm to merge the state machines of vNFs for reducing redundant resource usages, and for the vNFs whose operations involve interactions with the control plane, we develop a PktIn-Table to reduce the resource usage in PDP switches and the interaction latency. Then, we propose an SFC deployment algorithm that realizes stateful SFCs on PDP switches on demand, aiming to optimize the resource usages across all the switches in runtime. We prototype st-SFC with PDP switches based on Tofino ASICs and demonstrate its effectiveness experimentally.

Data plane programmable (PDP) switches are becoming increasingly popular for network function virtualization (NFV), for their programmability and high packet processing performance. However, the inherent limitations of PDP switches, such as limited memory space, make it challenging to implement certain types of network functions (NFs) (i.e., the stateful ones) on them. This paper proposes SFCache, which combines PDP switches and commodity servers to achieve self-adaptive SFC deployment. SFCache aims to exploit the high packet processing performance of PDP switches while supporting the flexible deployment of a wide range of SFCs (including the stateful ones) with servers. Specifically, SFCache can dynamically improve the packet processing performance of the SFCs that were deployed on servers by selectively caching SFC-level packet processing rules on PDP switches. We design a few key components to facilitate SFCache, including an NF-destructed P4 pipeline that allows customizing packet processing rules in a match-rewrite pattern, a runtime NF synthesis method that can transform a set of NF-level match-rewrite rules into an equivalent SFC-level rule, and a count-min selection strategy to choose the best synthesized rule for being cached in PDP switch pipeline. We prototype SFCache with a PDP switch based on Tofino ASIC and a server, and demonstrate the effectiveness of our proposal experimentally.

Interplanetary networks (IPNs) are complex communication infrastructures used for data exchange among spacecrafts, rovers and ground stations. Due to the significant delay and uncertainty in communications, efficient routing and data scheduling of interplanetary data transfer (IP-DT) becomes crucial. With the increase of deep space (DS) exploration missions, it would be difficult for existing IPNs to cope with the growing of IP-DT demands. In this work, to improve the performance of IP-DTs in IPNs, we formulate an integer linear programming (ILP) model and design an effective fine-grained distributed routing and data scheduling (FD-RDS) algorithm based on it. We prove that the proposed algorithm is a polynomial-time 2-approximation algorithm for solving the ILP model. Extensive simulations show that our proposals can significantly improve the efficiency and reliability of IPNs. Specifically, our proposals outperforms known benchmarks in terms of both the delivery ratio and E2E latency of IP-DTs.

Self-adaptation of service function chains (SFCs) has been considered as an important attribute to ensure the resource-efficiency and reliability of network function virtualization (NFV) systems. In this work, we leverage the idea of integrating segment routing over IPv6 (SRv6) and in-band network telemetry (INT) seamlessly to realize SRv6-INT and explore the mutual benefits of SRv6 and INT for achieving self-adaptive SFC deployment. Specifically, we design and experimentally demonstrate a self-adaptive SRv6-INT-driven SFC deployment system that orchestrates network and IT resources timely to adapt to bursty traffic and network changes. We first enhance our previous design of SRv6-INT to better use it for self-adaptive SFC deployment, and then propose an IT resource management technique for Kubernetes (K8s) to accomplish resource allocation and contention resolution without offline virtual network function (vNF) profiling. Next, a closed-loop system is designed to manage SFCs in both the local and global ways. As for the local way, we let servers make local decisions based on the INT data encoded in packets to scale the vNFs running on them vertically. The global way involves the control plane, which oversees the SFC deployment in the whole network to change the number and placement of vNFs and the traffic routing through them. Finally, we prototype our proposal with commodity servers and hardware PDP switches based on Tofino ASICs, and experimentally demonstrate its effectiveness.

With the development of programmable data plane (PDP), in-band network telemetry (INT) has become a promising network monitoring technique to visualize network operations in a fine-grained and real-time way. In this work, to better balance the tradeoff between INT overheads and monitoring accuracy, we design and optimize an information-sensitive INT system (namely, P4InfoSen-INT), which makes each PDP switch decide locally whether and what type(s) of telemetry data should be inserted in a packet based on the “information content” of the data, and implement it in P4-based PDP switches. We first realize the basic principle of P4InfoSen-INT with P4 programs. Then, we propose algorithms to estimate the information content of telemetry data accurately in a dynamic network and optimize the tradeoff between INT overheads and monitoring accuracy. Finally, we further optimize the implementation of P4InfoSen-INT by proposing table merging to reduce stage occupation in each switch. Experimental results verify that our proposed P4InfoSen-INT can balance the tradeoff between INT overheads and monitoring accuracy better than existing benchmarks.

Due to the advantages of optical circuit switching (OCS), all-optical data center networks (DCNs) have attracted intensive research interests recently. Hyper-FleX-LION is a highly-flexible all-optical DCN architecture that operates with the OCS based on wavelength-division multiplexing (WDM). In this work, we study how to realize traffic-aware configuration of all-optical DCNs in Hyper-FleX-LION. We formulate an integer linear programming (ILP) model for the problem to jointly optimize the configuration of Hyper-FleX-LION and the provisioning schemes of demands in it for minimizing its port usage. To ensure the practicalness of the optimization, we assume that each top-of-rack (ToR) switch can not only receive the traffic targeting to its rack but also forward traffic to other racks as an intermediate node. We also classifier traffic demands as normal and latency-sensitive ones, and set the maximum hop-count for routing latency-sensitive demands. By analyzing the complexity of the problem theoretically, we prove its$\mathcal{APX}$-hardness, i.e., there does not exist a polynomial-time approximation algorithm for it unless$\mathcal{P}=\mathcal{NP}$. Then, we propose a polynomial-time heuristic JTRO based on iterative optimization to solve the problem effectively and time-efficiently. Extensive numerical simulations verify the effectiveness of our proposed algorithm. We also build a small-scale but real all-optical DCN testbed in Hyper-FleX-LION to interconnect four racks, and leverage distributed machine learning (DML) as the network services in it to demonstrate the performance of our proposal experimentally.

With the fast development of deep space exploration missions, the data transfer in interplanetary networks (IPNs) is gaining increasing attention. In this work, we propose a deep reinforcement learning (DRL) based routing and data scheduling approach, which leverages a multi-agent setup for distributed operations and aims to balance the trade-off between average end-to-end (E2E) latency and delivery ratio of interplanetary data transfers (IP-DTs) well. Specifically, DRL agents based on asynchronous advantage actor-critic (A3C) are deployed on each IPN node to handle the routing and data scheduling of IP-DTs there separately. Simulation results confirm that our proposal can handle the routing and data scheduling of IP-DTs more adaptively and balance the tradeoff between the delivery ratio and average E2E latency better than the benchmarks.

Runtime re-optimization of service function chains (SFCs) has been considered as a must-have feature to enable cost-effective SFC provisioning for the multi-access edge computing (MEC) in beyond 5G (B5G) networks. Therefore, in this work, we propose SRv6-INT, which time-multiplexes the fields of segment routing over IPv6 (SRv6) and in-band network telemetry (INT) in packets to monitor and adjust SFCs timely and efficiently. We also prototype a closed-loop network control and management (NC&M) system based on SRv6-INT to re-optimize SFC provisioning in runtime for precisely balancing the tradeoff between quality-of-service (QoS) and energy usage of SFCs. Experimental results show that with SRv6-INT, the provisioning of SFCs can be adjusted in runtime to save switch ports and CPU frequency, leading to effective energy-saving without QoS violation.

The growth of Internet of Things (IoT) has led to the convergence of heterogeneous networking systems powered by various protocols, and consequently the emergence of protocol-independent packet processing based on programmable data plane (PDP) for IoT. In this work, we study how to leverage the hardware acceleration enabled by offloading flow tables to SmartNIC to improve the performance of software PDP switches based on protocol-oblivious forwarding (POF). We design our SmartNIC offloading system (namely, OVS-POF-TC) based on the Linux kernel traffic classification (TC) system and open vSwitch (OVS), extend OVS to enable the installation of POF-based flow tables (POF-FTs) in a SmartNIC, and design a selective offloading mechanism for purposely offloading heavy-load POF-FTs. Our experimental results indicate that OVS-POF-TC offloads and updates POF-FTs timely, supports runtime programmability, and has improved packet processing throughput by$1.52\times $and$3.82\times $, when applying POF-FTs with SetField and AddField to packets, respectively. Moreover, to ensure that OVS-POF-TC can offload and replace POF-FTs adaptively, we formulate two mixed-integer linear programming (MILP) models to, respectively, solve the problems of flow placement and flow replacement, and also design time-efficient heuristics for them.

With the rapid development of programmable data plane (PDP), both segment routing (SR) and in-band network telemetry (INT) have attracted intensive interests. Hence, we have previously proposed the technique of SR-INT, which explores the benefits of SR and INT simultaneously and gets rid of the hassle of the accumulated overheads of them. In this work, we further expand the advantage of SR-INT by studying how to plan the SR-INT schemes of flows at the network level to balance the tradeoff between bandwidth usage and coverage of network monitoring, namely, the problem of “SR-INT orchestration”. A mixed integer linear programming model (MILP) is first formulated for the problem, and we prove its NP-hardness. Then, to reduce the time complexity of problem-solving, we propose a novel greedy algorithm based on path ranking and a column generation (CG) based approximation algorithm. Extensive simulations verify the performance of our proposed algorithms.

The rapid development of in-network computing motivates people to consider deploying virtual network functions (vNFs) on heterogeneous platforms that include both software systems like virtual machines and docker containers and hardware systems like programmable data plane switches. Meanwhile, with the emergence of multi-client network services, service providers need to build vNF service trees (vNF-STs) in their substrate networks (SNTs). In this work, we study how to optimize the management reconfiguration of vNF-STs in an SNT equipped with heterogeneous platforms. An integer linear programming (ILP) model is first formulated to consider three common reasons for vNF-ST reconfiguration and reduce both the total resource usage after reconfiguration and the overall vNF migration cost during reconfiguration. Then, we design a two-step algorithm to reduce the time complexity of problem-solving. Specifically, the algorithm first checks all the active vNF-STs to select the vNF-STs that should be reconfigured, and then leverages an approach based on layered auxiliary graphs (LAGs) to reconfigure the selected vNF-STs. Extensive simulations verify the effectiveness of our algorithms on optimizing the management reconfiguration, and demonstrate that they can outperform existing benchmarks.

