Link Load Balancing Research Articles

Multi-objective optimization of asymmetric bit rate partitioning for multipath protection in elastic optical networks

In survivable elastic optical networks, multi-path protection combined with traffic squeezing has gained attention. Link-disjoint multipath routing (LD-MPR) and bandwidth squeezing protection (BSP) when applied to the routing, modulation level and spectrum assignment problem are efficient strategies to address the excessive bandwidth demanded by protected services, spectral fragmentation and link-load balancing. LD-MPR enables the division of service transmission bit rate into independent flows, whereas BSP tolerates traffic reduction under link failure. An open issue in the literature is how to efficiently divide demanded traffic among the link-disjoint routes under dynamic-traffic and BSP, while complying the service level agreement (SLA). We propose in this paper a multi-objective-optimization genetic algorithm that defines how service transmission bit rate should be partitioned among the candidate link-disjoint routes. A customized partitioning definition is made for each source–destination node pair in the network, aiming the simultaneous minimization of network blocking probability and average squeezing transmission bit rate experienced by the services during single-link failure. We also propose, a fixed-alternate routing using groups of disjoint paths (FARgdp). Complex dynamic-traffic network scenarios that simultaneously consider BSP, LD-MPR and FARgdp are addressed.

Telemetry-aided cooperative multi-agent online reinforcement learning for DAG task scheduling in computing power networks

As demand for computing power and low latency in intelligence applications grows, the efficient management and coordination of resources in computing power networks become crucial. This paper presents a telemetry-aided multi-agent cooperation framework for DAG task scheduling in computing power networks. Utilizing distributed agents with network telemetry, the framework accurately assesses local network state information, formulates scheduling policies, and assigns tasks to edge servers. An online learning algorithm for DAG task scheduling is also introduced to enhance the cooperation strategy in decision-making, enabling rapid task scheduling and resource allocation decisions. Simulation results demonstrate a minimum 13.5% reduction in total task execution time compared to sub-optimal methods, along with improved node and link load balancing.

Joint RMSSA scheme and link load balancing for static SD-SCNs from model building to algorithm design

The spatial channel network (SCN) is a new optical network architecture that can support traffic requests from 100 Gb/s to 10 Tb/s and beyond. Software-defined networking (SDN) has superb management and control capabilities, making it easier to obtain network topology and link load information with it than with traditional networks. Combining the advantages of both, we propose a multi-plane SDN-based spatial channel network (SD-SCN) architecture and focus on solving the routing, modulation format, spatial lane, and spectrum block assignment (RMSSA) problem in static SD-SCNs. In this paper, we build a multi-plane network model of an SD-SCN, design the functional modules of each plane, and give the operation principle of the model. Furthermore, we formulate the RMSSA problem in the static SD-SCN model and propose a heuristic spatial lane and spectrum block minimization and load balancing (MLB2) algorithm to solve the problem. Also, we design an improved ant colony optimization (IACO) algorithm (the heuristic function and iteration criterion are optimized) to implement the routing path assignment function of the MLB2 algorithm. The simulation results show that the MLB2 algorithm can carry traffic requests with minimum network resources, and when the total traffic rate is high, the link load can be balanced by invoking the IACO algorithm to increase the network throughput. The MLB2 algorithm performs better in small-scale networks than in large-scale networks.

Application study of ant colony algorithm for network data transmission path scheduling optimization

Abstract With the rapid development of the information age, the traditional data center network management can no longer meet the rapid expansion of network data traffic needs. Therefore, the research uses the biological ant colony foraging behavior to find the optimal path of network traffic scheduling, and introduces pheromone and heuristic functions to improve the convergence and stability of the algorithm. In order to find the light load path more accurately, the strategy redefines the heuristic function according to the number of large streams on the link and the real-time load. At the same time, in order to reduce the delay, the strategy defines the optimal path determination rule according to the path delay and real-time load. The experiments show that under the link load balancing strategy based on ant colony algorithm, the link utilization ratio is 4.6% higher than that of ECMP, while the traffic delay is reduced, and the delay deviation fluctuates within ±2 ms. The proposed network data transmission scheduling strategy can better solve the problems in traffic scheduling, and effectively improve network throughput and traffic transmission quality.

