Traffic Load Balancing Research Articles

Congestion Control and Traffic Scheduling for Collaborative Crowdsourcing in SDN Enabled Mobile Wireless Networks

Currently, a number of crowdsourcing‐based mobile applications have been implemented in mobile networks and Internet of Things (IoT), targeted at real‐time services and recommendation. The frequent information exchanges and data transmissions in collaborative crowdsourcing are heavily injected into the current communication networks, which poses great challenges for Mobile Wireless Networks (MWN). This paper focuses on the traffic scheduling and load balancing problem in software‐defined MWN and designs a hybrid routing forwarding scheme as well as a congestion control algorithm to achieve the feasible solution. The traffic scheduling algorithm first sorts the tasks in an ascending order depending on the amount of tasks and then solves it using a greedy scheme. In the proposed congestion control scheme, the traffic assignment is first transformed into a multiknapsack problem, and then the Artificial Fish Swarm Algorithm (AFSA) is utilized to solve this problem. Numerical results on practical network topology reveal that, compared with the traditional schemes, the proposed congestion control and traffic scheduling schemes can achieve load balancing, reduce the probability of network congestion, and improve the network throughput.

Design of WSN traffic forecasting system with delayed self-sensing

ABSTRACTIn order to achieve the load balancing between network nodes, improve the utilization of system resources and network throughput, and reduce network delay. Therefore, based on the Optimized Link State Routing (OLSR) protocol, this paper proposes an improved protocol of OLSR based on traffic load balancing and QoS. OLSR-TLB protocol takes the traffic load balancing and QoS into account. For real-time traffic, focus on the traffic load of the access point. If the access point traffic load is too high, it cannot be connected with the routing. However, for non-real-time traffic, QoS but not traffic load is considered, making routing decisions based on hop counts. In addition, the traffic service is divided into real-time traffic and non-real-time traffic. The former considers the real-time, making routing decisions based on traffic load, while the latter considers QoS, making routing decisions based on hop counts. Simulation results show that the proposed OLSR-TLB protocol can effectively improve the throughput and reduce the delay performance. Compared with the traditional OLSR protocol, the throughput of OLSR-TLB protocol is improved to 3.5Mbps, and the delay is reduced to 50 ms.

A Straight Skeleton Based Connectivity Restoration Strategy in the Presence of Obstacles for WSNs.

Connectivity has significance in both of data collection and aggregation for Wireless Sensor Networks (WSNs). Once the connectivity is lost, relay nodes are deployed to build a Steiner Minimal Tree (SMT) such that the inter-component connection is reestablished. In recent years, there has been a growing interest in connectivity restoration problems. In previous works, the deployment area of a WSN is assumed to be flat without obstacles. However, such an assumption is not realistic. In addition, most of the existing strategies chose the representative of each component, which serves as the starting point of relay node deployment during the connectivity restoration, either in a random way or in the shortest-distance based manner. In fact, both ways of representative selection could potentially increase the length of the SMT such that more relay nodes are required. In this paper, a novel connectivity restoration strategy is proposed—Obstacle–Avoid connectivity restoration strategy based on Straight Skeletons (OASS), which employs both the polygon based representative selection with the presence of obstacles and the straight skeleton based SMT establishment. The OASS is proved to be a 3- approximation algorithm with the complexity of , and the approximation ratio can reduce to while it satisfies a certain condition. The theoretical analysis and simulations show that the performance of the OASS is better than other strategies in terms of the relay count and the quality of the established topology (i.e., distances between components, delivery latency and balanced traffic load) as well.

Network Utility Aware Traffic Load Balancing in Backhaul-Constrained Cache-Enabled Small Cell Networks with Hybrid Power Supplies

Explosive data traffic growth leads to a continuous surge in capacity demands across mobile networks. In order to provision high network capacity, small cell base stations (SCBSs) are widely deployed. Owing to the close proximity to mobile users, SCBSs can effectively enhance the network capacity and offloading traffic load from macro BSs (MBSs). However, the cost-effective backhaul may not be readily available for SCBSs, thus leading to backhaul constraints in small cell networks (SCNs). Enabling cache in BSs may mitigate the backhaul constraints in SCNs. Moreover, the dense deployment of SCBSs may incur excessive energy consumption. To alleviate brown power consumption, renewable energy will be explored to power BSs. In such a network, it is challenging to dynamically balance traffic load among BSs to optimize the network utilities. In this paper, we investigate the traffic load balancing in backhaul-constrained cache-enabled small cell networks powered by hybrid energy sources. We have proposed a network utility aware (NUA) traffic load balancing scheme that optimizes user association to strike a tradeoff between the green power utilization and the traffic delivery latency. On balancing the traffic load, the proposed NUA traffic load balancing scheme considers the green power utilization, the traffic delivery latency in both BSs and their backhaul, and the cache hit ratio. The NUA traffic load balancing scheme allows dynamically adjusting the tradeoff between the green power utilization and the traffic delivery latency. We have proved the convergence and the optimality of the proposed NUA traffic load balancing scheme. Through extensive simulations, we have compared performance of the NUA traffic load balancing scheme with other schemes and showed its advantages in backhaul-constrained cache-enabled small cell networks with hybrid power supplies.