Nowadays, the booming of emerging network services have shifted the major traffic pattern in metro-aggregation networks from point-to-point (P2P) to hub-and-spoke (H&S). Hence, it will be promising to plan metro-aggregation networks with point-to-multipoint coherent optical transceivers (P2MP-TRXs). This work studies how to plan a survivable wavelength-switched optical network (WSON) with P2MP-TRXs and shared backup path protection (SBPP) to address single-link failures. We formulate an integer linear programming (ILP) model to place P2MP-TRXs, assign sub-carriers (SCs) to P2MP-TRXs, and calculate routing and spectrum assignment (RSA) for the working/backup lightpath between each hub-leaf P2MP-TRX pair, such that traffic demands can be satisfied with the minimum cost. A heuristic based on adaptive demand grouping (ADG) is also proposed to solve the problem time-efficiently. Extensive simulations confirmed the performance of our proposals.

With enhanced cost-effectiveness, filterless optical networks (FONs) have been considered as a promising candidate for future optical infrastructure. However, as the transmission in FON relies on the “select-and-broadcast” scenario, it is more vulnerable to eavesdropping. Therefore, encrypting the communications in FONs will be indispensable, and this can be realized by introducing the optical transport network (OTN) encryption technologies that leverage high-speed encryption cards (ECs) to protect the integrity of OTN payload frames. In this paper, we study the problem of security-aware multilayer planning of FONs with OTN encryption. We first formulate a mixed integer linear programming (MILP) model (i.e., w-MILP) to solve the problem exactly. Then, to reduce the time complexity of problem-solving, we transform w-MILP into two correlated MILP models for establishing fiber trees for an FON (t-MILP) and planning flows in the fiber trees (s-MILP), respectively. The optimization in t-MILP is further transformed into a weighted set partitioning problem, which can be solved time-efficiently. As for s-MILP, we propose a polynomial-time approximation algorithm based on linear programming (LP) relaxation and randomized rounding. Extensive simulations verify the performance of our proposals.

As a promising network monitoring technique, in-band network telemetry (INT) helps to visualize networks in a fine-grained and real-time manner. Meanwhile, to address the overheads of INT, people have proposed a few selective INT (Sel-INT) approaches that only select a portion of packets in each flow to insert INT fields and distribute different types of INT fields over the selected packets. In this paper, we study how to use Sel-INT wisely in a network such that the tradeoff between monitoring accuracy/coverage and INT overheads can be balanced well. Specifically, we try to orchestrate the Sel-INT schemes of flows in both network- and flow-levels. For the network-level optimization, we model it as an INT planning problem in which the Sel-INT schemes of flows should be determined to maximize the information gain of INT as well as minimize the bandwidth overheads of INT. We formulate an integer linear programming (ILP) model to tackle the problem, prove its$\mathcal {N} \mathcal {P}$-hardness, and leverage Lagrangian relaxation to design a polynomial-time approximation algorithm for it. The flow-level optimization considers a dynamic network environment, and we propose to combine deep learning (DL) based traffic prediction with Sel-INT, such that the Sel-INT scheme of each individual flow can be updated timely and adaptively. We implement the proposal in a small but real network testbed and experimentally demonstrate self-adaptive orchestration of Sel-INT with it.

Although the idea of multilayer network automation (MLy-NA) has gained its initial success due to the advances on software-defined networking (SDN) and machine learning (ML), the security vulnerabilities brought by the reduction of human intervention in network operation should not be ignored. In this work, we study how to mislead the ML-based classifiers for anomaly detection in MLy - NA of packet-over-optical networks, and propose a self-taught black-box adversarial attack (BB-AdA) scheme. Specifically, we design novel algorithms to synthesize and label training data for the substitute classifier used by the BB-AdA. The algorithms not only generate synthetic data to cover all the anomaly types based on a small set of legitimate telemetry data only containing the “Normal” type, but also label the data with minimized queries to the target classifier in MLy-NA. Extensive simulations are conducted with the telemetry data collected from a real-world packet-over-optical network testbed. The results show that with our self-taught BB-AdA, an attacker can interact with an MLy-NA system quietly and efficiently to train itself adaptively, generate well-crafted adversarial samples to mislead the target classifiers in different ML architectures and severely affect their performance on anomaly detection, and disturb the operation of MLy-NA in the hard-to-detect manner.

In-band network telemetry (INT) can monitor networks in a flow-oriented and realtime way. The bandwidth overheads caused by per-packet INT can be effectively reduced by selective INT (Sel-INT), i.e., sampling packets or/and types of telemetry data for INT. For the sampling in Sel-INT, there is a tradeoff between bandwidth overheads and monitoring accuracy, which can be adjusted by the sampling ratio. In this paper, we investigate how to adjust the sampling ratio of Sel-INT adaptively such that the tradeoff can be balanced precisely. We first develop a theoretical model to analyze the accuracy of Sel-INT for traffic trace reconstruction under different sampling ratios. We assume that the data samples of a flow's traffic trace (i.e., bandwidth usage) follow the Gaussian random process, and the traffic trace is reconstructed with linear interpolation based on the samples collected by Sel-INT. We analyze this process theoretically and derive the expected reconstruction error (RCE) between the original and reconstructed traces. Then, we use RCE to determine whether the sampling ratio of Sel-INT is properly set and propose two algorithms, namely, RCESA and pRCESA, to adjust the sampling ratio adaptively. Extensive simulations with realistic traffic traces verify the effectiveness of our proposal.

With momentum gained from programmable data plane (PDP), in-band network telemetry (INT) has been widely considered as a promising technique for realtime network monitoring. In this work, we leverage network coding (NC) to design CodedINT, for improving the visibility of INT in lossy networks. Specifically, we propose to encode the telemetry data in multiple packets with NC and distribute the encoded data over a group of packets. Then, among the group of packets, if we can receive enough ones that satisfy the decoding condition of NC, the whole original telemetry data can be recovered. We first explain the design of CodedINT to elaborate on its operation principle, packet format, and system implementation. Then, we implement and experimentally evaluate CodedINT in a real network testbed. Our experiments demonstrate that in a lossy network with packet loss rate at 80%, CodedINT ensures that 89.48% of the telemetry data carried by INT packets can be recovered successfully.

With the advances on human’s exploration of the universe, interplanetary networks (IPNs) are attracting more and more research interests. However, the unique characteristics of IPNs make many of the networking technologies on Earth not applicable. In this work, we design a routing and data scheduling algorithm that can make interplanetary data transfers (IP-DTs) more scalable and robust. Specifically, we propose an online approach to schedule and route IP-DTs in the distributed way, by leveraging the Lyapunov optimization. With extensive simulations, we show that our proposed algorithm can optimize the performance of IP-DTs with only the information about local queues on each node in an IPN. The simulation results also verify that our algorithm outperforms the existing ones significantly in terms of the average E2E latency of IP-DTs, and properly adjusts the tradeoff between average E2E latency and delivery ratio.

As a promising technology, Flex Ethernet (FlexE) helps to realize deterministic and ultra-low latency in metro and transport networks. Meanwhile, previous studies have confirmed the advantages of the symbiosis of FlexE and elastic optical network (EON) (i.e., a FlexE-over-EON) on resource utilization and cost-effectiveness. In this paper, we consider the cross-layer restoration (CLR) in FlexE-over-EONs based on the FlexE-aware architecture. Specifically, we address the situation where an outage happened on one FlexE switch in the packet layer to bring it offline temporarily and then the affected client flows need to be recovered quickly and proactively. Three CLR strategies are first proposed to fully explore the flexibility of FlexE-over-EON for restoring the affected flows. Then, with the strategies, we formulate an integer linear programming (ILP) model and design an auxiliary graph (AG) based algorithm to reroute the affected flows as well as minimize the additional operational expense (OPEX) incurred during the CLR. Extensive simulations verify the effectiveness of our proposed CLR algorithms.

Hybrid optical/electrical datacenter network (HOE-DCN) uses the inter-rack networks that consist of both electrical Ethernet switches and optical cross-connects (OXCs), for better cost-efficiency and scalability. Meanwhile, to provision dynamic network services well, the operator of an HOE-DCN needs to deploy virtual networks (VNTs) and remap them adaptively. Therefore, this work studies the problem of VNT remapping in an HOE-DCN from a novel perspective, i.e., the remapping schemes should be optimized for not only the network status after the remapping but also the transition to realize it. Specifically, we model this problem as a bilevel optimization, where the upper-level optimization aims at selecting proper virtual machines (VMs) to migrate such that the estimated latency of VM migration can be minimized, and the lower-level optimization determines the actual scheme of VNT remapping for minimizing the number of resource hot-spots. We first formulate a bilevel mixed integer linear programming (BMILP) model for the bilevel optimization, and then propose a polynomial time algorithm based on enumeration to solve it approximately. Extensive simulations verify the effectiveness of our proposal.

With the explosive growth of edge applications in the 5G/B5G era, edge-cloud collaboration (ECC) is playing a prominent role in edge service provisioning. For highly diversified edge-cloud collaborative services (ECSs), the joint allocation of heterogeneous computing resources in heteroge-neous servers and multi-dimensional underlying optical network resources should be conducted. In this paper, we investigate the heterogeneous task scheduling for ECSs over multilayer elastic optical network (ML-EON), which involves the joint allocation of heterogeneous computing resources in edge and cloud servers and high-dimensional network resources. We propose a Task-Node Matching Score (TNMS) based method, which evaluates the fitness for each mapping tuple between each task in ECS and each substrate node in ML-EON, and adaptively generates a specific matching score for each task-node pair. Furthermore, TNMS is extended with a pre-allocation mechanism (TNMS-Pre) to estimate the costs of multi-dimensional resources in ML-EON for virtual link (VL) mapping. The estimated VL mapping costs are integrated into the matching scores to guide the task placement to be cost-efficient. To guarantee the feasibility, a maximal weight matching (MWM) based method is presented to determine the task placement schemes. Simulation results demonstrate the effectiveness of the adaptive scoring for heterogeneous task placement and the pre-allocation mechanism for reducing the ML-EON costs.

The advances on hybrid optical/electrical DCNs (HOE-DCNs) make it easier to support network function virtualization (NFV) with various quality-of-service (QoS) demands. However, the existing approaches for NFV orchestration rely heavily on the control plane, which might lead to scalability and performance issues. In this work, we propose to offload certain network orchestration tasks from the control plane to top-of-rack (ToR) switches, by leveraging the flexibility of programmable data plane (PDP). Specifically, we architect an HOE-DCN whose ToR switches are PDP switches, design the network orchestration system for it, and program the ToR switches, such that they can make quick decisions locally to assist the control plane to provision the service chains of virtual network functions (vNF-SCs) agilely. We prototype our proposal in a small-scale HOE-DCN testbed, and the experimental results show that without much involvement of the control plane, the ToR switches can make local decisions and readjust the provisioning schemes of vNF-SCs adaptively to satisfy their QoS demands.

