Inter-datacenter Elastic Optical Networks Research Articles

Joint allocation of computing and connectivity resources in survivable inter-datacenter elastic optical networks

Joint allocation of computing and connectivity resources in survivable inter-datacenter elastic optical networks

Delay-aware and resource-efficient service function chain mapping in inter-datacenter elastic optical networks

Network function virtualization (NFV) is emerging as a promising paradigm for network architectures. By migrating network functions from dedicated hardware appliances to software instances running in a virtualized environment, NFV promises to offer a more flexible way to deploy and manage service function chains (SFCs). When deploying these SFCs to users, the network operators require not only the user’s demands (e.g., end-to-end delay) to be satisfied, but require the cost of SFC mapping to be minimized (e.g., resource consumption). To fulfill these two goals, in this paper, we have investigated how to realize the delay-aware and resource-efficient SFC mapping in inter-datacenter elastic optical networks. We first formulate an integer linear programming (ILP) model to solve the problem exactly. The main optimization goal in the ILP model is to jointly minimize resource consumption and end-to-end delay to achieve optimal virtual network function placement. Then, a delay-aware and load-balancing mapping algorithm (DALB-MA) is proposed to obtain a near-optimal solution in a reasonable amount of time. Finally, we evaluate the proposed ILP model and heuristic algorithms via extensive simulations. The results indicate that the proposed ILP model and the DALB-MA outperform the benchmarks in terms of block rate, average cost, number of CPUs used, maximum frequency slot index, and delay margin gain.

Deep Reinforcement Learning for Provisioning Virtualized Network Function in Inter-Datacenter Elastic Optical Networks

In today’s datacenters (DCs), IT resources virtualization is leveraged to realize Network Function Virtualization (NFV) over general-purpose servers. Meanwhile, most of the service providers (SPs) are planning to use Virtual Network Functions (VNFs) to provide agile and flexible network services. In service provisioning, the VNF selection and mapping greatly affect IT resource utilization in DCs and spectrum resource utilization in optical networks. This paper proposes a Deep Reinforce Learning (DRL)-based algorithm for VNF provisioning. By selecting appropriate VNFs for the service requests, the algorithm intelligently guarantees efficient reusing of deployed VNFs while consuming fewer spectrum resources in inter-DC elastic optical networks (EONs). To facilitate the decision-making of the DRL agent, we first decompose the complex VNF-based service chaining (VNF-SC) into several VNF components (VNFCs), which can be solved one-by-one in turn. Then, a feature matrix-based encoding scheme is designed to represent the set of the VNFCs, the available DCs for the VNFCs, and the VNFC being operated, i.e., the input of neural networks. In addition, considering the complexity and difficulty of the VNF-SC provisioning problem, Double Deep Q Network (DDQN) is introduced in the proposed algorithm. Finally, compared with the benchmark heuristics, the extensive simulation results in different network topologies show that the proposed algorithm can reduce the IT and spectrum resource consumption by at least 9.6% and 1.6%, which proves the effectiveness of the proposed DRL-based VNF provisioning algorithm.

CASM: A Cost-Aware Switch Migration Strategy for Elastic Optical Inter-Datacenter Networks

In inter-datacenter elastic optical networks, multi-controller deployment is adopted to improve the stability and scalability of the control plane. As the network scale increases, the traditional multi-controller deployment scheme ignores the dynamic characteristics of traffic, resulting in unbalanced load among multiple controllers. In response to this problem, the existing switch migration mechanism is proposed to achieve balanced distribution of control loads. However, most of the existing research work does not consider the additional cost of switch migration, and the load balancing performance of the controller is not significantly improved after switch migration. In this paper, we propose a cost-aware switch migration (CASM) strategy for controller load balancing. The proposed CASM strategy first measures the controller load through multiple performance indicators that affect the controller load, and then judges whether the controller is overloaded or underloaded based on the controller’s response time to the request message, thereby improving the load balancing performance of the controller. Additionally, when selecting the switch to be migrated, the CASM selects the optimal switch for migration based on minimizing the migration cost, thereby reducing the cost of switch migration. The performance evaluation shows that CASM significantly improves load balancing performance of controllers and reduces the migration cost compared to existing solutions.

Balanced resource allocation for VNF service chain provisioning in inter-datacenter elastic optical networks

Network Function Virtualization (NFV) is a novel technology that enables flexible and cost-effective network services by replacing the hardware-based middleboxes with software instances named virtual network functions (VNFs). In NFV, each requested Service Function Chain (SFC) is an ordered sequence of VNFs that must be placed optimally on the geographically distributed data centers (DCs). For the popular Elastic Optical Networks (EONs) with flexible bandwidth management, a major challenge is optimal placement of the VNF-based Service Chain (VNF-SC) on data centers and steering traffic through deployed VNFs in the substrate network. In this paper, we study both the problems of VNF placement and Routing/Spectrum Allocation (RSA) for inter-DC EONs with the aim of minimizing the total network cost while considering the balanced resource utilization to reduce the blocking probability. We first formulate the problem as an Integer Linear Programming (ILP) to solve the problem exactly, and then propose a heuristic algorithm based on the resource-balancing and fragmentation-aware solution to reduce the blocking probability. The simulation results verify that the proposed algorithm outperforms an existing algorithm.