Proactive Radio Resource Optimization With Margin Prediction: A Data Mining Approach

Driven by the exponential surge on high data rate services, network operators are facing the challenges of how to enhance the capacity and optimize the coverage in a cost-efficient approach. However, traditional network optimization technologies passively adjust the network configurations based on network's congestion ratio, drop-off rate, coverage holes, etc., leading to suboptimum user experiences. Therefore, the objective of this paper is to optimize the network configurations by obtaining the accurate network status, user demand, and application request distribution based on the real-time data. The data mining technique is introduced to predict the resource margin based on historical measurement statistics. To explore the dynamic distribution of user demand and application request, a weighted k-nearest neighbors model is proposed to predict periodic characteristics of network traffics, denoting different temporal and spatial patterns of radio resource margins. In contrast to the traditional passive network optimization approaches, the radio resources can be reconfigured actively to meet the dynamic patterns of traffic loads by using the proposed optimization algorithm. Results prove that the proposed data mining model can capture the dynamics of traffic loads to optimize the traffic load balance and increase the efficiency of radio resource utilization using the network statistic data.

QoS-Aware Dynamic RRH Allocation in a Self-Optimized Cloud Radio Access Network With RRH Proximity Constraint

An inefficient utilization of network resources in a time-varying traffic environment often leads to load imbalances, high call-blocking events and degraded quality of service (QoS). This paper optimizes the QoS of a cloud radio access network (C-RAN) by investigating load balancing solutions. The dynamic re-mapping ability of C-RAN is exploited to configure the remote radio heads (RRHs) to proper base band unit sectors in a time-varying traffic environment. RRH-sector configuration redistributes the network capacity over a given geographical area. A self-optimized cloud radio access network (SOCRAN) is considered to enhance the network QoS by traffic load balancing with minimum possible handovers in the network. QoS is formulated as an optimization problem by defining it as a weighted combination of new key performance indicators for the number of blocked users and handovers in the network subject to RRH sectorization constraint. A genetic algorithm (GA) and discrete particle swarm optimization (DPSO) are proposed as evolutionary algorithms to solve the optimization problem. Computational results based on three benchmark problems demonstrate that GA and DPSO deliver optimum performance for small networks, whereas close-optimum is delivered for large networks. The results of both GA and DPSO are compared to exhaustive search and $K$ -mean clustering algorithms. The percentage of blocked users in a medium sized network scenario is reduced from 10.523% to 0.421% and 0.409% by GA and DPSO, respectively. Also in a vast network scenario, the blocked users are reduced from 5.394% to 0.611% and 0.56% by GA and DPSO, respectively. The DPSO outperforms GA regarding execution, convergence, complexity, and achieving higher levels of QoS with fewer iterations to minimize both handovers and blocked users. Furthermore, a tradeoff between two critical parameters for the SOCRAN algorithm is presented, to achieve performance benefits based on the type of hardware utilized for C-RAN.

An analytical model of sequential hypothesis estimation for femtocell network optimisation

ABSTRACTFemto Base Stations (FBSs) are small cells that provide radio coverage for low-power-consuming smart mobile devices in indoors. In this article, we contemplate on the networks optimisation issue of a number of FBSs which anticipate joint network coverage in commercial area. In such a situation, FBSs reduce coverage overlapping, network holes and make sure a balanced traffic load is distributed among them. We apply sequential hypothesis estimation methods to quantify the probabilistic accuracy of a provided femtocell (small cell) configuration at runtime. If any disobeying is observed in providing the required standard of quality service, a self-optimisation process is enabled to build on the present configuration.

