Neighbour Traffic-Aware Load Balancing Method in Ad Hoc Networks

Firewalls are installed at network perimeters to secure organization's network as alternatives lo general gateways. Because of potential performance problems on the gateways, load balancing technique has been applied. However, compared to general gateways, firewalls require more intelligent load balancing method lo process massive network traffic because of their relatively complex operations. In this paper, we analyze the inherent problems of existing simple load balancing methods for firewalls and propose the interval weighted load balancing (IWLB) to enhance the processing of massive network traffics. The IWLB deals with network traffics in consideration of the characteristics of application protocols to achieve more effective load balancing. We observed that the IWLB outperforms other simple load balancing methods during our simulation. Therefore, we expect that the IWLB is suitable to balancing loads for multiple firewalls at a network perimeter.

Load balancing is one of the key target of LTE Self-Optimization Network (SON). In this paper, we propose a load balancing method for LTE downlink network, namely Load Vector Minimization based Load Balancing (LVMLB) method. Load Vector (LV) is a vector whose elements are the load values of cells and sorted in descending order. The order of LVs is defined by the lexicographical order. The smaller the LV is, the higher the balance degree of cells load will be. As the LV has a lower bound with total load fixed, the balance degree of cells load would reach a local optimal. On this basis, we design the LVMLB algorithm, trying to get the optimal solutions to load balancing problems, the proof of being optimal will also be given in this paper. Simulation scenarios are set in a square part of Macro-Pico mixed HetNets. Simulation results show that LVMLB outperforms the Cell Region Expansion (or Bias) scheme, increasing the capacities of Macro and Pico tiers at the same time, and improving balance degree of cells load, only sacrificing a little QoS performance.

Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) have been recently proposed as a promising technique for indoor wireless communications. Such a network combines the high-speed transmission of LiFi and the ubiquitous coverage of WiFi. Meanwhile, in addition to user mobility, intermittent light-path blockages make the handover issue intricate in HLWNets. Also, load balancing (LB) is necessary because the coverage areas of LiFi and WiFi networks are completely overlapped. This further increases the difficulty in handover as the decision for a vertical or a horizontal handover in a mobile environment with ultra-small cells is non-trival. Aimed at providing an optimal solution to the access point selection (APS) with given channel state information (CSI), the conventional LB method might cause frequent and unnecessary handovers. In this paper, a joint optimisation problem is formulated to simultaneously consider LB and handover in HLWNets. Results show that compared with the conventional LB method, the proposed method can improve the system throughput by up to 60%.

Video transmission over wireless packet networks is gaining importance due to the concept of universal personal communication. Further, it is considered an important step towards wireless multimedia. The challenge however is to achieve good video quality over mobile channels, where typically the channel conditions vary due to signal fading. Hence this paper investigates adaptive rate controlled video transmission for robust video communication under packet wireless environment. A combination of mobile and an ATM backbone network is assumed in this work. An error resilient design for the video coder, as proposed in Rajugopal et al. (1996) is employed here. This video coder comprises wavelet transform (WT), multi-resolution motion estimation (MRME) and a robust design for zero tree quantization. Two configurations, one employing MRME and the other using 1D-WT for temporal analysis, are considered for the video coder. Adaptive dynamic rate control is required to adapt the video communication to the channel conditions. It provides more channel protection when the channel is severe and improves the source rate and hence the performance when the conditions are favorable. An algorithm for dynamic rate control under varying channel conditions is proposed in this paper. It is evaluated under narrowband and broadband channel conditions. From the results, it is concluded that the dynamic rate control is very effective in optimizing the quality under varying mobile channel conditions. It was observed that the dynamic rate control provides at least an acceptable video quality under severe channel conditions and a good video quality when the channel conditions are favorable.

In ad hoc networks of nodes equipped with multiple antennas, the tradeoff between spatial multiplexing and diversity gains in each link impacts the overall network capacity. An optimal algorithm is developed for adaptive rate and power control for a communication link over multiple channels in a Poi

Load balancing problem is one of the key issues in multi-server architecture Distributed Virtual Environment (DVE) system. Existing load balancing methods are mainly for the one-dimensional cutting based DVE system or square cell based DVE system, and it is difficult to apply them to the hexagonal cell based DVE system. In this paper, we propose a new load balancing method for hexagonal cell virtual environment system, which can effectively reduce the number of overloaded server at run time and reduce the client migrations between servers by selecting appropriate migration cells. The experimental results show that the proposed method can effectively keep the load balance among servers in the virtual environment system based on hexagon partitioning and reduce the probability of client migrations, thus improving the usability of the system.

Radio frequency spectrum in wireless communication is a common resource shared by all collocated devices. Most spectrum management schemes rely on a centralized base station-receiver station relaying architecture. In contrast, infrastructure-less systems including wireless ad hoc networks require a scalable, distributed methodology. Moreover, the access has to be coordinated among the nodes in the network in the aspects which include power and rate adaptation. The challenge is to find an optimal tradeoff between such parameters not only for a particular link but also for an entire multi-hop network. Several existing schemes address power, throughput and Signal-to-Interference ratio (SIR) control on a link level. In contrast, the proposed work performs optimization for the entire network. The game theory methodology is employed to solve the distribution of the radio resources while the adaptive power and rate control estimates the required but unknown radio interface parameters online. Moreover, the adaptive power and rate control scheme optimizes the local channel access. The resulting spectrum channel management scheme becomes a hybrid of adaptive controls and game theory approaches that guarantees a fair and efficient channel sharing and maximizes the overall performance. The results are shown analytically and through simulations.