Load Balancing Control Algorithm of Internet of Things Link Based on Non-Parametric Regression Model

In order to solve the problems of poor channel balance control ability and unable to effectively reduce the output bit error rate in the traditional Internet of things link load balance control methods, a new Internet of things (IoT) link load balance control algorithm based on non-parametric regression model is proposed in this paper. The transmission model of IoT link channel is constructed, and the sparse random cluster analysis method is used to extract the load characteristics of IoT link. According to the load feature extraction results, through the estimated regression function of known data features, a non-parametric regression model is constructed, and the fuzzy cyclic iterative control is used to realize the load balancing control of the Internet of things link. The experimental results show that this method has strong channel balance control ability, low output bit error rate, the maximum average link utilisation can reach 1, and the maximum output bit error rate is only 102, which improves the stability of the Internet of things.

New approach to dynamic load balancing in software‐defined network‐based data centers

AbstractCritical issues such as connection congestion, long transmission delay, and packet loss become even worse during epidemic, disaster, and so on. In this study, a link load balancing method is proposed to address these issues on the data plane, a plane of the software‐defined network (SDN) architecture. These problems are NP‐complete, so a meta‐heuristic approach, discrete particle swarm optimization, is used with a novel hybrid cost function. The superiority of the proposed method over existing methods in the literature is that it provides link and switch load balancing simultaneously. The goal is to choose a path that minimizes the connection load between the source and destination in multipath SDNs. Furthermore, the proposed work is dynamic, so selected paths are regularly updated. Simulation results prove that with the proposed method, streams reach the target with minimum time, no loss, low power consumption, and low memory usage.

Traffic Engineering in a Shared Inter-DC WAN via Deep Reinforcement Learning

To reduce the investment on network infrastructure, many online service providers have begun to adopt the shared inter-DataCenter Wide Area Network (inter-DC WAN) that connects different datacenters and Internet Service Providers (ISPs). The shared inter-DC WAN accommodates two kinds of traffic, i.e. delay-sensitive ISP-facing traffic and high-throughput inter-DC traffic. Traffic Engineering (TE) in the shared inter-DC WAN should determine the routing paths for all traffic to achieve link load balancing, while select lower-latency egress routers for ISP-facing traffic to guarantee the Quality of Service (QoS). Therefore, this paper mainly focuses on jointly optimizing routing paths selection and egress router selection to strike a balance between QoS and link load balancing. Specifically, we first formulate the TE problem in the shared inter-DC WAN as a mixed integer nonlinear programming problem. Then, a TED method is proposed to jointly optimize the egress router selection and routing path selection by learning an intelligent agent with Deep Reinforcement Learning (DRL). The learnt agent can self-adaptively and rapidly select the optimal egress routers by considering the utilization-latency balance when traffic demand changes. Finally, we conduct extensive evaluations on Alibaba WAN with real traffic traces to demonstrate the effectiveness and superiority of the proposed method.

Priority-awareness VNF migration method based on deep reinforcement learning

By decoupling the software function on hardware devices, Network Function Virtualization (NFV) provides a new service architecture named Service Function Chain (SFC), which combines multiple Virtual Network Functions (VNFs) in a specific order. In order to improve the reliability and Quality of Service (QoS) of network services, VNF migration provides an effective solution for this requirement. However, traditional migration methods lack an appropriate VNF selection mechanism and consider a single network state. Moreover, how to determine the accurate migration location dynamically with machine learning is challenging. This paper proposed a novel PAVM algorithm for reliable and optimal VNF migration, which distinguishes two kinds of VNF migration schemes for network congestion and node failure states respectively, and a node and VNF priority-awareness mechanism is designed, which can select appropriate VNF on suitable node to migrate. Moreover, PAVM utilizes deep reinforcement learning algorithm to choose target node location of VNF migration, which jointly utilizes delay, load balancing of network as feedback factors to optimize the QoS. The experimental results indicate that compared with other three benchmark algorithms, PAVM can effectively reduce the transmission delay and improve node and link load balancing after the VNF migration.

Traffic Engineering in Hybrid Software Defined Network via Reinforcement Learning

The emergence of Software Defined Network (SDN) provides a centralized and flexible approach to route network flows. Due to the technical and economic challenges in upgrading to a fully SDN-enabled network, hybrid SDN, with a partial deployment of SDN switches in a traditional network, has been a prevailing network architecture. Meanwhile, Traffic Engineering (TE) in the hydbrid SDN has attracted wide attentions from academia and industry. Previous studies on TE in the hybrid SDN are either traffic-oblivious or time-consuming, which causes routing schemes failed in responding to the dynamically-changing traffic rapidly and intelligently. Therefore, in this paper, we propose a Reinforcement Learning (RL) based method, which learns a traffic-splitting agent to address the dynamically-changing traffic and achieve the link load balancing in the hybrid SDN. Specifically, to rapidly and intelligently determine a routing scheme to the new traffic demands, a traffic-splitting agent is designed and learnt offline by exploiting the RL algorithm to establish the direct relationship between traffic demands and traffic-splitting policies. Once the traffic-splitting agent is learnt, the effective traffic-splitting policies, which are used to determine the traffic-splitting ratios on SDN switches, can be generated rapidly. Additionally, to meet the interactive requirements for learning a traffic-splitting agent, a reasonable simulation environment is proposed to be constructed to avoid routing loops when traffic-splitting policies are taken. Extensive evaluations on different topologies and real traffic demands demonstrate that the proposed method achieves the comparable network performance and performs superiorities in rapidly generating the satisfying routing schemes.