Traffic aware load balancing in AOMDV for mobile Ad-hoc networks

The wireless networks have a very bright future in networks and communications because of which they have taken a high interest of the researchers. As the users increased the purpose to use MANETs, they also became more diverse and wide due to which better performance is needed in MANETs. QoS is needed for applications for an efficient communication and load balancing is a feature in the routing protocol that can help in a better use of the resources and can help to increase the performance of the network. We propose a new approach for load balancing in AOMDV routing protocol for MANETs that can enhance the network performance by selecting paths using the temporal load on the intermediate nodes and by distributing the load amongst the free nodes while transmission of data, which is proved by simulations in NS-2.

Blocking cost‐driven multicast scheduling in fat‐tree data center networks

SummaryMulticast traffic scheduling in data center networks can not only improve network efficiency but also save network resources. However, the existing multicast scheduling algorithms cannot appropriately schedule flows to achieve traffic load balance so that the network may occur heavy blocking. This prevents the full utilization of high degree of link parallelism and causes unpredictable reduction of network performance. To address the problem, in this paper, we propose a blocking cost‐driven multicast scheduling algorithm by using optimization theory in fat‐tree data center networks. In particular, a model of multicast traffic subnetwork is established on the basis of the blocking probability of available paths at next time slot, which can reflect the blocking characteristics of multicast network and predict network state at next time slot. With the multicast blocking model, we derive the minimum blocking probability of multicast subnetworks, denoted as blocking cost. In addition, the algorithm can select the multicast subnetwork with minimum blocking cost to transfer multicast flows. Time complexity analysis and simulation results demonstrate the effectiveness and efficiency of our proposed multicast scheduling algorithm for different network traffic intensities.

A Hybrid Fuzzy Multi-hop Unequal Clustering Algorithm for Dense Wireless Sensor Networks

Clustering is carried out to explore and solve power dissipation problem in wireless sensor network (WSN). Hierarchical network architecture, based on clustering, can reduce energy consumption, balance traffic load, improve scalability, and prolong network lifetime. However, clustering faces two main challenges: hotspot problem and searching for effective techniques to perform clustering. This paper introduces a fuzzy unequal clustering technique for heterogeneous dense WSNs to determine both final cluster heads and their radii. Proposed fuzzy system blends three effective parameters together which are: the distance to the base station, the density of the cluster, and the deviation of the node’s residual energy from the average network energy. Our objectives are achieving gain for network lifetime, energy distribution, and energy consumption. To evaluate the proposed algorithm, WSN clustering based routing algorithms are analyzed, simulated, and compared with obtained results. These protocols are LEACH, SEP, HEED, EEUC, and MOFCA.

Load balancing in 5G Networks

The expected huge increase of mobile devices and user data demand by 2020 will stress the current mobile network in an unprecedented way. The future mobile networks must meet several strong requirements regarding the data rate, latency, quality of service and experience, mobility, spectrum and energy efficiency. Therefore, efforts for more efficient mobile network solutions have been recently initiated. To this direction, load balancing has attracted much attention as a promising solution for higher resource utilization, improved system performance and decreased operational cost. It is an effective method for balancing the traffic and alleviating the congestion among heterogeneous networks in the upcoming 5G networks. In this paper, we focus on an offloading scenario for load balancing among LTE and Wi-Fi networks. Additionally, network graphs methodology and its abstracted parameters are investigated in order to better manage wireless resource allocation among multiple connections. The COHERENT architectural framework, which consists of two main control components, makes use of such abstracted network graphs for controlling or managing various tasks such as traffic steering, load balancing, spectrum sharing and RAN sharing. As a result, the COHERENT project eventually develops a unified programmable control framework used to efficiently coordinate the underlying heterogeneous mobile networks as a whole.

Flexible, Efficient, and Scalable Software-Defined Over-the-Top Multicast for ISP Environments With DynSdm

A number of today's over-the-top (OTT) services could greatly benefit from a flexible, efficient, and scalable network-layer multicast support on the Internet. IP multicast showed not to meet these requirements, hindering its adoption in more than individual network islands. As a consequence, content delivery networks (CDNs) emerged as alternative and implemented one-to-many multicasting at the application layer. Yet, due to practical reasons and conflicting stakeholder interests, CDN deployments usually end at the border of Internet service provider networks, making one-to-many deliveries at an Internet-scale challenging up to date. In a previous work, the authors presented software-defined multicast (Sdm) to mitigate this problem by unlocking network-layer multicast support for OTT services inside ISP networks. In this paper, DYNSDM is proposed to complement Sdm in key aspects, allowing for a more holistic support of relevant scenarios aspects, and enabling a fine-granular traffic engineering of multicast traffic inside ISP networks. For this, DYNSDM presents a detailed design of the ISP-internal traffic and service management process and introduces a set of novel SDN-based network-layer mechanisms for traffic load balancing as well as handling of group and network dynamics. DYNSDM was implemented as prototype and extensively evaluated, showing its flexibility, high traffic efficiency, good scalability, and superior ability to balance traffic load inside ISP networks.