In this work, we try to combine software-defined networking (SDN), in-band network telemetry (INT), and data analytics to realize a novel closed-loop network automation system. To architect the data plane, we jointly consider P4-based and protocol-oblivious forwarding (POF) based programmable data plane switches (PDP-SWs) to build a generic programmable data plane (G-PDP), such that the two types of PDP-SWs can benefit each other mutually to overcome their own drawbacks. In the control plane, we expand ONOS to ensure that it can effectively manage the PDP-SWs in the G-PDP. We also design and implement data analyzers (DAs) in its data analytics subsystem, and deploy them distributedly in the G-PDP to alleviate the burden of data processing in the control plane. Our proposal is demonstrated experimentally with a network system prototype that consists of six PDP-SWs, and the results confirm that it can realize closed-loop network automation effectively and balance the tradeoff between flexibility and performance properly.

The advances on SmartNICs and programmable data plane (PDP) switches motivate people to deploy virtual network functions (vNFs) on these devices directly for better packet processing performance. Hence, this work considers how to leverage heterogeneous network function virtualization (NFV) platforms to upgrade the provisioning of vNF service chains (vNF-SCs) in a datacenter network (DCN). We design a novel time-efficient heuristic, to plan the service upgrade under a limited budget such that the QoS improvement on latency reduction can be maximized. Specifically, our algorithm first chooses nodes in the DCN to upgrade (i.e., by either equipping SmartNICs on certain servers or replacing switches with PDPSWs), and then adjusts vNF-SC provisioning accordingly.

This Special Issue contains a collection of outstanding papers covering several recent advances in optical networks for 5G communications and beyond. Papers are organized into four categories: network resource planning; optical access networks; optical fronthaul solutions; and autonomous and data-driven network management. In this introduction, a brief overview of the field is given, followed by a summary of the seventeen papers of this Special Issue, and a discussion of future directions in the field.

The fast development of cloud computing and Big Data applications has promoted virtualization technologies such as network function virtualization (NFV), which in turn dramatically increased the amount of sensitive data being transmitted over the optical networks for datacenter interconnections (DCIs). To ensure the physical-layer security in DCIs, people have developed optical transport network (OTN) encryption technologies, i.e., leveraging high-speed encryption cards (ECs) to encrypt OTN payload frames. Although experimental studies have confirmed the benefits of ECs in terms of line-speed processing, low latency, and small encryption overhead, the problem of how to utilize them to build a secure packet-over-optical network with high cost-effectiveness has not been explored yet. In this paper, we study how to realize cost-effective and security-aware multilayer planning in a packet-over-optical network that covers both trusted and untrusted zones, in consideration of OTN encryption. We first formulate an integer linear programming (ILP) model to minimize the total capital expenditure (CAPEX) of the multilayer planning, which includes the costs of OTN linecards (LCs), ECs, and bandwidth resources, and solve the optimization exactly. Then, we prove theNP-hardness of the multilayer planning, and to reduce the time complexity, we propose a column generation (CG) model and design a more time-efficient approximation algorithm based on it. Our simulation results confirm the performance and advantages of our CG-based proposal, i.e., it is much more time-efficient than solving the ILP directly, and outperform the existing heuristic in terms of total CAPEX and costs of used LCs and ECs.

Although network function virtualization (NFV) has been proven to be beneficial in terms of equipment cost, service delivery flexibility, and time-to-market, most of the studies in this area only addressed homogeneous NFV platforms (e.g., with virtual machines (VMs) only). In this work, we argue that by leveraging heterogeneous NFV platforms such as VMs, docker containers, and programmable hardware accelerators (e.g., SmartNICs), one could achieve better flexibility and cost-effectiveness to support virtual network function service chains (vNF-SCs) with various quality-of-service (QoS) requirements. Therefore, we study application-driven provisioning of vNF-SCs over heterogeneous NFV platforms, and design a polynomial-time approximation algorithm to tackle the problem for near-optimal solutions. We first introduce a layered auxiliary graph (LAG) based approach to model the problem of vNF-SC provisioning, and then formulate a novel integer linear programming (ILP) model based on it. Specifically, the ILP model minimizes the total cost of vNF-SC deployment while ensuring that the QoS requirements of all the vNF-SCs are satisfied. To solve the ILP time-efficiently, we propose an approximation algorithm based on linear programming (LP) relaxation and randomized rounding. Extensive simulations confirm that with significantly improved time-efficiency, our proposed algorithm can provide near-optimal solutions whose gaps to the exact ones are bounded.

The service provisioning in multi-domain software-defined elastic optical networks (SD-EONs) is an interesting but difficult problem to tackle, because the basic problem of lightpath provisioning, i.e., the routing and spectrum assignment (RSA), is$\mathcal {NP}$-hard, and each domain is owned and operated by a different carrier. Therefore, even though numerous RSA heuristics have been proposed, there does not exist a universal winner that can always achieve the lowest blocking probability in all the scenarios of a multi-domain SD-EON. This motivates us to revisit the inter-domain provisioning problem in this paper by leveraging deep reinforcement learning (DRL). Specifically, we propose DeepCoop, which is an inter-domain service framework that uses multiple cooperative DRL agents to achieve scalable network automation in a multi-domain SD-EON. DeepCoop employs a DRL agent in each domain to optimize intra-domain service provisioning, while a domain-level path computation element (PCE) is introduced to obtain the sequence of the domains to go through for each lightpath request. By sharing a restricted amount of information among each other, the DRL agents can make their decisions distributedly. To ensure scalability and universality, we design the action space of each DRL agent based on well-known RSA heuristics, and architect the agents based on the soft actor-critic (SAC) scenario. We run extensive simulations to evaluate DeepCoop, and the results show that DeepCoop can adapt to the dynamic environment in a multi-domain SD-EON to always select the best RSA heuristic for minimizing blocking probability, and it outperforms the existing algorithms on inter-domain provisioning in various scenarios. Moreover, we verify that the distributed training implemented in DeepCoop ensures its universality and scalability (i.e., its training and operation do not depend on the topology of the SD-EON).

This article studies the cross-layer network planning that tries to combine flexible Ethernet (FlexE) and elastic optical networks (EONs), for FlexE-over-EONs. We focus our investigation on the most challenging setting, i.e., the FlexE-over-EONs based on the FlexE-aware architecture, and consider both single-hop and multi-hop scenarios for the cross-layer planning. For the single-hop scenario, we assume that all the client flows are routed over end-to-end lightpaths in the EON. We formulate a mixed integer linear programming (MILP) model for this problem, transform it into the class constrained bin packing problem (CCBP), and leverage the primal-dual interior-point (PDIP) method to propose a polynomial-time approximation algorithm for it. Then, for the multi-hop scenario, we use a more realistic assumption that each client flow can be routed over multiple lightpaths in the EON. We show that after solving the virtual topology design, the cross-layer planning in this scenario can be transformed into that in the single-hop scenario. Therefore, an integer linear programming (ILP) model is formulated to tackle the virtual topology design, and we design a polynomial-time approximation algorithm for it by modifying the well-known branch-and-bond method. To evaluate the performance of our two-step method for the multi-hop scenario, we also propose a heuristic algorithm. Extensive simulations verify that regarding large-scale cross-layer planning for FlexE-over-EONs, our approximation algorithms are significantly more time-efficient than the ILP/MILP models, and their solutions have bounded gaps to the optimal ones and are much better than those of the heuristic.

Natural disasters have challenged the survivability of Elastic Optical Inter-DataCenter Networks (EO-DCNs), and it is urgent to establish efficient disaster protection schemes. In this paper, we investigate the disaster-resilient service provisioning problem leveraging cooperative storage system (CSS). Instead of mirrored content backup on a single DC, our proposed CSS partitions a required content into no less than three fragments if possible, each of which is then stored on a DC located in different disaster zones. Accordingly, multi-path routing with the adaptive number of working paths to distinct DCs is employed to serve each request, while a protection path is computed to protect against a disaster failure. Our main objective is to jointly minimize the spectrum usage and maximal occupied frequency slot index (MOFI) subject to disaster resilience. Besides, we also expect to cut the content storage space. To this end, we propose for the first time a CSS-based dedicated end-to-content path protection (CDP), which allows service provisioning through multiple paths with the adaptive number of paths rather than a single path. This consequently reduces at least half of the reserved spectrum on the protection path. To find the optimal CDP strategy, we formulate the studied problem as an integer linear program (ILP) and then propose a fast heuristic algorithm. Observing the trade-off between the spectrum usage and content storage space, we further design a maximum-CDP (M-CDP), which generates the maximum number of working paths to reduce the content storage space. Simulations are conducted to compare the proposed schemes with the traditional protection strategy using mirrored storage and single-path routing. Numerical results demonstrate that the proposed CSS-based protection schemes enable to cut up to 21.6% of the spectrum usage and 15% of the content storage space.

In the network planning of software-defined optical networks (SDONs), the control plane design is of great importance because it directly affects the performance and reliability of network control and management (NC&M). In this article, we consider the planned physical-layer attacks from a rational attacker, which can analyze the control plane of an SDON and target its attacks to the most vulnerable part. To address such attacks, we model the control plane design as a bilevel optimization, where the upper-level optimization is for the network planner to design the control plane whose vulnerability to planned attacks is minimized, while the lower-level optimization is for the attacker to plan its attacks such that the control plane can be disturbed as severely as possible. We first develop two approaches to solve the bilevel model exactly. Specifically, we first leverage the cutting plane method to solve it directly, and then transform it into a single-level mixed integer linear programming (MILP) model with the Bellman method for problem solving. To improve the time efficiency for large-scale problems, we also propose a polynomial-time approximation algorithm based on linear programming (LP) relaxation and randomized rounding. Extensive simulations with various physical topologies verify the effectiveness of our proposals.

Recently, the programmable data plane (PDP) switches have been considered as the key enablers for in-network computing. However, the limited memory resources in them for flow tables might restrict their performance. This work addresses this challenge by studying how to optimize the placements of flow tables in the external memory on multiple servers, and to access them with remote direct memory access (RDMA) for ensuring low latency. Specifically, we consider a data-center network (DCN) that uses PDP switches as top-of-rack (ToR) switches, and propose and optimize the hybrid flow table installation (hFT-INST) on each ToR switch. With hFT-INST, the switch can either store flow tables in its local memory or use RDMA to install and access them remotely in its rack servers. We first design the protocol and operation procedure of hFT-INST. Then, regarding the key problem of hFT-INST, i.e., how to place the flow tables on the external memory on different servers, we take a few practical parameters into account, and formulate a mixed integer linear programming (MILP) model to tackle it. Next, the optimization in the MILP is transformed into a capacitated facility location problem (CFLP) with additional constraints. We further transform it into a k-median problem through pre-processing, and design a polynomial-time approximation algorithm to solve the problem. Extensive simulations confirm the performance of our proposed algorithm. We also prototype our design of the hFT-INST, and conduct experiments to demonstrate its feasibility.