Adaptive data rate control in low power wide area networks for long range IoT services

Maximizing network lifetime of unreliable and resource constrained wireless networks such as mobile ad hoc networks involves optimal allocation of power and shared channels in the network. Existing optimal power and channel allocation mechanisms mainly focus on throughput maximization and energy efficiency but fail to obtain a trade-off between delay and energy consumption in the network. This paper proposes a game theoretic energy efficient approach that mimics a fair non-cooperative game among nodes competing for shared channels and power level. Unlike existing power and rate control mechanisms, the proposed energy conserving adaptive power and rate control (ECAPRC) mechanism takes into consideration the constraints of outage probability and total average delay to deal with stochastic changes in the channel that improves the network performance in terms of throughput, delay and total energy consumption. Proposed super-modular game has a unique convergence point called “Nash Equilibrium” and its existence and uniqueness is proved in the paper. To enhance the convergence rate of proposed ECAPRC approach, an adaptive grey wolf optimizer is employed to deal with delay and energy constraints. Simulation results and their analysis show that proposed ECAPRC approach outperforms existing approaches such as dynamic rate and power allocation algorithm, rate-effective network utility maximization and energy conserving power and rate control in terms of above mentioned network parameters and total utility of the system.

The Internet of Things (IoT) is a network of different objects that refers to an environment in which intelligent devices around us can connect to the Internet and exchange information together. A large number of generated events from IoT objects causes overhead on the network. Therefore, to optimize the usage of IoT network, it is essential to provide solutions for network problems including scalability, routing, reliability, security, energy conservation, network lifetime, congestion, heterogeneity, and quality of service (QoS). In this regard, load balancing as an efficient method takes a significant role in distributing loads among different routes. Imbalance traffic load across the network causes high latency in some routes and loss of data packets and decreases packet delivery ratio. Although load balancing has a critical importance in the IoT, there is still a lack of an organized and comprehensive review about analyzing and examining its remarkable methods. Therefore, this paper by adopting a systematic manner aims to address this gap. In this research, the load balancing methods are categorized into two main categories including centralized and distributed and their merits and demerits are specified. Moreover, vital parameters, the challenges, and open issues in this scope are also discussed. Thus, future authors will be able to develop more effective load balancing mechanisms.

This paper proposes a joint adaptive rate selection and power control algorithm for broadband TDMA/TDM wireless networks. The proposed algorithm is a modified, enhanced, and more robust version of the selective power control with active link protection (SPC-ALP) algorithm proposed for adaptive transmission rate and power control in CDMA networks [R.Jantti and S. Kim, 2000]. Unlike SPC-ALP, the proposed algorithm uses adaptive coding and modulation (instead of variable spreading gain) for transmission rate control. In addition, the proposed algorithm is different in two aspects; the first one is the inclusion of the cochannel interferer assistance mode, while the second one is the use of the signal quality as a criterion for user removal. Results show that the proposed algorithm outperforms SPC-ALP in terms of the net throughput and signal quality measures such as the outage probability and frame error rate. Channel reallocation is also studied and found to be very effective in enhancing the system performance particularly at low to medium loading.

Distributed multimedia applications (DMA), such as video-on-demand (VoD) require dynamic quality-of-service (QoS) guarantee from end-user machines for their continuous multimedia streams. The QoS requirements and improvement in job execution time can be achieved with the aid of proper load balancing among the machines. An effective way to achieve load balance is to link up these end-user machines with multiple potential heterogeneous idle or under-loaded clustered-servers and allow these servers with idle retrieval bandwidth to help out end-user machines that are low power or temporarily overloaded (bottleneck). The clustered VoD servers approach provides scalability and availability compared to a single video server. In this paper we design and implement a new clustered-server-client architecture for multimedia VoD applications along with a new REMS+VoD algorithm. Rigorous simulation for the algorithm has been performed by varying both the number of video-requests (sessions) and the number of potential servers. The simulation results show that our approach provides: (1) effective load balancing; (2) scalability; and (3) availability in a VoD system

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

The uncertain nature of mapping user tasks to Virtual Machines (VMs) causes system failure or execution delay in Cloud Computing. To maximize cloud resource throughput and decrease user response time, load balancing is needed. Possible load balancing is needed to overcome user task execution delay and system failure. Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration. Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs. Thus, the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism (HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly. This load balancing approach improved performance by considering average network latency, dependability, and throughput. This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation, assign jobs to VMs with more solution diversity, and prevent the solution from reaching a local optimality. Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan, 16.21% increase in mean throughput, and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA, HD-WOA, and Binary Bird Swap.

This paper proposes a wireless channel assessment metric A posteriori Bit Error Probability (ABEP) that indicates the error likelihood of each bit during demodulation. Estimated upon constellations, ABEP provides the finest granularity (per bit) in revealing dynamic channel conditions. With comparative validation on an implemented GNU SDR testbed, ABEP significantly outperforms a pioneer flagship work SoftPHY in fidelity and channel predictability. An ABEP based channel assessment algorithm is devised to recognize channel conditions such as channel fading and collision, and it demonstrates a recognition accuracy up to 99% across various channel conditions. Based on the ABEP channel assessment, a cross-layer protocol-ABEP-enabled adaptive rate control (AARC)-is designed for high wireless network performance. AARC is implemented and evaluated on ns-3 simulator with a newly developed physical layer. Extensive experiments show that AARC significantly outperforms prior rate control solutions in rate selection accuracy and throughput.