Near-Optimal Routing Protection for In-Band Software-Defined Heterogeneous Networks

Facing the spectrum supply-demand gap, heterogeneous network (HetNet) is a promising approach to achieve drastic gains in network coverage and capacity compared with macro-only networks, thus making it especially attractive to network operators. On the other hand, software-defined networking brings a number of advantages along with many challenges. One particular concern is on the resilience for in-band fashioned control plane. Existing approaches mainly rely on a local rerouting policy when performing the routing protection for the target sessions in software-defined networks. However, such a policy would potentially bring congestions in the neighbouring links of the failed one. To this end, we study a weighted cost-minimization problem, where the traffic load balancing and control-channel setup cost are jointly considered. Because this problem is NP-hard, we first propose a near-optimal Markov approximation-based approach for in-band-fashioned software-defined HetNets. We then extend our solution to an online case that handles a single-link failure. We also conduct theoretical analysis on the performance fluctuation due to the single-link failure. We finally carry out experiments by experimental simulation. The extensive numerical results show that the proposed algorithm has fast convergence and high efficiency in resource utilization.

Braided Routing Technique to Balance Traffic Load in Wireless Sensor Networks

Many-to-one wireless sensor networks suffer from an extreme variation of traffic load between nodes. Sensor nodes near the sink consume much more energy than distant ones, resulting in the energy hole problem (global variation of load). In addition, even nodes located at the same distance from the sink experience very different traffic load with each other (local variation). This uneven distribution of traffic load, both globally and locally, results in a severe shortening of the time until first node runs out of battery. This work focuses on balancing the load of equally-distant nodes from the sink by sharing each one's load among its next-hop neighbors. Eventually, packets are travelling from node to sink by following interlaced paths. The proposed routing mechanism, called braided routing, is a simple one and can be applied over any cost-based routing, incurring a negligible overhead. Simulation results show that the local variance of load is reduced nearly 20-60% on average while the time until first death can be prolonged more than twice in many cases and the lifetime about 15%.

A Unified Solution for Gateway and In-Network Traffic Load Balancing in Multihop Data Collection Scenarios

Data collection is a fundamental task of wireless sensor networks to support a variety of applications, such as remote monitoring, and emergency response, where collected information is relayed to an infrastructure network via packet gateways for processing and decision making. In large-scale monitoring scenarios, data packets need to be relayed over multihop paths to the gateways, and sensors are often randomly deployed, causing local node density differences. As a result, imbalance in data traffic load on the gateways is likely to occur. Furthermore, due to dynamic network conditions and differences in sensor data generation rates, congestion on some data paths is also often experienced. Numerous studies have focused on the problem of in-network traffic load balancing, whereas a few works have aimed at equalizing the loads on gateways. However, there is a potential tradeoff between these two problems. In this paper, the dual objective of gateway and in-network load balancing is addressed, and the reactive and adaptive load-balancing (RALB) algorithm is presented. RALB is proposed as a generic solution for multihop networks and mesh topologies, particularly in large-scale remote monitoring scenarios, to balance traffic loads.

Load Balancing With 3-D Beamforming in Macro-Assisted Small Cell Architecture

Small cell is an attractive and promising technology for improving capacity in traffic hotspots using cell densification. We consider the traffic load-balancing problem for a macro-assisted small cell architecture exploiting the flexible 3-D beamforming facilitated by the adoption of the active antenna system at base stations. Specifically, 3-D user equipment (UE)-specific beamforming is utilized for traffic load balancing due to its unique feature that the received signal is maximized, while the interference is limited with narrower beamwidth. We propose a novel traffic load-balancing algorithm based on cell association and cell sectorization using 3-D UE-specific beamforming, and the performance of the proposed algorithm is evaluated via system-level simulations. Results show that cell edge user throughput and cell average throughput both improve significantly with the proposed load-balancing algorithm compared with the conventional sectorization with fixed antenna down-tilt scheme. In addition, in denser small cell scenarios, our proposed scheme can further increase the load-balancing gain with reduced cell planning efforts compared with the conventional scheme.