The fast development of high-performance and flexible SmartNICs and programmable data plane switches (PDP-SWs) has motivated people to consider the deployment of virtual network functions (vNFs) on them. Hence, together with traditional virtual machines (VMs), SmartNICs and PDP-SWs form heterogeneous network function virtualization (NFV) platforms for realizing vNF service chains (vNF-SCs). In this work, we consider the transition from software-based homogeneous NFV platforms to the heterogeneous ones, and study how to optimize the service upgrade of vNF-SCs. Specifically, the service upgrade is divided into two steps, which are 1) selecting servers/switches in the substrate network (SNT) to upgrade, which is done by adding SmartNICs to servers and replacing traditional switches with PDP-SWs, under a fixed budget, and 2) redeploying the existing vNF-SCs in the updated SNT to maximize the quality-of-service (QoS) improvement on latency reductions. We first formulate an integer linear programming (ILP) model to optimize the overall service upgrade, then design two correlated optimizations for its two steps, and finally propose polynomial-time approximation algorithms to solve the optimizations. The results of extensive simulations confirm that our proposed algorithm outperforms the existing benchmarks in various network scenarios, and achieves better tradeoff between performance and time-efficiency.

The rapid development of software-defined networking (SDN) has promoted the idea of programmable data plane (PDP), which opens up unprecedented opportunities for realizing powerful and timely network monitoring. This work explores the advantages of PDP to merge two famous techniques (i.e., the segment routing (SR) and in-band network telemetry (INT)) seamlessly for highly-efficient and adaptive network monitoring. Specifically, by leveraging the protocol-oblivious forwarding (POF), we propose SR-INT, which time-multiplexes the header fields in each packet for INT and SR, and keeps packet length constant end-to-end even though both INT and SR are used. Hence, our proposal can enjoy the benefits of INT and SR, while avoiding the accumulated overheads due to simultaneous usage. We design the packet format of SR-INT, and lay out its packet processing procedure to guarantee that the configuration of SR-INT can be adjusted dynamically to adapt to the requirements of network monitoring. We implement and experimentally demonstrate SR-INT in a POF-based SDN environment. Our results show that SR-INT not only reduces the bandwidth overheads of using SR and INT simultaneously but also simplifies the operations in software-based POF switches.

Natural disasters have challenged the survivability of Elastic Optical Inter-DataCenter Networks (EO-DCNs), and it is urgent to establish efficient disaster protection schemes. In this paper, we investigate the disaster-resilient service provisioning problem leveraging cooperative storage system (CSS) and multipath routing. The studied problem involves data center (DC) assignment, content partition and placement, working/protection paths computation, as well as spectrum allocation. Our main objective is to jointly minimize the spectrum usage and maximal frequency slot index. Besides, we also expect to cut the content storage space. To this end, we first formulate the studied CSS-based protection problem as an integer linear program (ILP), and then propose a fast heuristic algorithm to improve the network scalability in large instances. Numerical simulations are conducted to compare the proposed schemes with the traditional protection strategy using entire content replication and single path routing. Simulation results demonstrate that the CSS-based protection scheme enables to cut up to 17.8% of the spectrum usage and half of the content storage space.

Recently, hybrid optical/electrical datacenter networks (HOE-DCNs) have been considered as a promising DCN architecture, because they merge the merits of electrical packet switching (EPS) and optical circuit switching (OCS). This paper considers the reconfiguration of virtual networks (VNTs) in an HOE-DCN to address the dynamic nature of emerging network services. Specifically, we study the problem that given the original and new virtual network embedding (VNE) schemes of several VNTs, how to schedule parallel virtual machine (VM) migrations in batches to realize the VNT reconfiguration within the shortest time. We design two algorithms to reconFigure the inter-rack network in an HOE-DCN in steps, schedule VMs to migrate accordingly, and allocate bandwidth to the VM migrations. The first algorithm uses the one-shot approach, where all the VM migrations are conducted in parallel within the shortest possible time. We formulate a linear programming (LP) to solve the bandwidth allocations in it exactly. Next, to relieve the bandwidth competition introduced by the one-shot approach, we propose the second algorithm by leveraging the multi-shot approach, i.e., invoking multiple batches of parallel VM migrations such that the reconfiguration time can be further reduced. Extensive simulations verify the effectiveness of our proposals.

It is known that the design of the control plane (CP) is vital in the network planning for software-defined networking (SDN), while to improve throughput and enlarge geographical coverage, optical networks are commonly used as the physical infrastructure of SDN. However, physical-layer attacks can disrupt the operation of an optical network and thus complicate the CP design. In this work, we consider planned physical-layer attacks when designing the CP of an SDN that uses an optical network as its physical infrastructure. We first show that the CP design problem should be modeled as a bilevel optimization, where the network planner designs the CP with the minimum vulnerability to physical-layer attacks (i.e., the upper-level optimization), while the attacker plans and launches attacks to disrupt the designed CP (i.e., the lower-level optimization). Then, the bilevel mode is transformed into a mixed integer linear programming (MILP) model, which can solve the CP design problem exactly. We also propose a heuristic to tackle the problem time-efficiently.

We study how to achieve cost-effective and security-aware multilayer planning for an optical transport network (OTN) that covers both trusted and untrusted zones and has the option to choose encryption solution deployment (ESD) architectures based on traffic condition. We first formulate an integer linear programming (ILP) model to solve the optimization exactly, and then propose a novel heuristic based on collapsed auxiliary graphs (CAGs) to have improved time-efficiency.

It is known that by leveraging programmable data plane, in-band network telemetry (INT) can be realized to provide a powerful and promising method to collect realtime network statistics for monitoring and troubleshooting. However, existing INT implementations still exhibit a few drawbacks such as lack of runtime-programmability and relatively high overheads due to per-packet operation. In this work, we propose and design a runtime-programmable selective INT system, namely, Sel-INT, to resolve these issues. Specifically, we first design a runtime-programmable selective INT scheme based on protocol oblivious forwarding (POF), and then prototype our design by extending the famous OpenvSwitch (OVS) platform to obtain a software switch that supports Sel-INT and implementing a Data Analyzer to parse, extract and analyze the INT data. Our implementation of Sel-INT is verified and evaluated in a real network testbed that consists of a few stand-alone software switches. The experimental results demonstrate that Sel-INT can not only adjust the sampling rate of INT in runtime but also program the corresponding data types dynamically, and they also confirm that our proposal can ensure proper accuracy and timeliness for network monitoring while greatly reducing the overheads of INT.

Recently, the advances on programmable data plane (PDP) promote the studies on the network virtualization in a PDP-based substrate network (SNT). In this paper, we address the table resource virtualization and network slicing in PDP-based SNTs. We first leverage the idea of “Big-Switch” to design an effective table resource virtualization scheme in which the flow tables of a virtual switch (V-SW) can be installed in multiple adjacent substrate switches (S-SWs) according to their table sizes, while the feasible table size(s) on each S-SW are determined based on the global information of the SNT. By doing so, we can regulate the flow tables in the S-SWs in a more organized way to minimize memory fragmentation. Next, we address the network slicing based on the virtualization scheme, and come up with a three-layer VNE problem. To the best of our knowledge, such a VNE problem has not been studied before and the existing algorithms designed for traditional two-layer VNE problems can hardly solve it. We formulate an integer linear programming (ILP) model to solve the VNE problem exactly, and also design a time-efficient heuristic that can provide near-optimal solutions. Finally, we implement the heuristic in TPVX, which is a network hypervisor based on protocol-oblivious forwarding (POF), and also improve its performance by introducing source routing. The new TPVX is experimentally demonstrated in a real network testbed, and the results verify that our proposal maintains the additional latency caused by the three-layer VNE well and would not degrade the network services in virtual networks (VNTs).

The symbiosis of network virtualization and software-defined networking (SDN) enables an infrastructure provider (InP) to build various virtual software defined networks (vSDNs) over a shared substrate network (SNT). To handle a dynamic network environment, the InP may need to reconfigure the mapping schemes of vSDNs for a variety of reasons. Although previous studies have addressed how to calculate the new virtual network embedding (VNE) schemes for vSDN reconfiguration under different objectives, the transition to migrate vSDNs from their original VNE schemes to new ones is still under-explored. Hence, this article studies how to realize parallel and hitless vSDN reconfiguration, by leveraging the “makebefore-break” scenario. We come up with a generic solution to optimize the transition to remap vSDNs to new VNE schemes, such that the remappings can be done in the parallel, hitless and resource-efficient manner, as long as the new VNE schemes are feasible. More specifically, our proposal is the multi-stage parallel vSDN reconfiguration based on maximal connected reconfigurable subgraph (MCRSG). To ensure the efficiency of our proposal, we formulate the optimization for selecting MCRSGs to reconfigure in each stage, and prove the NP-hardness of the problem. Then, we design an approximation algorithm based on Lagrangian relaxation to solve it time-efficiently. Extensive simulations verify that the proposed algorithm can obtain nearoptimal solutions quickly. In addition to the algorithmic study, we also realize our multi-stage parallel vSDN reconfiguration in a practical NVH system, and demonstrate its performance in a real network testbed. Our experimental study identifies in what condition losing of packets during remapping would be inevitable, studies the tradeoff between reconfiguration latency and packet loss rate, and reveal an empirical method to adjust key parameters of our NVH system, for adapting to various network environments.

In this work, we try to further enhance the flexibility and cost-effectiveness of network function virtualization (NFV) by considering virtual network function service chaining (vNF-SC) in a heterogeneous NFV environment that can instantiate vNFs on virtual machines (VMs), docker containers, and SmartNICs. Specifically, we first lay out the network model, build a real network testbed that supports vNF deployment on kernel-based VMs, docker containers, and commercial SmartNICs, and then conduct experiments to measure the throughput and latency of traffic processing and memory usage of four types of vNFs implemented on them. Next, based on the measurement results, we formulate an integer linear programming (ILP) model to optimize the application-aware vNF-SC provisioning in the heterogenous NFV environment. Finally, we design and perform two experiments to demonstrate that our heterogeneous NFV environment can combine the advantages of VM/docker container/SmartNIC to provide enhanced flexibility for realizing on-demand and application-aware vNF-SC composition.

In this paper, we explore how much flexible Ethernet (FlexE) and elastic optical network (EON) can mutually benefit each other, given their flexibilities in managing Ethernet channels and optical spectra. Specifically, we consider three FlexE architectures, i.e., FlexE-unaware, FlexE-aware and FlexE-terminal, explain how to integrate them with EON, and formulate an mixed integer linear programming mode (MILP) to optimize the corresponding network design of each integration. The exact solutions provided by the MILP models confirm that FlexE and EON can mutually benefit each other when the FlexE-aware and FlexE-terminal architectures are considered, and the more flexible the FlexE architecture is, the more benefits the integration can get. Meanwhile, the solutions also show that fixed-grid wavelength-division multiplexing (WDM) networks cannot fully explore the advantages of the FlexE architectures due to the rigid transmission scheme. Hence, our results suggest that integrating FlexE and EON would be necessary in the future.