Profit Maximization of Online Service Function Chain Orchestration in an Inter-Datacenter Elastic Optical Network

To meet the increasing traffic demands characterized by large bandwidth and high burstiness, more traffic has been moving to inter-datacenter elastic optical networks (inter-DC EONs) for processing. The integration of two emerging paradigms, network function virtualization (NFV) and software-defined networking (SDN), enables Internet service providers (ISPs) to deploy service function chains (SFCs) from users flexibly while reducing operational and capital expenditures. This article focuses on the problem of online SFC provisioning in inter-DC-EONs with the aim of maximizing ISP profits, where the challenge in jointly allocating IT and spectrum resources when deploying SFCs is balanced with the deployment costs of processing as many user requests as possible. We design two-phase time-efficient orchestration algorithms for online SFC requests and the strategy of SFC splitting is adopted to improve the utilization of spectrum resources on fiber links. Simulation results show that, compared with the existing algorithm, our proposed algorithms significantly shorten the deployment time, improve total profit of ISP by up to 40% and reduce the blocking probability by up to 35%.

Routing, modulation level and spectrum assignment for deadline-driven manycast requests in survivable inter-datacenter elastic optical networks

Abstract In inter-datacenter elastic optical networks (IDC-EONs), many demands, such as data backup, focus on being served before a specific moment rather than immediately. In addition, as a point to multi-point transmission framework, manycast is particularly applicable for these services because it could select destinations from a set of candidate nodes flexibly. As one of key measures to improve the network survivability and spectrum utilization, survivable routing, modulation level and spectrum assignment (SRMLSA) has attracted much attention. The traditional SRMLSA scheme could not optimize the spectrum assignment in time domain. In this paper, SRMLSA problem has been addressed for deadline-driven manycast requests against single-link failure in IDC-EONs. A manycast survivable routing, modulation level and spectrum assignment based on deadline-driven queuing with capacity weighting (MA-SRMLSA-DQCW) scheme is proposed, which could achieve joint optimization in both frequency and time domains while providing 100% protection. Numerical results demonstrate that MA-SRMLSA-DQCW scheme offers a substantial reduction of blocking probability and occupies less spectrum resources compared to reference algorithms.

Joint balancing of IT and spectrum resources for selecting virtualized network function in inter-datacenter elastic optical networks.

With network functions decoupled from specific hardware, network function virtualization (NFV) is a promising technology to accelerate service provisioning in datacenter (DC) networks. In inter-datacenter elastic optical networks, each virtualized network function (VNF) is usually deployed in multiple DCs for the sake of survivability. In service provisioning, different VNF selections greatly affect IT resources in DCs as well as spectrum resources in optical networks. This paper investigates how to select appropriate VNFs for service requests to achieve joint load balancing of IT and spectrum resources. Two joint balancing factors are proposed to quantify the impact of different VNF selections on network load. Furthermore, a Joint-Optimization Selection (JOS) algorithm is designed to select VNFs in a joint load balancing manner. The performance of the proposed algorithm is evaluated in terms of resource utilization, blocking probability and average path length. Simulation results indicate that the proposed algorithm outperforms the benchmark algorithm.

Energy-Efficient Service Provisioning in Inter-Data Center Elastic Optical Networks

We investigate the problem of energy-efficient transponder configuration constrained to quality of service and physical requirements in orthogonal frequency division multiplexing-based software-defined inter-data center elastic optical networks. We consider two types of services which are called delay-sensitive and delay-tolerant. A deterministic and a stochastic mixed-integer nonlinear program with a target of minimizing the total transponder power consumption are formulated which their solutions provide a long-term and a short-term configuration for delay-sensitive service and delay-tolerant service transponders, respectively. We use geometric and stochastic Lyapunov optimization techniques to convert the complex mixed-integer nonlinear formulations to their equivalent convex forms that can be solved more than 1 order of magnitude faster. We demonstrate that total power consumption of delay-tolerant service transponders is reduced by more than 29% when average traffic load is 30% and transponder parameters are stochastically reconfigured rather than fixed configuration. Furthermore, we show that there is an adjustable tradeoff between transmission delay and transponder power consumption in the proposed scheme. We also incorporate transmit optical power as a tunable parameter in the transponder configuration process and use simulation results to demonstrate its impact on power consumption reduction.

On Incentive-Driven VNF Service Chaining in Inter-Datacenter Elastic Optical Networks: A Hierarchical Game-Theoretic Mechanism

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.