A fairness‐aware distributed dual‐hop heterogeneous half and full‐duplex link scheduling for 6G network

AbstractCurrent network technologies evolve toward the sixth generation of mobile communications (6G) systems. To achieve the performance requirements of high data rate, low latency, and massive connection, full‐duplex (FD) wireless communication is one possible solution to provide high throughput and channel utilization. However, it is difficult to do so for legacy communication nodes. A distributed fairness‐aware full‐duplex dual‐hop link scheduling algorithm, named weighted full duplex (W‐FD), is proposed to achieve the efficient node selection and dynamic relay probing. The scheduling weight that considered traffic load, mutual interference, and response interval is employed to ensure link load balance. A node with a higher scheduling weight in the neighborhood is selected as the link establishment object to build a full‐duplex link. Better than conventional half‐duplex(HD) link scheduling, W‐FD reduces link scheduling interval and ensures the access opportunities for legacy nodes. Simulation result shows that throughput and delay performance are improved. Compared with the existed full‐duplex link scheduling, W‐FD could better reflect the fairness of scheduling in multi‐nodes and asymmetric‐packets networks, and ensure the throughput and delay performance.

RETRACTED ARTICLE: Proactive Load Balancing Strategy Towards Intelligence-Enabled Software-Defined Network

Software-defined network provides a greater solution to many of the complex network management functionalities in a data center network (DCN). In a software-defined data center network, it is essential to manage heavy traffic, to avoid data loss, and to avoid server unavailability during heavy flows. One of the major tasks in a network is the load balancing in the available links. Due to the dynamic data traffic in the network, it is necessary to perform a deep learning of the long-term and the short-term data. Hence, this paper proposes an intelligent load balancing scenario that includes link load balancing, server load balancing, and traffic classification. An artificial neural network is needed because the data are uncertain all the time. Thus, our method provides improved throughput, minimized the flow completion time, and minimizes data loss.

Energy-aware routing considering load balancing for SDN: a minimum graph-based Ant Colony Optimization

Software-Defined Network (SDN) technology is a network management approach that facilitates a high level of programmability and centralized manageability. By leveraging the control and data plane separation, an energy-aware routing model could be easily implemented in the networks. In the present paper, we propose a two-phase SDN-based routing mechanism that aims at minimizing energy consumption while providing a certain level of QoS for the users’ flows and realizing the link load balancing. To reduce the network energy consumption, a minimum graph-based Ant Colony Optimization (ACO) approach is used in the first phase. It prunes and optimizes the network tree by turning unnecessary switches off and providing an energy-minimized sub-graph that is responsible for the network existing flows. In the second phase, an innovative weighted routing approach is developed that guarantees the QoS requirements of the incoming flows and routes them so that to balance the loads on the links. We validated our proposed approach by conducting extensive simulations on different traffic patterns and scenarios with different thresholds. The results indicate that the proposed routing method considerably minimizes the network energy consumption, especially for congested traffics with mice-type flows. It can provide effective link load balancing while satisfying the users’ QoS requirements.

Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks

A data center undertakes increasing background services of various applications, and the data flows transmitted between the nodes in data center networks (DCNs) are consequently increased. At the same time, the traffic of each link in a DCN changes dynamically over time. Flow scheduling algorithms can improve the distribution of data flows among the network links so as to improve the balance of link loads in a DCN. However, most current load balancing works achieve flow scheduling decisions to the current links on the basis of past link flow conditions. This situation impedes the existing link scheduling methods from implementing optimal decisions for scheduling data flows among the network links in a DCN. This paper proposes a predictive link load balance routing algorithm for a DCN based on residual networks (ResNet), i.e., the link load balance route (LLBR) algorithm. The LLBR algorithm predicts the occupancy of the network links in the next duty cycle, according to the ResNet architecture, and then the optimal traffic route is selected according to the predictive network environment. The LLBR algorithm, round-robin scheduling (RRS), and weighted round-robin scheduling (WRRS) are used in the same experimental environment. Experimental results show that compared with the WRRS and RRS, the LLBR algorithm can reduce the transmission time by approximately 50%, reduce the packet loss rate from 0.05% to 0.02%, and improve the bandwidth utilization by 30%.