Elastic Optical Network (EON) has been considered as a promising optical networking technology to architect the next-generation backbone networks. The spectrum management in EONs is directly determined by the Routing and Spectrum Assignment (RSA). Generally, the RSA is solved by routing the requests with lightpaths first and then assigning spectrum resources to the lightpaths to optimize the spectrum usage. Thus, the spectrum assignment explicitly determines the spectrum usage. Besides, the network topology, traffic distribution and routing scheme implicitly impact the spectrum usage. However, few related work involves this implicit impact. In this paper, we aim to provide a thoroughly theoretical analysis on the impact of the three key factors on the spectrum usage. To this end, two theoretical chains are proposed: (1) The optimal spectrum usage can be measured by the chromatic number of the conflict graph, which is positively correlated to the intersecting probability, i.e., the smaller the intersecting probability, the smaller the optimal spectrum usage; (2) The intersecting probability is determined by the network topology, traffic distribution and routing scheme via a quadratic programming parameterized with a matrix of conflict coefficients. The effectiveness of our theoretical analysis has been validated by extensive numerical results.

In this paper, we propose an incentive-driven virtual network function service chaining (VNF-SC) framework for optimizing the cross-stratum resource provisioning in multi-broker orchestrated inter-datacenter elastic optical networks (IDC-EONs). The proposed framework employs a non-cooperative hierarchical game-theoretic mechanism, where the resource brokers and the VNF-SC users play the leader and the follower games, respectively. In the leader game, the brokers calculate VNF-SC service schemes for users and compete for the provisioning tasks. While in the follower game, the users compete for VNF-SC services for jointly optimizing the resource cost and the received quality-of-service. We first elaborate on the modeling of the follower game, discuss the existence of Nash equilibrium and propose a mixed-strategy gaming approach enabled by an auxiliary graph-based algorithm to facilitate users selecting the most appropriate service schemes. Then, under the assumption that the brokers are aware of the principle of the follower game, we present the model for the leader game and develop a time-efficient heuristic algorithm for brokers to compete for the provisioning tasks. Simulations show that the proposed incentive-driven VNF-SC framework significantly improves the network throughput (i.e., $>4.8 \times$ blocking reduction) while assisting users and brokers in achieving higher utilities compared with existing solutions.

Recently, a number of WiFi access points (APs) have been densely deployed to provide widely available, high-performance Internet services. As such, an energy efficiency issue becomes crucial toward the design of green wireless local area networks (WLANs). In this paper, we propose a resource and re-association scheduling algorithm (referred to RAS) based on Benders' decomposition to reduce the energy consumption. In particular, we endeavor to aggregate WLAN users on the small number of APs and turn off many APs without compromising users' quality of experience (QoE) and system coverage. We conduct the analysis by using real trace data and formulate the energy minimization as the mixed integer nonlinear programming (MINLP) problem. We then transform and solve the original problem through the RAS algorithm. For practical implementation, we further propose the fast RAS (Fast-RAS) algorithm to relax the binary integer constraints and transform the MINLP problem into the nonlinear programming (NLP) problem. The relaxed problem then can be solved by using Feasible Pump algorithm with the reduced computational complexity. We evaluate the performance of RAS and Fast-RAS algorithms via extensive simulations. The results demonstrate that the Fast-RAS algorithm can achieve up to 20% improvement of energy saving comparing with existed methods.

The combination of network virtualization and software-defined networking enables an infrastructure provider to create software-defined virtual networks (vSDNs) over a shared substrate network (SNT), for supporting new network services more timely and cost-effectively. Meanwhile, as both the services and traffic in the Internet are becoming more and more dynamic, how to properly maintain vSDNs in a dynamic network environment exhibits increasing importance but still has not been fully explored. In this paper, we conduct a study on how to realize proactive and hitless vSDN reconfiguration to balance the utilization of ternary content-addressable memory (TCAM) in a dynamic SNT. Specifically, we consider both algorithm design and system prototyping. From the algorithmic perspective, we try to solve the problems of “what to reconfigure” and “how to reconfigure”. A selection algorithm is designed to proactively choose the virtual switches (vSWs) that should be migrated to other substrate switches for balancing TCAM utilization, i.e., solving what to reconfigure. Then, for the problem of how to reconfigure, i.e., where to re-map the selected vSWs and the virtual links connecting to them, we formulate a mixed integer linear programming model to solve it exactly, and design two heuristics to improve time efficiency. Next, we move to the system part, implement the proposed algorithms in our protocol-oblivious forwarding enabled network virtualization hypervisor system, and conduct experiments to demonstrate proactive and hitless vSDN reconfiguration. The experimental results indicate that our proposal does make vSDN reconfiguration transparent to the vSDNs' virtual controllers and proactive, and when reconfiguring a vSDN with live traffic, it achieves hitless operations without traffic disruption.

Recently, there has been an increase in the number of location-based services and applications. It is common for these applications to provide facilities or rewards for users who visit specific venues frequently. This creates the incentive for dishonest users to lie about their location and submit fake check-ins by changing their GPS data. To solve this issue, different distributed location proof schemes have been proposed to generate location proofs for mobile users. However, these schemes have some drawbacks: (1) they are vulnerable to either Prover-Prover or Prover-Witness collusions, (2) the location proof generation process is slow when users adopt a long private key, and (3) their implementation requires some hardware changes on mobile devices. To address these issues, we propose the Secure, Privacy-Aware and collusion Resistant poSition vErification (SPARSE) scheme to generate private location proofs for mobile users. SPARSE has a distributed architecture designed for ad-hoc scenarios in which mobile users generate location proofs for each other. Since we do not integrate any distance bounding protocol into SPARSE, it becomes an easy-to-implement scheme in which the location proof generation process is independent of the length of the users' private key. We provide a comprehensive security analysis and simulation which show that SPARSE provides privacy protection as well as security properties for users including integrity, unforgeability and non-transferability of the location proofs. Moreover, it achieves a highly reliable performance against collusions.

In this work, we try to address the problem of memory fragmentation in ternary content addressable memory (TCAM) in programmable data plane (PDP), by designing and implementing a novel network hypervisor for PDP, namely, TPVX. TPVX realizes the virtualization of table resources in PDP with global consideration, i.e., when mapping tenant flow tables to physical switches, TPVX considers their table sizes and the pre-formatted sub-tables in the physical network to improve TCAM utilization and avoid memory fragmentation. Our experimental results verify that with TPVX, the utilization of the table resources in PDP can be improved dramatically and the extra processing latency due to the newly-introduced overheads can be maintained well simultaneously.

This paper discusses the network architecture and mechanisms used for advancing the economics of multi-domain software-defined elastic optical networks (SD-EONs) with the considerations of both noncooperative and cooperative games among the incentive-driven brokers. Specifically, in order to architect multi-domain SD-EONs such that the end-to-end lightpath provisioning across multiple domains can be facilitated cost-effectively, we propose to introduce a management plane that contains incentive-driven brokers. As the brokers may compete or cooperate with each other to offer cross-domain lightpath provisioning services due to profits, we design and elaborate on the operation mechanisms for both incentive-driven broker competition (i.e., noncooperative gaming) and market-share based broker bargain (i.e., cooperative gaming). The experimental results that are obtained with an OpenFlow-based control plane SD-EON testbed are also presented to verify the cost-effectiveness of the proposed mechanisms.

With network virtualization, an infrastructure provider can create virtual software-defined networks (vSDNs) over a shared substrate network and lease them to service providers (SPs). This enables the SPs to run their Big Data applications in a short time-to-market, flexible and cost-effective way. However, in a dynamic network, both the instances of vSDNs and the traffic in each vSDN can change over time, which would degrade the optimality of the embedding schemes of vSDNs and even cause serious reliability issues. Therefore, in this work, we extend our protocol-oblivious forwarding (POF) based network virtualization hypervisor (NVH) system (i.e., PVX) to realize hitless vSDN migration to avoid ternary content addressable memory (TCAM) depletion. Specifically, we design the PVX system to realize the vSDN migration that is transparent to the controllers of vSDNs and would cause zero or very few packet losses. The proposed PVX is then implemented in a real network testbed, and we conduct experiments to verify its effectiveness.

Software-defined networking (SDN) has been considered as a break-through technology for the next-generation Internet. It enables fine-grained flow control that can make networks more flexible and programmable. However, this might lead to scalability issues due to the possible flow state explosion in SDN switches. SDN-based source routing can reduce the volume of flow-tables significantly by encoding the path information into packet headers. In this paper, we leverage the protocol-oblivious forwarding instruction set to design protocol-oblivious source routing (POSR), which is a protocol-independent, bandwidth-efficient, and flow-table-saving packet forwarding technique. We lay out the packet format for POSR, come up with the packet processing pipelines for realizing unicast, multicast, and link failure recovery, and implement POSR in a protocol-oblivious forwarding-enabled SDN network system. Experiments are then performed in a network testbed, which consists of 14 stand-alone SDN switches, to validate the advantages of POSR. Specifically, we compare POSR with several OpenFlow-based benchmarks for unicast, multicast, and link failure recovery, and confirm that POSR can reduce flow-table utilization effectively, shorten path setup latency and expedite link failure recovery.

Inter-datacenter (inter-DC) networks are essential for large enterprises to deliver high-quality services to end-users. Since DCs are vulnerable to natural disasters, an inter-DC network operator needs an effective emergency backup plan to evacuate the endangered data out in case of a progressive disaster whose status can be predicted by an early warning system. In this paper, we try to solve the problem of emergency backup in inter-DC networks with progressive disasters. We first utilize the time-expanded network (TEN) approach to model the time-variant inter-DC network during a progressive disaster as a variant TEN (VTEN) and convert the dynamic flow scheduling for emergency backup to a static one. Then, with the VTEN, we formulate an optimization model to maximize the profit from the emergency backup in consideration of data values and resource costs. Although this large-scale optimization can be solved in a distributed way by leveraging the alternation direction method of multipliers (ADMM), we find that one of its subproblems is nontrivial in the distributed setting. We propose a novel inexact ADMM approach to resolve the issue induced by the subproblem, and prove that the proposed algorithm can converge to the optimal solution. The results from extensive simulations confirm that our algorithm is robust and time-efficient, and outperforms several benchmarks in terms of backup profit and running time.

In the Internet, to enable emerging network services such as e-Health to be delivered to numerous people anytime and anywhere, mobility management plays an important role. In this work, we leverage the protocol-oblivious forwarding (POF) to design a novel network system that can realize highly-efficient mobility management and overcome the drawbacks of existing approaches. Moreover, we explore the forwarding plane programmability provided by POF to realize dynamic virtual network function (vNF) deployment for traffic adaption. We implement our system and conduct experiments to verify that it functions well for NFV-assisted mobility management and achieves reduced handover latency and enhanced QoE.