关于数据中心间弹性光网络中虚拟网络功能配置的问题建模及优化算法

关于数据中心间弹性光网络中虚拟网络功能配置的问题建模及优化算法

Deep-Learning-Assisted Network Orchestration for On-Demand and Cost-Effective vNF Service Chaining in Inter-DC Elastic Optical Networks

This work addresses the relatively long setup latency and complicated network control and management caused by on-demand virtual network function service chain (vNF-SC) provisioning in inter-datacenter elastic optical networks. We first design a provisioning framework with resource pre-deployment to resolve the aforementioned challenge. Specifically, the framework is designed as a discrete-time system, in which the operations are performed periodically in fixed time slots (TS). Each TS includes a pre-deployment phase followed by a provisioning phase. In the pre-deployment phase, a deep-learning (DL) model is designed to predict future vNF-SC requests, then lightpath establishment and vNF deployment are performed accordingly to pre-deploy resources for the predicted requests. Then, the system proceeds to the provisioning phase, which collects dynamic vNF-SC requests from clients and serves them in real-time by steering their traffic through the required vNFs in sequence. In order to forecast the high-dimensional data of future vNF-SC requests accurately, we design our DL model based on the long/short-term memory-based neural network and develop an effective training scheme for it. Then, the provisioning framework and DL model are optimized from several perspectives. We evaluate our proposed framework with simulations that leverage real traffic traces. The results indicate that our DL model achieves higher request prediction accuracy and lower blocking probability than two benchmarks that also predict vNF-SC requests and follow the principle of the proposed provisioning framework.

Global Optimization of All-Optical Hybrid-Casting in Inter-Datacenter Elastic Optical Networks

In inter-datacenter elastic optical networks (IDC-EONs), routing, modulation-level, and spectrum assignment (RMLSA) is one of the key measures to increase network capacity and flexibility of spectrum resource management. The coexistence of unicast, anycast, multicast, and manycast traffic in IDC-EONs forms a more complex network communication scheme called hybrid cast. However, the majority of the previous RMLSA approaches focused on the network communication scheme with single traffic type and optimized network performance on the specific network metric, which leads to less practicability and a limit to one-sided network performance improvement. To achieve better network performance, we investigate the hybrid-cast RMLSA (HC-RMLSA) problem with the objective of improving IDC-EON overall performance. Specifically, a criterion is defined to estimate the overall network performance by considering both the overall network service and operation effectiveness and the network cost. An ILP model is formulated to solve the HC-RMLSA problem under static network scenario. Then, we propose an independent optimization genetic algorithm with priority-based rationality adaption (IOGA-PRA) by coordinating a set of hybrid-cast requests simultaneously to solve the HC-RMLSA problem under both static and dynamic network scenarios. Numerical results show that the performance of IOGA-PRA is very close to the optimal solutions obtained by the ILP model on the network cost-effectiveness in static network scenario. The IOGA-PRA also achieves outstanding improvement of the overall network performance compared with benchmark algorithms in the dynamic network scenario.

A spectrum-efficient algorithm based on traffic splitting and merging transmission for anycast in inter-datacenter elastic optical networks

Anycast is attracting much attention due to the need of scalable and cost-effective data delivery in inter-datacenter elastic optical networks. However, spectrum fragmentation degrades network’s performance and decreases probability of successful anycast delivery significantly. When the idle spectrum block in elastic optical network is not enough to transmit the anycast request, spectrum splitting with multi-path transmitting the anycast is an effective approach to improve the spectrum fragmentation utilization. For improving spectrum utilization and reducing time delay between multiple paths, we propose a spectrum-efficient algorithm based on traffic splitting and merging (Anycast_SA_TSM) transmission to avoid spectrum fragmentation and delay between multiple paths. In order to minimize the time delay between multiple paths, we design a modified scheme to select the multiple paths with minimal time delay to transmit the anycast. During the spectrum allocation phase, a new spectrum block allocation scheme, the exact fit or fragmentation minimal, is put forward. Moreover, when an appropriate size of spectrum block is found for the split anycast request, merging split sub-requests to a single path is activated for minimizing the additional guard bands and improving the spectrum efficiency. Comparing with other two anycast algorithms, simulation results show that the proposed algorithm can get minimal time delay between split multiple paths, the minimal bandwidth blocking probability and the highest spectrum utilization.

Joint Spectrum and IT Resource Allocation for Efficient VNF Service Chaining in Inter-Datacenter Elastic Optical Networks

We study how to allocate spectrum and IT resources jointly for realizing efficient virtual network function (VNF) service chaining in inter-datacenter elastic optical networks. We first formulate an integer linear programming model to solve the problem exactly, and then a longest common subsequence-based heuristic is proposed. The simulation results indicate that the proposed algorithms can reuse the deployed VNFs efficiently and arrange the spectrum utilization in a much more load-balanced manner.