A Multi-Domain VNE Algorithm Based on Load Balancing in the IoT Networks

The coordinated development of big data, Internet of Things, cloud computing and other technologies has led to an exponential growth in Internet business. However, the traditional Internet architecture gradually shows a rigid phenomenon due to the binding of the network structure and the hardware. In a high-traffic environment, it has been insufficient to meet people’s increasing service quality requirements. Network virtualization is considered to be an effective method to solve the rigidity of the Internet. Among them, virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a large number of research has focused on the evolutionary algorithm’s masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and its low flexibility makes it unable to adapt to increasingly complex network environments. In addition, link-mapping strategies that do not consider load balancing can easily cause link blocking in high-traffic environments. In the IoT environment involving medical, disaster relief, life support and other equipment, network performance and stability are particularly important. Therefore, how to provide a more flexible virtual network mapping service in a heterogeneous network environment with large traffic is an urgent problem. Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed. This strategy uses a dynamically calculated cross-probability and pheromone-based mutation gene selection strategy to improve the flexibility of the algorithm. In addition, a weight update mechanism based on load balancing is introduced to reduce the probability of mapping failure while balancing the load. Simulation results show that the proposed method performs well in a number of performance metrics including mapping average quotation, link load balancing, mapping cost-benefit ratio, acceptance rate and running time.

Joint Traffic-Aware Consolidated Middleboxes Selection and Routing in Distributed SDNs

Software middlebox-based services can be flexibly managed by software defined networking (SDN) and network function virtualization (NFV). Meanwhile, traffic routing can be simplified and the number of routing rules in the SDN-enabled switches can be reduced through the consolidated middlebox model. However, different network functions in middleboxes may alter the volume of processed traffic, so high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in the data plane, but also pose a challenge to controller load balancing. Therefore, it’s necessary to achieve better quality-of-service (QoS) performance in both control and data planes. In this article, we first formulate this problem as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and prove its NP-hardness. Then, we design a two-phase algorithm to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, compared with the existing algorithms through extensive simulations, it demonstrates that our method has near-optimal controller load balancing and link load balancing performance and can improve response time by 9.7% compared with static scheme.

Research on Load Balancing Control of Internet of Things (IoT) Link Based on Sparse Random Clustering

Aiming at the problems of poor channel balancing control ability and high output bit error rate in traditional IoT link load balancing control methods, an improved ant colony algorithm based load balancing control method for IoT link is proposed. This paper constructs the channel transmission model of IoT, uses sparse random clustering analysis method to detect the load characteristics of Internet of things links, and extracts the statistical characteristics of IoT link. The output balance control and interference suppression of IoT link are carried out by matching filter detection method, and the load balance of IoT link is fuzzy control combined with fuzzy PID control method. The simulation results show that the proposed method has good self-adaptability, strong channel equalization control ability and lower output bit error rate.

DRL-R: Deep reinforcement learning approach for intelligent routing in software-defined data-center networks

Data-center networks (DCN) possess multiple new features: coexistence of elephant flow/mice flow/coflow, and coexistence of multiple network resources (bandwidth, cache and computing). The cache should be a factor of effecting routing decision because it can eliminate redundant traffic in DCN. However, the conventional routing schemes cannot learn from their previous experiences regarding network abnormalities (such as, congestion), and their metric are still the single link state (such as, hop, distance, and cost) which does not include the effect of cache. Thus, they cannot enough efficiently allocate these resources to well meet the performance requirements for various flow types. Therefore, this paper proposes deep reinforcement learning-based routing (DRL-R). Firstly, we propose a method that recombines multiple network resources with different metrics, where we recombine cache and bandwidth by quantifying their contribution score in reducing the delay. Secondly, we propose a routing scheme with resource-recombined state. By optimally allocating network resources for traffic, a DRL agent deployed on a software-defined networking (SDN) controller continually interacts with the network to adaptively perform reasonable routing according to the network state. We employ deep Q-network (DQN) and deep deterministic policy gradient (DDPG) to build the DRL-R. Finally, we demonstrate the effectiveness of DRL-R through extensive simulations. Benefitting from continuous learning with a global view, DRL-R has lower flow completion time, higher throughput and better load balance as well as better robustness, compared to OSPF. In addition, because it efficiently utilizes the network resources, DRL-R can also outperform another DRL-based routing scheme (namely TIDE). Compared to OSPF and TIDE, respectively, DRL-R can improve throughput by up to 40% and 18.5%; DRL-R can reduce flow completion time by up to 47% and 39%; DRL-R can improve the link load balance by up to 18.8% and 9.3%. Additionally, we observed that DDPG has better performance than DQN.