Channel-Access-Aware User Association With Interference Coordination in Two-Tier Downlink Cellular Networks

The diverse transmit power levels of base stations (BSs) in a multitier cellular network, on the one hand, lead to an uneven distribution of traffic loads among different BSs when received signal power (RSP)-based user association is used. This causes underutilization of resources at low-power BSs. On the other hand, strong interference from high-power BSs affects downlink transmissions to the users associated with low-power BSs. In this context, this paper proposes a channel-access-aware (CAA) user association scheme that can simultaneously enhance the spectral efficiency (SE) of downlink transmission and achieve traffic load balancing among different BSs. The CAA scheme is a network-assisted user association scheme that requires traffic load information from different BSs, in addition to channel quality indicators. We develop a tractable mathematical model to derive the SE of the network and the SE of downlink transmission to a user who associates with a BS using the proposed CAA scheme considering almost blank subframe (ABS)-based interference coordination at a macro BS. The framework can model and analyze the individual traffic load distributions of different BSs in a two-tier network. The derived expressions provide approximate solutions of reasonable accuracy, compared with the results obtained from Monte Carlo simulations. Numerical results comparatively analyze the gains of the CAA scheme over conventional-RSP-based association and biased-RSP-based association. Based on this, important insights are extracted, which are related to the selection of the proportion of an ABS in various traffic load scenarios.

Security Threats in Software Defined Mobile Clouds (SDMC)

Future Internet comprises of emerging ICT mega-trends (e.g., mobile, social, cloud, and big data) commands new challenges like ubiquitous accessibility, high bandwidth, and dynamic management to meet the data tsunami requirements. In the recent years, the rapid growth of smartphone business is highly evidenced due to its versatile usage irrespective of location, personality or context. Despite of increased smartphone usage, exploiting its full potential becomes very difficult owing to its typical issues such as resource scarcity, mobility and more prominently the security. Software Defined Networking (SDN), an emerging wireless network paradigm can make use of rich mobile cloud functionalities such as traffic management, load balancing, routing, and firewall configuration over physical abstraction of control planes from data planes. Hence SDN leads to a clear roadmap to Software Security control in Mobile Clouds (SDMC). Further it can be extended to a level of Security prevention. To address in this direction, this paper surveys the relevant backgrounds of the existing state-of-art works to come up with all possible SDMC threats and its countermeasures.

An Enhanced Perceptive Queuing Technique (CBCRTQ) for Traffic Management in VoIP Over MANET

Traffic management in the VOIP transmission is the most important criteria and issue (to be maintained the packet). The traffic maintains packets and data as a source of outcome for VoIP (QoS) over Manet background. Queuing is one of the most important mechanisms in traffic executive. Mobile Adhoc network (MANETs) present a good platform for the fast deployment of VoIP services in many application scenarios. Ad hoc networks consist a set of identical node that moves freely, independently and communicate with other node via wireless links, the main challenge in extreme sensitivity to delay and packet loss in Voip over Manet. In this research a new algorithm proposed which enhance the performance of traffic load in VOIP carryover MANET environment. Through a simulation and mathematical expression we analyze and evaluate QoS parameters, in the existing method there is no load balanced queuing mechanism and lack in cluster selection prototype. Hence researcher proposed a new Class Based Cluster Round Trip Queue (CBCRTQ) algorithm for VoIP (QoS) over MANET. The main objective of this algorithm selection of cluster heads form a virtual backbone and may be used to route packets for nodes in their cluster. There is a need of traffic load balance at Cluster Round Trip methodology in VoIP, Simulation experiment makes clear that the future algorithm does provide longer cluster head selection and duration with traffic load-balancing.

Proportional Fair Traffic Splitting and Aggregation in Heterogeneous Wireless Networks

Traffic load balancing and resource allocation is set to play a crucial role in leveraging the dense and increasingly heterogeneous deployment of multiradio wireless networks. Traffic aggregation across different access points (APs)/radio access technologies (RATs) has become an important feature of recently introduced cellular standards on LTE dual connectivity and LTE-WLAN aggregation (LWA). Low complexity traffic splitting solutions for scenarios where the APs are not necessarily collocated are of great interest for operators. In this letter, we consider a scenario, where traffic for each user may be split across macrocell and an LTE or WiFi small cells connected by nonideal backhaul links, and develop a closed form solution for optimal aggregation accounting for the backhaul delay. The optimal solution lends itself to a “water-filling” based interpretation, where the fraction of user’s traffic sent over macrocell is proportional to ratio of user’s peak capacity on that macrocell and its throughput on the small cell. Using comprehensive system level simulations, the developed optimal solution is shown to provide substantial edge and median throughput gain over algorithms representative of current 3GPP-WLAN interworking solutions. The achievable performance benefits hold promise for operators expecting to introduce aggregation solutions with their existing WLAN deployments.