We study how to embed virtual software-defined networks (vSDNs) cost-effectively over a substrate network with distributed network virtualization hypervisors (NVHs) for virtual datacenter (vDC) formulation to support Big Data applications. Specifically, we try to jointly optimize the embedding schemes of the control and data planes of each vSDN, i.e., to minimize the data plane's resource consumption and limit the number of NVHs used in the control plane simultaneously. We first formulate an integer linear programming (ILP) model to solve the problem exactly, and then design a heuristic to reduce the time complexity. Simulation results suggest that our proposed algorithms can embed vSDNs cost-effectively and significantly outperform the existing scheme based on global resource capacity (GRC).

This paper considers the emergency backup in an inter-datacenter (inter-DC) network whose topology is time-variant due to the progress of a disaster. We first transform the dynamic backup into a static flow problem through building a variable time-expanded network (V-TEN). Then, by considering both data utility and resource cost, we formulate an optimization to maximize the backup profit and leverage the alternating direction method of multipliers (ADMM) to design a time-efficient and distributed algorithm. Simulation results show that our ADMM-based algorithm outperforms several existing ones.

Network function virtualization (NFV) is a promising technology to decouple the network functions from dedicated hardware elements, leading to the significant cost reduction in network service provisioning. As more and more users are trying to access their services wherever and whenever, we expect the NFV-related service function chains (SFCs) to be dynamic and adaptive, i.e., they can be readjusted to adapt to the service requests' dynamics for better user experience. In this paper, we study how to optimize SFC deployment and readjustment in the dynamic situation. Specifically, we try to jointly optimize the deployment of new users' SFCs and the readjustment of in-service users' SFCs while considering the trade-off between resource consumption and operational overhead. We first formulate an integer linear programming (ILP) model to solve the problem exactly. Then, to reduce the time complexity, we design a column generation (CG) model for the optimization. Simulation results show that the proposed CG-based algorithm can approximate the performance of the ILP and outperform an existing benchmark in terms of the profit from service provisioning.

In elastic optical networks, two lightpaths sharing common fiber links might have to be isolated in the spectrum domain with a proper guard-band to prevent crosstalk and/or reduce physical-layer security threats. Meanwhile, the actual requirements on guard-band sizes can vary for different lightpath pairs, because of various reasons. Therefore, in this paper, we consider the situation in which the actual guardband requirements for different lightpath pairs are different, and formulate the distance spectrum assignment (DSA) problem to investigate how to assign the spectrum resources efficiently in such a situation. We first define the DSA problem formally and prove its NP-hardness and inapproximability. Then, we analyze and provide the upper and lower bounds for the optimal solution of DSA, and prove that they are tight. In order to solve the DSA problem time-efficiently, we develop a two-phase algorithm. In its first phase, we obtain an initial solution and then the second phase improves the quality of the initial solution with random optimization. We prove that the proposed two-phase algorithm can get the optimal solution in bipartite DSA conflict graphs and can ensure an approximate ratio of O(log(|V|)) in complete DSA conflict graphs, where |V| is the number of vertices in the conflict graph, i.e., the number of lightpaths to be considered. Numerical results demonstrate our proposed algorithm can find near-optimal solutions for DSA in various conflict graphs.

It is known that multicast provisioning is important for supporting cloud-based applications, and as the traffics from these applications are increasing quickly, we may rely on optical networks to realize high-throughput multicast. Meanwhile, the flexible-grid elastic optical networks (EONs) achieve agile access to the massive bandwidth in optical fibers, and hence can provision variable bandwidths to adapt to the dynamic demands from the cloud-based applications. In this paper, we consider all-optical multicast in EONs in a practical manner and focus on designing impairment- and splitting-aware multicast provisioning schemes. We first study the procedure of adaptive modulation selection for a light-tree, and point out that the multicast scheme in EONs is fundamentally different from that in the fixed-grid wavelength-division multiplexing networks. Then, we formulate the problem of impairment- and splitting-aware routing, modulation and spectrum assignment (ISa-RMSA) for all-optical multicast in EONs and analyze its hardness. Next, we analyze the advantages brought by the flexibility of routing structures and discuss the ISa-RMSA schemes based on light-trees and light-forests. This paper suggests that for ISa-RMSA, the light-forest-based approach can use less bandwidth than the light-tree-based one, while still satisfying the quality of transmission requirement. Therefore, we establish the minimum light-forest problem for optimizing a light-forest in ISa-RMSA. Finally, we design several time-efficient ISa-RMSA algorithms, and prove that one of them can solve the minimum light-forest problem with a fixed approximation ratio.

This paper studies the problem of service chain (SC) deployment. Specifically, we try to place virtual network functions (vNFs) on network nodes and connect the vNFs in sequence through link mapping. We start with the offline problem. An integer linear programming (ILP) model is formulated to minimize the total SC deployment cost. With the ILP, we prove that the offline problem is NP-hard and propose a time-efficient heuristic based on affiliation-aware vNF placement. Then, we move to the online problem, and design a forecast-assisted online SC deployment algorithm that includes the prediction of future vNF requirements. Simulation results show that the online algorithms can reduce the blocking probability of SC requests and increase the service provider's profit from SC deployment effectively.

Software-defined elastic optical networks (SD-EONs) provide operators more flexibility to customize their optical infrastructure dynamically and adaptively, and network virtualization, i.e., infrastructure-as-a-service (IaaS), enables multiple tenants to share the substrate infrastructure efficiently. In this paper, we study how to provision virtual SD-EONs (vSD-EONs) with the correlated data and control plane embedding (χ-VNE) that considers the quality-of-service (QoS) of virtual control plane (vCP), i.e., availability and control channel latency. We propose a χ-VNE algorithm to solve the problem, design the network system to realize it, and accomplish proof-of-concept experimental demonstrations in an OpenFlow-based network testbed. Numerical and experimental results indicate that the proposed algorithm and system function well and can realize on-demand and reliable vSD-EON provisioning.

It has already been confirmed that software-defined networking (SDN) can make the networks more programmable, adaptive and application aware. However, due to the large-scale and geographically-distributed nature of wide-area networks (WAN), the scalability could become a critical issue if we incorporate SDN for WANs (i.e., realizing SD-WANs). In this paper, we design and implement a novel network system that can leverage source routing with the protocol-oblivious forwarding (POF) to facilitate efficient e-Health data transfers with low setup latency. We develop the POF-based source routing protocol to realize a pipeline based packet processing procedure, which can replace the table-lookup based approach in traditional SDN networks and make the forwarding plane more efficient. The proposed scheme is demonstrated experimentally, and the results verify that with it, the flow-tables installed in each core switches in a POF-controlled SD-WAN can be minimized and the path setup latency of traffic flows can be reduced significantly as well.

It is known that by incorporating network function virtualization (NFV) in inter-datacenter (inter-DC) networks, we can use the network resources more intelligently to deploy new services faster. This paper considers an inter-DC elastic optical network (IDC-EON) and studies how to orchestrate the multicast-oriented NFV trees (M-NFV-Ts) in it efficiently. We first consider an offline scenario in which all the M-NFV-Ts are known and need to be served in the network. A mixed integer linear programming (MILP) model is formulated to solve the problem exactly, and we also propose a heuristic based on path-intersection (PI) to reduce the time complexity. With extensive simulations, we show that the proposed heuristic can approximate the MILP's performance on low-cost M-NFV-T provisioning but only requires much shorter running time. Next, the online scenario where the M-NFV-Ts can come and leave on-the-fly is addressed, and we leverage PI to design two online algorithms for orchestrating dynamic M-NFV-Ts in IDC-EONs, i e., with either the batch (B-PI) or sequential (S-PI) scheme. Simulation results indicate that compared with S-PI, B-PI can reduce blocking probability effectively.

This paper tries to solve the location-constrained virtual network embedding (LC-VNE) problem efficiently. We first investigate the complexity of LC-VNE, and by leveraging the graph bisection problem, we provide the first formal proof of the NP-completeness and inapproximability result of LC-VNE. Then, we propose two novel LC-VNE algorithms based on a compatibility graph (CG) to achieve integrated node and link mapping. In particular, in the CG, each node represents a candidate substrate path for a virtual link, and each link indicates the compatible relation between its two endnodes. Our theoretical analysis proves that the maximal clique in the CG is also the maximum one when the substrate network has sufficient resources. With CG, we reduce LC-VNE to the minimumcost maximum clique problem, which inspires us to propose two efficient LC-VNE heuristics. Extensive numerical simulations demonstrate that compared with the existing ones, our proposed LC-VNE algorithms have significantly reduced time complexity and can provide smaller gaps to the optimal solutions, lower blocking probabilities, and higher time-average revenue as well.

Flexible-grid elastic optical networks (EONs) have attracted intensive research interests for the agile spectrum management in the optical layer. Meanwhile, due to the relatively small spectrum allocation granularity, spectrum fragmentation has been commonly recognized as one of the key factors that can deteriorate the performance of EONs. To alleviate spectrum fragmentation, various defragmentation (DF) schemes have been considered to consolidate spectrum utilization in EONs through connection reconfigurations. However, most of the previous approaches operate in the sequential manner (Seq-DF), i.e., involving a sequence of reconfigurations to progressively migrate highly fragmented spectrum utilization to consolidated state. In this paper, we propose to perform the DF operations in a parallel manner (Par-DF), i.e., conducting all the DF-related connection reconfigurations simultaneously. We first provide a detailed analysis on the latency and disruption of Seq-DF and Par-DF in EONs, and highlight the benefits of Par-DF. Then, we study two types of Par-DF approaches in EONs, i.e., reactive Par-DF (re-Par-DF) and proactive Par-DF (pro-Par-DF). We perform hardness analysis on them, and prove that the problem of re-Par-DF is NP-hard in the strong sense while pro-Par-DF is an APX-hard problem. Next, we focus on pro-Par-DF and propose a Lagrangian-relaxation (LR) based heuristic to solve it time-efficiently. The proposed algorithm decomposes the original problem into several independent subproblems and ensures that each of them can be solved efficiently. The LR based approach informs us the proximity of current feasible solution to the optimal one constantly, and offers a near-optimal performance (relative dual gap 5%) within 500 iterations in most simulations. Extensive simulations also verify that the proposed pro-Par-DF approach outperforms Seq-DF in terms of the DF Latency, Disruption and Cost.

With the development of software-defined networking (SDN), people start to realize that the protocol-dependent nature of OpenFlow, i.e., the matching fields are defined according to existing network protocols (e.g., Ethernet and IP), will limit the programmability of forwarding plane and cause scalability issues. In this work, we focus on Protocol-Oblivious Forwarding (POF) [1], which can make the forwarding plane reconfigurable, programmable and future-proof with a protocol-independent instruction set. We design and implement a POF-based flexible flow converging (F-FC) scheme to reduce the number of flow-entries for enhanced scalability. To evaluate the POF system experimentally, we build a network testbed that consists of both commercial and software-based POF switches. Network experiments with real-time video streaming in the proposed POF system demonstrate that our POF-based F-FC approach can outperform conventional schemes.