Energy‐aware dynamic‐link load balancing method for a software‐defined network using a multi‐objective artificial bee colony algorithm and genetic operators

Information and communication technology (ICT) is one of the sectors that have the highest energy consumption worldwide. It implies that the use of energy in the ICT must be controlled. A software-defined network (SDN) is a new technology in computer networking. It separates the control and data planes to make networks more programmable and flexible. To obtain maximum scalability and robustness, load balancing is essential. The SDN controller has full knowledge of the network. It can perform load balancing efficiently. Link congestion causes some problems such as long transmission delay and increased queueing time. To overcome this obstacle, the link load balancing strategy is useful. The link load-balancing problem has the nature of NP-complete; therefore, it can be solved using a meta-heuristic approach. In this study, a novel energy-aware dynamic routing method is proposed to solve the link load-balancing problem while reducing power consumption using the multi-objective artificial bee colony algorithm and genetic operators. The simulation results have shown that the proposed scheme has improved packet loss rate, round trip time and jitter metrics compared with the basic ant colony, genetic-ant colony optimisation, and round-robin methods. Moreover, it has reduced energy consumption.

Computing Cost Optimization for Multi-BS in MEC by Offloading

As today’s Internet of Things (IoT) applications are becoming more complicated and intelligent, IoT devices alone can no longer well support the ever-increasing demand for powerful computation and high energy efficiency. Mobile Edge Computing (MEC) and 5G technology emerge as promising solutions, which enable IoT tasks to be offloaded to edge servers for effective processing. Though desirable, there however exists a mismatch between the massive IoT task workloads and limited wireless bandwidth, making it challenging to achieve an optimal offloading strategy at the mobile edge, e.g., the base station (BS) server. In this paper, we aim to migrate the most suitable offloading tasks to fully obtain the benefits of the MEC task offloading. We first formulate the task offloading model as an optimization problem, and theoretically prove the NP-hardness in achieving the optimal solution. Thus, a Genetic algorithm, named M-COGA, is proposed to solve the task offloading selection in both single and multiple BS scenarios. The algorithm focuses on offloading as many tasks as possible with the maximum cost offloading. The proposed cost function takes into account both the computation overhead and energy consumption. Besides, for the multi-BS coverage scenario, we also consider the approach flexibility as well as link load balance. And an enhanced dynamic task offloading scenario is further discussed. We verify the efficiency of our algorithm under the condition of both uniform and non-uniform distribution of covered nodes. Numerical experiments demonstrate that our dynamic allocating scheme can effectively work in MEC offloading. Besides, it largely outperforms the single BS scenarios and reduces the cost of edge devices.

VNE strategy based on chaos hybrid flower pollination algorithm considering multi-criteria decision making

With the development of science and technology and the need for multi-criteria decision making (MCDM), the optimization problem to be solved becomes extremely complex. The theoretically accurate and optimal solutions are often difficult to obtain. Therefore, meta-heuristic algorithms based on multi-point search have received extensive attention. The flower pollination algorithm (FPA) is a new swarm intelligence meta-heuristic algorithm, which can effectively control the balance between global search and local search through a handover probability, and gradually attracts the attention of researchers. However, the algorithm still has problems that are common to optimization algorithms. For example, the global search operation guided by the optimal solution is easy to lead the algorithm into local optimum, and the vector-guided search process is not suitable for solving some problems in discrete space. Moreover, the algorithm does not consider dynamic multi-criteria decision problems well. Aiming at these problems, the design strategy of hybrid flower pollination algorithm for virtual network embedding problem is discussed. Combining the advantages of the genetic algorithm and FPA, the algorithm is optimized for the characteristics of discrete optimization problems. The cross-operation is used to replace the cross-pollination operation to complete the global search and replace the mutation operation with self-pollination operation to enhance the ability of local search. Moreover, a life cycle mechanism is introduced as a complement to the traditional fitness-based selection strategy to avoid premature convergence. A chaos optimization strategy is introduced to replace the random sequence-guided crossover process to strengthen the global search capability and reduce the probability of producing invalid individuals. In addition, a two-layer BP neural network is introduced to replace the traditional objective function to strengthen the dynamic MCDM ability. Simulation results show that the proposed method has good performance in link load balancing, revenue???cost ratio, VN requests acceptance ratio, mapping average quotation, average time delay, average packet loss rate, and the average running time of the algorithm.