As multi-domain elastic optical networks (EONs) can enhance network scalability, extend service reach, and accommodate the inter-operability issues, it is very relevant to consider them in practical network operations. In this work, we study the problem of how to achieve energy-aware service provisioning in a multi-domain EON, where the optoelectronic regenerators only exist in border nodes. We consider dynamic lightpath requests and propose two algorithms, i.e., the greedy regenerator allocation (GRA) and the set-cover based regenerator allocation (STC), to realize the joint optimization of routing, modulation and spectrum assignment (RMSA) and regenerator allocation. The algorithms are evaluated with extensive simulations that use multi-domain EONs built with different regenerator placement strategies. The results verify that GRA and STC outperform the existing algorithm for energy-aware multi-domain service provisioning, and STC achieves the best performance in terms of both blocking probability and power efficiency.

This paper develops and evaluates a new protection solution for pre-configured-cycle (p-cycle) design in Mixed-Line-Rate (MLR) optical networks. Conventional p-cycle approaches require enumerating candidate cycles in advance and screening p-cycles using heuristic algorithms. Our method generates p-cycles directly in one-step using an Integer Linear Programming (ILP) model. Cost-effective transponders and distance-adaptive line rates are provisioned for every p-cycle to minimize joint cost of transponders and spare capacity. The design problem is solved together with spectral clustering based graph partitioning, which permits to compute the optimal solution in independent sub-graphs concurrently. The results show that our protection method is cost-efficient for p-cycle design with mixed line rates and scalable for large optical networks.

For an inter-datacenter (inter-DC) network that carries multiple cloud applications, tasks may arise in the DCs and need to transfer data to others with different latency requirements. Therefore, it is desired that a highly efficient online scheduling algorithm could be developed to consider the request admission, routing selection and bandwidth allocation for the data-transfers jointly. In this work, we investigate this problem, and propose an online scheduling algorithm, namely, GlobeAny, to maximize the time-average profit from provisioning the requests. The proposed algorithm leverages Lyapunov optimization techniques and can achieve arbitrarily approaching to the optimal value within O(1/V) gap. We also show that by adjusting the application weights, it can provide differentiated services to requests with different latency requirements.

In this paper, we propose to use IP-forwarding interchanging (i.e., exchanging packets between IPv4 and IPv6 according to the network status) enabled by OpenFlow to realize quality-of-service (QoS) aware flexible traffic engineering (F-TE) in a hybrid network where IPv4 and IPv6 coexist. Specifically, we have different IP domains interconnected by OpenFlow switches managed by a centralized controller, and design the network system to facilitate online, adaptive and per-flow-based IP-forwarding interchanging for link utilization optimization with the considerations on applications' QoS requirements. We implement the design in a semi-practical network testbed, and demonstrate the advantages of F-TE with experiments that include simultaneous video streaming and file transfer.

It is known that with a hybrid cloud, a multimedia cloud service provider (MCSP) can quickly extend its services to multiple geographical locations with quality-of-service (QoS) guarantees. Meanwhile, to maximize its profit, the MCSP needs an online management mechanism to operate the hybrid cloud efficiently. In this paper, we study how to maximize an MCSP's profit from provisioning multimedia services to geographically distributed users with a hybrid cloud. We first design a service provisioning model to manage the resources in the hybrid cloud. Here, in order to make the model practical and address the different situations in private and public clouds, we consider different time granularities for resource reservations. Then, we leverage the Lyapunov optimization technique to maximize the profit of MCSP and propose an online algorithm that can manage the hybrid cloud in the distributed manner. Specifically , the algorithm determines the access control and routing of each multimedia service request, and allocates the resources in the hybrid cloud accordingly. We also apply the ε-persistent technique to ensure that the worst-case latency of the provisioned requests is bounded. Finally, the proposed algorithm is evaluated with extensive simulations using both synthetical and real traces. Simulation results indicate that the algorithm can manage the hybrid cloud efficiently and maximize the profit of MCSP.

Software defined networking (SDN) makes networks programmable and application-aware by decoupling network control and management (NC&M) from data forwarding and leveraging centralized NC&M to facilitate user-customized routing and switching. Inspired by these, this paper investigates how to realize the OpenFlow-controlled (OF-controlled) network orchestration that can facilitate efficient scalable video coding (SVC) streaming to heterogeneous clients. Specifically, we consider real-time SVC streaming and address the situation in which video sources reside in geographically- distributed servers and clients can join and leave the streaming services dynamically. We formulate this as a multi-source multi-destination manycast problem and realize the networking system with an OF-controlled SDN architecture. We first design the OF controller to enable efficient network operations. Then, we focus on solving the multi-source multi-destination SVC video manycast problem and design several algorithms. Initially, an integer linear programming (ILP) model is formulated to obtain the optimal solutions for small-scale problems. Next, we try to make the manycast algorithm suitable for practical implementation, and design two time-efficient heuristics. Simulation results indicate that the heuristics can provide close-to-optimal solutions. Finally, we build an OF network testbed that consists of OF switches, SVC video servers and clients, and perform SVC streaming experiments to demonstrate our design. Experimental results verify that the proposed scheme can allocate bandwidth intelligently and ensure high-quality video streaming. To the best of our knowledge, this is the first work that accomplishes experimental demonstration of OF-controlled network orchestration for adaptive SVC video manycast.

In this paper, we consider to apply a joint defrag-mentation (DF) of spectrum and computing resources in the inter-datacenter (inter-DC) networks built over elastic optical infrastructure based on the optical orthogonal frequency-division multiplexing (O-OFDM) technology. We propose joint DF algorithms that consist of a request selection strategy to select active requests for reconfiguring and an anycast-based DF routing and spectrum assignment (DF-RSA) to reconfigure the selected requests. For the joint DF, two request selection strategies and three anycast-based DF-RSA schemes are designed, and their combinations are evaluated with simulations. The simulation results show that by considering the usages of spectrum and computing resources jointly, the proposed algorithms can improve the blocking performance of the inter-DC network significantly with controlled numbers of DF operations.

We optimize the disaster backup in multi-datacenter (multi-DC) cloud systems and design disaster-aware algorithms to realize rapid backup with the objective of minimizing the backup window for all the DCs in the network. A mixed integer linear programming (MILP) model is first formulated to optimize the backup processes of all production DCs jointly. We then develop three heuristics that use the one-step or two-step approaches for the selection of backup DCs and the calculation of backup routing paths. Simulation results show that the Two-Step algorithm can achieve the shortest backup window with the lowest operation complexity.

In this paper, after proposing a novel metric, i.e., global resource capacity (GRC), to quantify the embedding potential of each substrate node, we propose an efficient heuristic virtual network embedding (VNE) algorithm, called as GRC-VNE. The proposed algorithm aims to maximize the revenue and to minimize the cost of the infrastructure provider (InP). Based on GRC, the proposed algorithm applies a greedy load-balance manner to embed each virtual node sequentially, and then adopts the shortest path routing to embed each virtual link. Simulation results demonstrate that our proposed GRC-VNE algorithm achieves lower request blocking probability and higher revenue due to the more appropriate consideration of the resource distribution of the entire network, when compared to the two lastest VNE algorithms that also consider the resources of entire substrate network. Then, we introduce a classical reserved cloud revenue model, which consists of fixed revenue and variable one. Based on this revenue model, we design a novel admission control policy selectively accepting the VNR with high revenue-to-cost ratio to maximize the InP's profit based on an empirical threshold. Through extensive simulations, we observe that the optimal empirical threshold is proportional to the ratio of variable revenue to the fixed one.

Mobile health (m-Health) applications delivers healthcare services through mobile applications regardless of time and place. An m-Health application makes use of wireless communications to sustain its health services and often providing a patient-doctor interaction. Therefore, m-Health applications present several challenging issues and constraints, such as, mobile devices battery and storage capacity, broadcast constraints, interferences, disconnections, noises, limited bandwidths, network delays, and of most importance, privacy and confidentiality concerns. This paper proposes a novel and enhanced cryptography solution in a cooperative environment considering a novel and early-proposed cooperation strategy for m-Health Applications. This proposal aims to face the challenges related to privacy and security issues of all forwarded and retrieved data concerning user sensitive information. Furthermore, it presents a performance evaluation of this proposal considering a comparison with an earlier proposed encryption strategy for the same cooperative environment.

Virtual network embedding (VNE), working as a key step for network virtualization, has recently gained intensive attentions from the research community. In this paper, we propose a novel VNE algorithm that aims to maximize the infrastructure provider's revenue from serving virtual network (VN) requests, with the help of the global resource information. The proposed algorithm, named as revenue-driven VNE (RD-VNE), adopts a node-ranking approach that takes the global resource information into account in a recursive manner to assist the greedy node mapping, and leverages the shortest-path routing for link mapping. Our simulation results suggest that the proposed VNE algorithm outperforms two existing VNE algorithms that also take global resource information into consideration, in terms of request blocking probability, and brings higher time-average revenue to the infrastructure provider (InP).

We propose three algorithms for dynamic pre-configured-cycle (p-cycle) configuration in the spectrum-sliced elastic optical networks (EONs) based on the optical orthogonal frequency-division multiplexing (O-OFDM) technology, aiming to provide 100% restoration against single-link failures. The first one (PE-p-cycle) configures the working path and p-cycles of a request together according to the protection efficiencies of the p-cycles. In order to reduce request blocking probability, we then propose to use the spectrum planning technique to regulate the spectra of working and protection resources and design two algorithms based on the protected working capacity envelope (PWCE) cycles (PWCE-p-cycle-SP) and Hamiltonian cycles (Ham-p-cycle-SP). The three algorithms are then evaluated in two mesh network topologies, using dynamic Poisson traffic. The simulation results indicate that with the spectrum planning, PWCE-p-cycle-SP and Ham-p-cycle-SP reduce the blocking probability effectively, when comparing with the one without it, i.e., PE-p-cycle. To the best of our knowledge, this is the first attempt to address dynamic p-cycle configuration in EONs.

In this paper, we incorporate a layered approach to design integrated multicast-capable routing and spectrum assignment (MC-RSA) algorithms for achieving efficient all-optical multicasting in spectrum-sliced elastic optical networks (EONs), which are based on the optical orthogonal frequency-division multiplexing (O-OFDM) technology. For each multicast request, the proposed algorithms decompose the physical topology into several layered auxiliary graphs according to the network spectrum utilization. Then, based on the request's bandwidth requirement, we select a proper layer and calculate a multicast light-tree within it. With these procedures, the routing and spectrum assignment (RSA) for each multicast request is done in an integrated way. We evaluate the proposed algorithms in simulations of static network planning and dynamic network provisioning. The simulation results demonstrate that compared to the existing MC-RSA algorithms, our approaches achieve more efficient network planning in terms of spectrum utilization, and provide lower blocking probabilities in network provisioning.

We construct an experiment testbed using commercial routers and demonstrate QoS-aware multi-path SVC video streaming with it. The testbed consists of six integrated services routers (Cisco 2900 Series) that are configured using a mesh topology. To realize QoS-aware multi-path routing efficiently, we develop a centralized automatic NC&M system that monitors link status proactively, calculates the multi-path routing scheme for each streaming session, and communicates with the routers' control plane to adjust their routing policies. For each streaming session, when the NC&M finds a better multi-path routing scheme, it reconfigures the routers to invoke a path-switching. The experimental results indicate that the multi-path SVC video streaming scheme reduces the packet loss rate (PLR) from 3.33% to 0.62% for the base layer (BL) packets, and to 1.71% for the enhancement layer (EL) packets. Additional experiments on video playback quality, video playback peak signal-to-noise ratio (PSNR), and delay jitter also verify that the multi-path scheme outperforms the single-path one significantly and utilizes the network resources more efficiently.

We propose a novel dynamic transparent virtual network embedding (VNE) algorithm, which considers node mapping and link mapping jointly, for network virtualization over optical orthogonal frequency-division multiplexing (O-OFDM) based elastic optical infrastructures. For each virtual optical network (VON) request, the algorithm first transfers the substrate optical network into a layered-auxiliary-graph according to the spectrum usage of each fiber link, then applies a node mapping approach that considers the local information of all substrate nodes, and accomplishes the link mapping, in a single layer of the auxiliary graph. The simulation results verify that the proposed algorithm considers the uniqueness of O-OFDM networks and outperforms two reference algorithms that directly apply the VNE schemes developed for Layer 2/3 or WDM network virtualization, by providing lower VON blocking probability. The simulations with a realistic topology also demonstrate that the average lengths of embedded substrate paths are well-controlled within the typical transmission reaches of O-OFDM signals. To the best of our knowledge, this is the first proposal that includes both link mapping and node mapping to address dynamic transparent VNE over elastic optical infrastructures.

We develop a novel traffic scheduling algorithm based on a master-slave linecard (LC) configuration to improve the energy-efficiency of hybrid fiber-coaxial (HFC) networks. The algorithm forwards packets to the master or slave LC adaptively according to the traffic load, and toggles the LCs between working and sleeping modes for energy-saving. To optimize the algorithm's key parameters, we model the system using a two-dimensional Markov process and derive the analytical expressions of several performance metrics, including average packet delay, LC switching frequency, and energy efficiency improvement. We then verify the theoretical analysis with numerical simulations using the Monte Carlo method. Both the theoretical and simulation results indicate that the proposed algorithm can achieve significant energy efficiency improvement, while keeping the average packet delay and LC switching frequency low.

Bandwidth defragmentation, i.e., the operation to reconfigure existing connections for making the spectrum usage less fragmented and less misaligned, has recently been recognized as one of the most important features for elastic optical networks (EONs). In this paper, we propose a novel comprehensive bandwidth defragmentation algorithm that considers the problems of 1) When to defragment? 2) What for defragment? and 3) How to defragment? jointly. The proposed algorithm accomplishes defragmentation through proactive network reconfiguration that only reroutes a portion of existing connections. In each defragmentation operation, we first choose the connections to reroute using a selection strategy, then determine how to reroute them with the defragmentation based routing and spectrum assignment (DF-RSA), and finally perform rerouting with best-effort traffic migration to minimize traffic disruptions. Simulation results indicate that in order to make the bandwidth blocking probability (BBP) comparable with that from the Greenfield scenario (100% rerouting all the time), the proposed algorithm only needs to reroute ~30% existing connections. The simulations also demonstrate that the traffic disruption percentages are less than 1% for defragmentations with 30% rerouting and can be further reduced to within 0.25% by adding a move-to-vacancy (MTV) approach in the traffic migration.

The swift adoption of cloud services is accelerating the deployment of data centers. These data centers are consuming a large amount of energy, which is expected to grow dramatically under the existing technological trends. Therefore, research efforts are in great need to architect green data centers with better energy efficiency. The most prominent approach is the consolidation enabled by virtualization. However, little effort has been paid to the potential overhead in energy usage and the throughput reduction for virtualized servers. Clear understanding of energy usage on virtualized servers lays out a solid foundation for green data-center architecture. This paper investigates how virtualization affects the energy usage in servers under different task loads, aiming to understand a fundamental trade-off between the energy saving from consolidation and the detrimental effects from virtualization. We adopt an empirical approach to measure the server energy usage with different configurations, including a benchmark case and two alternative hypervisors. Based on the collected data, we report a few findings on the impact of virtualization on server energy usage and their implications to green data-center architecture. We envision that these technical insights would bring tremendous value propositions to green data-center architecture and operations.

In elastic optical networks (EONs), bandwidth fragmentation refers to the existence of non-aligned, isolated and small-sized blocks of contiguous subcarrier slots in the optical spectrum. As they are neither contiguous in the spectrum domain nor aligned along the routing paths, the network operator will have difficulty to use these slots for future connections. In this work, we analyze the effect of bandwidth fragmentation on the blocking probability in EONs. Our theoretical analysis indicates that two factors related to bandwidth fragmentation have effects on the blocking probability: 1) the extent that the available slot-blocks (i.e., blocks of contiguous slots) on different links are aligned on spectrum locations, and 2) the sizes of the available slot-blocks in links' spectra for future requests. When an EON's spectrum becomes more fragmented, the first factor actually reduces the blocking probability, while the second one increases the blocking probability. Their mixed effect determines the overall trend of how the blocking probability will change with bandwidth fragmentation. Our theoretical model can forecast this trend and reveal the relation among the blocking probability, bandwidth fragmentation, request bandwidth distribution, and spectrum utilization. We have also conducted numerical simulations to verify the theoretical analysis, and the simulation results exhibit similar trends as predicted by the theoretical model.

We layout a network infrastructure that leverages the storage and computing power of a cloud residing in the core for collecting network status and computing multi-path scalable video coding (SVC) streaming provisioning strategies. Therefore, in addition to its conventional tasks in the application layer, the cloud also gets involved in the network layer for the optimization of routing and forwarding. We call this scheme as cloud-assisted SVC streaming, and use it to further improve the performance of SVC streaming by using close cooperation between cloud and network. Compared to source-routing based provisioning, the cloud-assisted scheme can provide more cost-effective provisioning strategies by utilizing better knowledge of network environment together with more powerful computation power. We then propose several multi-path provisioning algorithms for cloud-assisted SVC streaming in heterogeneous networks. To the best of our knowledge, these are the first proposals to work on the problem of adaptive multi-path SVC streaming under the bandwidth, delay and differential delay constraints. Our design of the provisioning algorithms starts from an approach that is based on Max Flow and an Auxiliary Graph. Several extensions are then made based on this approach to address the situations such as provisioning from multiple sources and provisioning in dynamic network environments with rapid background traffic fluctuations. Simulations in both static and dynamic network environments show that the proposed algorithms can achieve effective performance improvements in terms of request blocking probability, bandwidth utilization, packet delay, packet loss rate, and video playback quality.

Hybrid Fiber Coaxial (HFC) networks support broadband Internet access with the existing cable TV systems. Recent advances on HFC networks have demonstrated effective improvements on the customers' access speeds with the channel-bonding technology. In this paper, we develop a novel energy-efficient traffic scheduling algorithm for the HFC networks that support channel bonding. The proposed algorithm is compliant with the newly-released DOCSIS 3.0 standard. We come up with a system model of the channel-bonding transmitters (TXs) on a Cable Modem (CM), and then define several operation modes for them. At the beginning of each scheduling cycle, the proposed algorithm adjusts the TXs' operation modes based on the traffic status. Both analytical analysis and numerical simulations are then developed to investigate the energy-saving and delay introduced by the algorithm. The results on energy-saving indicate that the energy-consumption of the TXs scales almost linearly with the input traffic load and effective energy-saving can be achieved. We also investigate the tradeoff between the energy-saving and the average delay to optimize the parameters for the energy-efficient scheduling, and to make sure that the traffic will not experience significant delay increase due to the energy-saving operations.

We develop an adaptive and efficient genetic algorithm (GA) to solve the dynamic routing, modulation and spectrum assignments (RMSA) for elastic O-OFDM networks. The algorithm offers an efficient way of serving the dynamic lightpath requests based on the current network status at each service provision time. The GA is designed for multi-objective optimization. For low traffic cases when there is no blocking, the GA minimizes the maximum number of slots required on any fiber in the network; otherwise, it minimizes the blocking probability. The performance of the proposed GA is evaluated in dynamic RMSA simulations with the 14-node NSFNET and the 28-node US Backbone topologies, and the results show that it converges within 25 generations. The simulation results also verify that the GA-RMSA outperforms several existing algorithms by providing more load-balanced network provisioning solutions with lower blocking probabilities. Specifically, when the traffic load is same, the GA can achieve more than one order-of-magnitude reduction on blocking probability. To the best of our knowledge, this is the first attempt to solve dynamic RMSA in elastic O-OFDM networks with a GA.

We design genetic algorithms (GA) to solve the mixed regenerator placement (MRP) problem of lightpaths with different lengths in optical transport networks, and investigate their performance with numerical simulations. By incorporating a theoretical model that can estimate BER changes hop-by-hop along lightpaths, the GA encodes the placements of 1R/2R/3R at intermediate regeneration sites as genes, and takes quality-of-transmission (QoT) and energy-efficiency as the fitness functions. With a relatively small population size (e.g. 30-50), the algorithms obtain multiple qualified MRP results that can satisfy both the QoT and energy requirements within 32 generations, for lightpaths with lengths up to 28 hops. Two crossover and two mutation operators are investigated within the GA. By adjusting the possibilities of the crossover and mutation intelligently, the adaptive scheme outperforms the uniform one by getting a larger percentage of fit individuals. We also propose two selection operators, and demonstrate an adjustable tradeoff between QoT and energy-efficiency.

We propose a traffic-aware and cooperative approach to achieve energy-saving in Hybrid Fiber-Coaxial (HFC) networks that support DOCSIS 3.0 standard. The approach incorporates a two-step operation that consists of two algorithms, one for the Cable Modem (CM) side and the other for the Cable Modem Termination System (CMTS) side. To avoid transition overheads, we design both algorithms with the consideration of energy-to-NoC (Number of Changes) tradeoff and try to minimize the number of Dynamic Bonding Change (DBC) operations while saving the energy. Simulations verify the effectiveness of the algorithms by using a realistic HFC network traffic model, and the results demonstrate significant saving on the ON-time (energy consumption) of the CMs' channels and the CMTS' ports.